Journal of Forensic Vocationology Vol. 7 (1) Monograph
[Note from the Editors: Permission to Reprint this Monograph of selected materials from JOFV Volume 7(1), Fall 2002, ©
2002 by Vocationology, Inc., granted by the Editors of the JOFV for inclusion on the IARP Fall Conference 2003 Disk].
2003 by Vocationology, Inc.
This JOFV Monograph includes the following Annotated Articles with minor updates:
Page - Title - Author(s)
3 Product Peer Review of Course Disk 1: The Updating of the 5th Edition DOT - Grimley
4 Product Peer Review on MVQS2003 - Streater
5 Frequently asked questions regarding the MVQS 2001-2003 Programs - McCroskey
8 The McCroskey Vocational Quotient System (MVQS) Theory of Transferable Skills: Revised, Extended and
Updated for the 21st Century - McCroskey, Grimley, Williams, Hahn, Lowe, Wattenbarger, Stein & Dennis
31 The MVQS Vocational Interest and Personality Reinforcer (VIPR) Job-based Personality Type Indicator and
MTSP1 Jobs-Based Vocational Interest Personality Types Crosswalk to Jung People-Based Personality Types McCroskey, Hahn, Streater, Sinsabaugh, Mayer, Van de Bittner, Lowe & Dennis
91 Emotional Trauma: Its Impact on Vocational Analysis - McCroskey & Dennis
104 MTSP VQ1-OES Aggregate & VQ1-McDOT Specific Wage Estimation - McCroskey, Hahn & Dennis
132 MTSP VQ1-OES Aggregate & VQ1-McDOT Specific Wage Estimation Follow-Up Study – McCroskey, Hahn,
Dennis & Wattenbarger
Note from the Editors: The Journal of Forensic Vocationology is a private, copyrighted, scientific,
research-oriented Journal, published periodically (typically 1 time per year) by Vocationology, Inc. Articles
considered for inclusion are subject to Peer-Review and Editorial review prior to inclusion in the Journal.
Co-Editors: Billy J. McCroskey, Ph.D. & William E. Wattenbarger, Ph.D. (763-569-0680)
The primary purpose of the Journal of Forensic Vocationology is to disseminate scientific research and
related information deemed relevant to the field of Vocationology (See definition in The Journal of
Vocationology, Vol. (1) 1, 1995) and related disciplines including: Psychology, Student Personnel Services,
Career Development, Rehabilitation Economics, Rehabilitation Counseling, Job Analysis, Worker TraitFactor Theory, Vocational Evaluation and Work Adjustment. From time to time, research & informational
articles may be invited or solicited from recognized experts in Vocationology and Related fields.
The Editorial Advisory Board and Peer Review Committee
Kenneth Dennis, Ph.D., LP, LMFT, CRV, Associate Editor -MN - ken.dennis@juno.com
Robert Male, Ph.D., CEA - OR - bobmale@home.com
Steve Hahn, M.S., CVE, CRC, QRC, Associate Editor - MN - sjhahn1@aol.com
David Stein, Ph.D., ABVE - NV - DBSPHD@aol.com
Scott Streater, D.V.S, ABVE - NC – sesdvs@aginet.com
Judith Harper-Haley, M.R.C., ABVE - TX - london_suede@email.msn.com
Craig Feldbaum, Ph.D., LP, ABVE - LA - cfeldb@gulfsouth.verio.net
Claude Peacock, M.S., ABVE - AL - cfpeacock@mindspring.com
David Toppino, M.A., CVE, CEA, ABVE - CA - veala@aol.com
John Williams, D.Ed., ABVE - FL - amwfl@myacc.net
Lyndette Mayer, Ph.D., ABVE - NC - lindphd@aol.com
Jan Lowe, MA, CRC, ABVE - MN - redfanjan@aol.com
Cynthia Grimley, MS, ABVE - SC - cpgrimley@aol.com
Nat Fentress, MS, ABVE - LA - nfrehab@worldnet.att.net
Ron Smolarski, MS, CEA, CLCP - MI - ron@beaconrehab.com
Larry L. Sinsabaugh, Ph.D., LMFT - l.l.sinsabaugh.phd@worldnet.att.net
Billy J. McCroskey, Ph.D., CRE, CRC, CRV, ABVE, Co-Editor
Dr. McCroskey is currently the President of Vocationology, Inc., located at 8209 Halifax Court North, Brooklyn Park, MN, 55443. He
was the Founding Editor of the Northwest Georgia Rehabilitation Counselor Newsletter, The Vocationologist, the Journal of
Vocationology, and the Journal of Forensic Vocationology. Dr. McCroskey spends approximately 50% of his time engaged in
1
McCroskey, B. J. (2003). The McCroskey Transferable Skills Program (MTSP 6.0). In: The McCroskey Vocational Quotient System 2003
(MVQS2003 – 6th Edition) Job-Person Matching Program. Brooklyn Park, MN: Vocationology, Inc.
Page: 2
Journal of Forensic Vocationology Vol. 7(1) Monograph
scientific research relating to the prediction starting and average wage. He was the original developer and senior author of the
Vocational Diagnosis and Residual Employability (VDARE) Process. He has engaged in many years of research leading to
development of the McCroskey Vocational Quotient (VQ), the DataMaster Job-Person Matching Program Series and the MVQS
McDOT, McPLOT and MTSP 2001 series of Worker Trait Factor Job-Person Matching Programs and Earning Capacity Prediction
Systems. In 2002 he received the David S. Frank ABVE Lifetime Achievement Award. Ph 763-569-0680, e-mail:
bjmccroskey@juno.com.
In his research, Dr. McCroskey has developed and updated more than 3,247 National, State, County, Parish, Province and Borough
Job Bank Databases of frequently-hired-for jobs. Dr. McCroskey’s Job Person Matching and Earning Capacity Systems have been
peer-reviewed in four Comparison of Job Person Matching Systems Publications (Botterbusch, 1983 & 1986; McDaniels, et. al., 1994;
and Dennis & Dennis, 1998).
Dr. McCroskey is a Lifetime Certified Diplomate Level ABVE Vocational Expert, a Certified Rehabilitation Counselor (CRC), a
Certified Rehabilitation Vocationologist (CRV) and a Lifetime Certified Rehabilitation Economist (CRE). He is a graduate of the
University of Georgia and a life member of both the American Board of Vocational Experts (ABVE) and the American Rehabilitation
Economic Association (AREA).
Dr. McCroskey has presented White Papers on the Predictive Validity of the Vocational Quotient at several National Conferences. He
currently specializes in Pre/Post Injury Employability Evaluations and Earning Capacity Determinations. Dr. McCroskey has worked
on 5,000+ cases involving Individualized Vocational Rehabilitation Planning, Medical Case Management, Job Placement, Job
Analysis, Earning Capacity, Workers Compensation, Rehabilitation Economic Past Wage Loss and Future Diminution of Earning
Capacity, Personal Injury, Wrongful Death, Harassment, Discrimination, Divorce and Social Security. E-mail bjmccroskey@juno.com
William E. Wattenbarger, Ph.D., CRC, CRV, Co-Editor
Dr. Wattenbarger hails from Epworth, GA. He has served as Co-Editor for The Vocationologist, the Journal of Vocationology and the
Journal of Forensic Vocationology since their inception. He is President of Rehabilitation Innovation, Inc. He currently works as a
Rehabilitation Counselor in North Central Georgia. In the past, he has worked as a Supervisor at the Roosevelt Warm Springs
Institute, in Warm Springs, GA.
Dr. Wattenbarger is a computer programmer extraordinaire. He provided the programming skills and consultation necessary to bring
the original McDOT, McPLOT, and MTSP series of Programs to fruition. Dr. Wattenbarger co-developed and co-authored the VDARE
Process. He designed the http://www.vocationology.com website. He enjoys family time and working quietly behind the scenes. Ph:
706-846-3470, email: wewatt@tds.net.
Journal Motto: When worker capacities equal or exceed job demands, employability and earning capacity exist. (BJM, 1981).
Abstracts for all articles in the first five issues of the Journal of Forensic Vocationology (JOFV) are available at our Internet Web Site:
http://www.vocationology.com. If you would like to contribute to this scientific, research-oriented, national peer-reviewed Journal, let
us know at our address below. Manuscripts submitted for peer-review and publication consideration should be provided to us on a 3
1/2 inch disk, or via e-mail attachment, in standard APA style, preferably in a Word for Windows 97, or Word 2000, word processing
document file.
Dr. Billy J. McCroskey, President
Vocationology, Inc.
8209 Halifax Court North
Brooklyn Park, MN 55443-2538
Phone: (763) 569-0680, or (763)-560-7072
e-mail: bjmccroskey@juno.com
Past issues of the JOFV (Volumes 1-6) have been electronically published in Word-97 or Word 2000 format and are available for
purchase (from Dr. Billy J. McCroskey) on the 30-Hour CEU Course Disk 1: The Updating of the Fifth Edition DOT (McCroskey
& Dennis, 2000) as well as well as on three other training Course Disks available for purchase thru Vocationology, Inc., at $165.00
each, including S & H. However you got it, you are now reading from the Spring 2002 issue of the Journal of Forensic Vocationology
Vol. (7), Issue 1. If you are intrigued with the research, theory and practice-related article in this issue and wish to obtain one or more
of the electronically published back issues of the JOFV, let us know by e-mail at bjmccroskey@juno.com and we'll try to
accommodate your wish. Enjoy.
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 3
Sample Product Peer Reviews
Product Peer Review
By
Cynthia P. Grimley, MS, CRC, CCM, ABVE
On
The Updating of the 5th Edition DOT
A 30-Hour CEU Course
Authors: Billy J. McCroskey Ph.D., CRC, CRE, CRV, ABVE
Kenneth L. Dennis, Ph.D., LP, CRC, CRV, LMFT
The U.S. Department of Labor, Employment and Training Administration published the Fourth Edition, Revised 1991
of the Dictionary of Occupational Titles (DOT). The U.S. Department of Labor has no scheduled plan to update the
DOT again. The current research conducted by the U.S. Department of Labor is geared towards the O*NET, which
currently has limitations as admitted by the government. The 4th Edition DOT is outdated. It is now up to the private
sector to update and coordinate both of these resources. The McCroskey Vocational Quotient System Programs,
(MVQS) McDOT, McPLOT and MTSP 2000) has done exactly this.
The text to begin the training and understanding of the MVQS 2000 Program Series (McCroskey, 2000) has been
developed. The 30 CEU Credit Course on The Updating of the 5th Edition DOT details data fusion research to produce
updated job analysis profiles for all 12,775 specific jobs in the McDOT 2000 and related job-person matching software.
The course also includes a fully developed, 45 page, Case Study Report with attachments. The course is copyrighted so
you are unable to print any of the information except the Case Study Report. This is a CD-ROM based course, which
runs off the DAC II Engine. This is compatible for all IBM PC’s and those with 100 percent compatibility. The DAC II
Engine will require 12.6 mb on your hard drive. The course itself will take up 20 MB of hard drive space.
The course installs with ease. After you enter your student registration data information and begin the course you may
exit any time you wish. When you return, the course will automatically pick up where you left off. There are hypertext
words underlined that enable you to click on to obtain the definitions and related information. The course features
colorful and comical graphics that highlight the material. The course contains eleven chapters:
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)
11)
Introduction,
Using McDOT, McPLOT, & MTSP 2000,
Expert Testimony,
Statistical Foundations Relating to Daubert,
Part 1: O’NET Validity under Daubert,
Part 2: O’NET Validity Under Daubert,
VDARE, O’NET 98 & MVQS Theoretical Basis,
O’NET 98 Transferable Skills (TS) Paradigm,
MVQS McDOT 2000 Contributions,
References & Bibliography: A-L,
References & Bibliography: M-Z.
As a bonus the CD-ROM Course contains the first five peer-reviewed Journals of Forensic Vocationology (Vols. 1-5),
a $250.00 value, which serve as the course text. These Journals are full of scientific research articles, which vocational
experts are increasingly in dire need of, when questioned under the Daubert Standards. The Updating of the 5th Edition
DOT and the five peer-reviewed Journals of Forensic Vocationology are available for $150.00 plus $15 for shipping
and handling. This course is the first in a five part series. For ordering information, contact Dr. Billy J. McCroskey,
Vocationology, Inc., 8209 Halifax Court North, Brooklyn Park, MN 55443. Phone: 612-569-0680 or 763-569-0680.
DLU: 08/7/2000
Page: 4
Journal of Forensic Vocationology Vol. 7(1) Monograph
MVQS 2003, 6th Edition Product Peer-Review
I recently purchased the McCroskey Vocational Quotient System (MVQS 6.0, McCroskey, 2003) and the
accompanying 6th Edition Dictionary of Occupational Titles (McDOT 6.0). This system, which was
developed and authored by Dr. Billy J. McCroskey, is a refreshing evolution from his former programs. I
have owned McCroskey reference materials and have copies of all of his systems since 1980. I have also
used the old manual system (VDARE), prior to the purchase of my first computer in 1983. I have seen the
ROC, his first system, but have never utilized it in the field. All of these systems take the guesswork and
much of the argument out of reasonable career choices, worker capacity, employability and earning
capacity. My statement is based on the fact that they are all empirically based, and since their inception,
have been able to meet the Daubert Standards, years before they were handed down from the US Supreme
Court. Both McCroskey’s works and the Daubert Standards rely on scientific research and standardized
measurement. With MVQS 6.0, you are able to produce solid findings that can lead to well researched
opinions, which in turn aids in formulation of your opinions and conclusions to form a solid basis for good
reports and sound testimony. This programs meet all of the requirements set forth for Judges by the 2000
reference entitled, Reference Manual on Scientific Evidence, published by the Federal Judicial Center and
available on the internet. Structuring a report with the data from this program, according to the directions of
this legal reference, will provide valid data on an empirical platform to make decisions, in language the
Judges have been instructed to look for. What more of a winning combination would one want?
McCroskey’s 6th Edition McDOT is masterfully conceived and constructed, accommodating the 1998-2002
O-Net data and all of the updated US DOL Crosswalks including the SOC, NAICS, CENOCC, OESSOC,
ONETSOC, and the Canadian NOC. In my opinion, McCroskey 2003 programs can be used by Vocational
Experts in: Social Security Hearings, Workers Compensation, Personal Injury, Divorce or Dissolution, or
any other venue that requires reliable vocational measurement and earning capacity estimation.
In contrast to his former programs, MVQS 2003 is still complex, yet far simpler to run and to interpret. It is
a dream to load, as it is now totally contained on one computer disk (CD-Rom). The loading is a two-step
process, involving the program and job bank databases all emanating from a single disk. Another of the real
steps forward is the program has been adapted to a Microsoft Windows Access 2000 platform. This makes
for truly simple to interpret screens and greatly simplified operation. The built in help screens, and the
notes “stashed” behind the Help buttons are clearly instructive to the newcomer, both inexperienced and
seasoned. In my opinion, the best visual aspect to this program are the screens, which are displayed in a
simple-to-read format, without a myriad of confusing data to dazzle the eye and confuse the mind.
The built in set of sample “client” cases gets things off and running with the easy introduction of a “person”
into the system as a trial subject. These take the inputs out of the user's responsibility; yet still allow the
flexibility of selected changes in values on worker trait inputs or test/subtest values. The relevant sections
of the internal manual help screens can be “pulled up” and referenced without confusing eye and head
movements. Everything is there on the screen, all in one place, with no distractions.
There is an absence of the old “necessary savvy” to run the programs. I believe anyone can learn to operate
the programs. One must know something about the inputs utilized and certainly one must know and
understand and be able to explain basic measurement concepts to be able to put this information into a
formal report or expert witness testimony. This is also true of the new MVQS Rehabilitation Economist.
All in all, I think this is the best effort I have ever seen in a program such as this and I can’t wait to get a
few cases under my belt and write one of the first technical articles on the McCroskey 2003. Bill
McCroskey and those involved in “the backroom” should be commended on this magnificent undertaking.
This was I’m sure, a massive task, at great expense, with much work and learning involved. To be available
at the very competitive rate that it is selling for makes it an “absolute tool” if one is to provide vocational
forensic opinions with reasonable certainty based on good science in this modern age. (DLU: 09/24/02)
 Scott Streater, D.V.S.
47 Skyline Road, Southern Shores, North Carolina 27949-3600
(252) 255-1854 Phone or FAX or email: : - sesdvs@aginet.com
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 5
Frequently Asked Questions (FAQ) Regarding MVQS 2001-2003 Programs
1.
How do you identify the salient physical and mental requirements of jobs profiled in McDOT?
Answer: We identify the salient physical and mental requirements of jobs from the McCroskey Dictionary
of Occupational Titles – 6.0 Edition (McDOT, McCroskey, 2003). The computerized, Windows(TM)
Access 2000 based, McDOT Program was modeled after the US DOT, 4th edition revised. The date of last
update (DLU) for all profiles of job demands/worker trait requirements in McDOT 2003 was Year 2002.
McDOT 2003 job requirement profiles were data mined from the 75 most vocationally significant O*NET
identified worker trait job requirement element variables for which means data job requirement profiles
were collected and provided for each of 1,122 Occupational Unit (OU) Classification Code Groups (ONET
Code Groups). This was necessary in order to bridge the gap between the outdated US 4th edition and the
updated job demand/worker trait requirement profiles in the McDOT 6th edition DOT.
2.
What type scales, or ratings, exist for measuring these variables?
Answer: The reference for the Job Analysis Scales used to profile jobs in the McDOT 2003 program is the
1991 US HAJ-R. The only major difference is that McDOT incorporates ascending, vs. descending Aptitude
Scales (e.g., the higher the scale level, the greater the job demand; i.e., instead of 1=high & 5=low, McDOT
aptitude scales are interpreted as 1=low & 5=high) otherwise the JA Scales are the same.
3. How do you address issues of validity and reliability?
To bridge the gap from the outdated 4th edition US DOT to the updated McDOT 6th edition DOT, O*NET98 Mean job requirements data from the 75 most salient physical and mental worker trait requirement
elements (52 ability elements on 8-point scales and 23 work context elements on 5 point scales), were data
mined from the 483-variable O*NET ascending scale means data profiles. These data were converted, via
multiple regression studies, to the more familiar 1972-91 HAJ-R job analysis scales, to reflect the 24 most
vocationally salient physical, mental and environmental worker traits. The data were combined, through
data fusion, from element level data, to the worker trait level data, for ease of understanding. In other
words, job demand profiles in McDOT 2003 are presented in terms job requirement scales from the 1991
US Revised Handbook for Analyzing Jobs, which are most familiar to SSA ALJs and knowledgeable
Vocational Experts, who have grown accustomed to the structure of the 1977 and 1991-Revised, US DOT.
The SVP component, which was missing in the O*NET means data profiles, has been reconstructed and
added back to the McDOT job specific data in order for Administrative Law Judges (ALJs) and Vocational
Experts to continue to be able to differentiate between unskilled, semi-skilled, and skilled jobs, as well as
complete reliable, valid transferable skills analyses (Grimley, et. al., 2000a, 2000b).
4. Has scientific research been published in professional peer-reviewed publications?
Answer: Yes, in many journals, monographs and technical manuals (e.g., the MVQS 2001 Resource
Manual) regarding the issues relating to validity and reliability for the MVQS programs employability and
earning capacity predictions. The seven issues of the JOFV (Volumes 1-7) on the MVQS 2003 Program
Installation Disk in the Journals Subdirectory contain a great deal of the scientific research, independent
replication and cross-validation studies attesting to the reliability and validity of the MVQS programs. Other
peer-reviewed journals in which some of the research has been published include: the American Board of
Vocational Experts Journal of Forensic Vocational Analysis and The Earnings Analyst Journal of the
American Rehabilitation Economics Association. Three-way & 5-way inter-rater reliability has been
established for MVQS Programs to be very high (Rxxx=0.99). The transferable skills paradigm has been
tested and found to be very valid relative to a criterion N of 93 Vocational Experts (Grimley, et. al., 2000a,
2000b). In our opinion, in the hands of a trained Vocational Expert, MVQS programs are underpinned with
more than enough scientific research, which has been peer-reviewed and published, to meet or exceed the
standards for expert witness testimony set fourth in the US Supreme Court in their Daubert Decision
(1993), and reiterated in their Joiner and Kumho Decisions, in which they addressed the Federal Rules of
Evidence (FRE 702), governing Expert Witness Testimony.
Page: 6
Journal of Forensic Vocationology Vol. 7(1) Monograph
The MVQS 2001 Resources (McCroskey, 2001) technical manual contains the technical data, including
Statistical Tables with all of the Standard Errors of Measurement, Predictive Validity Coefficients and
Standard Errors of Estimates, frequency counts and percentages of jobs at each level of job demand
associated with job analysis data profiles in McDOT across all 12,974 specific 9-digit DOT Coded
Occupations in McDOT. This technical manual covers the 75 most salient O*NET element variables, the 24
most vocationally salient physical, mental and environmentally significant worker traits, the Vocational
Quotient (Job Difficulty Index; Mean=100, SD=15), and the reconstructed 9-point Specific Vocational
Preparation (SVP) scale.
5. What methods do you use in collecting, updating and providing the data?
Answer: Typically, as in the past, our methods of data collection and updating the DOT rely primarily on
US Department of Labor data available in a variety of forms and cross-walked coded systems. Specifically,
McDOT 2003 job requirement profiles were data mined from the 75 most vocationally significant O*NET98 identified worker trait job requirement element variables for which Means data job requirement profiles
were constructed and provided for each of 1,122 Occupational Unit (OU) Classification Code Groups
(O*NET OU Codes). This was necessary in order to bridge the gap between the outdated US 4th edition
DOT and the updated job demand/worker trait requirement profiles in the McDOT 6th edition DOT.
Since the inception of the McCroskey Series of Job-Person Matching, Transferable Skills, and Earning
Capacity Estimation Programs, the programs and their databases have been periodically updated,
approximately every two years. In the past, McCroskey Programs have been peer-reviewed in each of three
University Comparison Studies of Job-Person Matching Systems (Botterbusch, 1983, 1986 and McDaniel,
et. al, 1993). Course Disk 1 was peer-reviewed by Grimley (2000, 2002) in two Journals of Forensic
Vocationology, and MVQS 2003 Programs were peer-reviewed, by Streater (2002), in this JOFV Issue.
6. What type of training is available regarding the development and use of MVQS Programs?
Answer: There are four 30-Hour CEU Course Disks specifically designed as graduate-level Distance
Education Learning courses for Administrative Law Judges, Vocational Experts, Psychologists,
Rehabilitation Counselors, Vocational Evaluators, Social Workers and related professionals. The Course
Disk CD- ROMs contain the first 5 Journals of Forensic Vocationology, which serve as text for the 4
Courses. The names of the four Courses are:




Updating the 5th Edition Dictionary of Occupational Titles. This course covers the history, theory,
theory extensions, research and systems leading up to the 1939, 1949, 1965, 1977 and 1991
revised US DOT, and culminates with the development of the McDOT 2001 5th edition DOT.
MVQS Vocational Analysis. This course covers scientifically-based transferable skills analysis
using the MTSP 2003 TSA program which is entirely based on the scientific foundation of the
McDOT 6th edition DOT.
O*NET DOT Database Introduction. This course covers the development of the O*NET-98, the
Means data profiles they constructed and the 75 most salient physical, mental and environmental
context job requirement elements which were incorporated into the McDOT 5 th and now the
McDOT 6th Edition DOT and utilized to construct the 24 most vocationally significant worker trait
level requirements of each of the 12,775 specific 9-digit DOT coded Occupational Profiles in
McDOT 2001 and the 12,974 Occupational Profiles in the McDOT 6.0 DOT. And,
Vocational Evaluation 001. This course covers the myriad of relevant vocational and mental
abilities tests and work samples than are commonly used in vocational analyses by vocational
professionals around the US to determine vocational functioning, transferable skills, employability
and earning capacity.
7. Can one become certified as reliable in the use of the MVQS 2003 Programs?
In addition to the 4 Training Course Disks, there are 7 sample practicum cases in MVQS 2003. Possession
of a valid Masters or Doctoral Degree in Vocationology or a related field of studies (See Page 1 of this
Journal for related fields of studies; See also, Vocationologist as defined in McDOT 2003) and two or more
years of relevant field experience, along with successful completion of the 4 Course Disks, with 80% or
better proficiency on the internal Course Disk Tests, and completion of written reports containing
acceptable answers (80% or better proficiency) on the questions posed for each of the 7 sample cases,
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 7
entitles one to sit for the Certified Rehabilitation Vocationologist (CRV) Exam, which is designed to yield
an individualized Inter-Rater Reliability Coefficient Score. All pre-requisites must be met along with an
IRR Coefficient score of 0.80 or better to become CRV Board Certified.
8. What's on the foreseeable horizon as far as planned updates for MVQS 2001 Programs?
In response to the need to incorporate the NAICS Codes, which have replaced the SIC Codes, and
incorporate the updated SOC Codes, which have replaced the OES Codes in the form of OccCodes and the
O*NET OU Codes in the form of O*NET SOC Codes, along with the identification of 199 new jobs in the
US Labor Market, we have now completed developing the McDOT 6th Edition DOT & Related Programs
slated for release in 2003.
The McDOT 6th Edition DOT has been updated to include the 200 new jobs (i.e., McDOT 6.0 now contains
12,975 specific, fully described, profiled, and cross-walked 9-digit DOT-Coded jobs vs. the 12,775 jobs
described, profiled, and cross-walked in the McDOT 5th Edition - Extended Dataset DOT). It includes all
the new cross-walk codes and has updated report formats to choose from for screen outputs, editing - using
Windows™ Office Links to Word for Windows™ 2000, and/or printing.
The McPLOT 6th Edition TestPlot Program will include approximately 900 tests which can be selected
from to build individualized batteries for use in the vocational analysis of individuals. It will continue to
have two internal tests, the Occupational Values and Needs Inventory (OVNI) and the Vocational Interest
and Personality Reinforcer (VIPR) for interest and personality type assessment relative to job matches. It
will have 5 updated report formats to choose from for screen outputs, editing - using Windows™ Office
Links to Word for Windows™, and/or printing.
The MTSP 6th Edition TSA Program includes 3,291 Job Bank Databases of known frequently-hired-for
jobs: One for each county, parish or borough, in each state in the US, One for each US State, One for each
Province in Canada, One for each major city in Puerto Rico, and Job Banks for American Samoa, Guam
and the Virgin Islands (St. Croix, St. John and St. Thomas) along with several national-level job banks and
one job bank containing all 12,974 specific jobs in the McDOT 6th edition DOT. It has 32 updated report
formats to choose from for screen outputs, editing - using Windows™ Office Links to Word for Windows™
2000, and/or printing. MTSP 6.0 includes a new curvilinear regression earning capacity paradigm, which is
more accurate (i.e., with higher predictive validity & smaller Standard Errors of Estimate) than its
predecessor. The curvilinear regression earning capacity paradigm was necessary to more closely follow
the positively skewed curvilinear line of best fit associated with worker wages in the US economy.
(McCroskey, MVQS 2001 Resources Technical Manual, page 92).
The three MVQS 6.0 Edition Programs described above are seamlessly integrated in the Windows™ Access
2000 based platform, which requires a relatively newer and faster computer with approximately 350
megabytes of hard disk space free on an IBM(TM) or IBM compatible PC, with a Windows 95, 98, 2000
ME, or XP based operating system. To insure full installation compatibility on your computer, users need
an installed version of MS Office™ 2000 - Professional Edition, which includes Access 2000, prior to
installing MVQS 2001 Programs.
MVQS 2003 can also be installed on a Laptop Computer and used for those last minute adjustments, which
often have to be made to allow for different hypothetical questions posed by an ALJ, or other interested
parties, to Vocational Experts during Expert Witness testimony. The rule of thumb for installing and using
MVQS 2003 Programs: the faster the computer, the more efficiently relevant data can be retrieved to
answer the myriad of questions which the vocational expert may need to answer with reasonable certainty.
Finally, MVQS 2003 Programs and the MVQS Rehabilitation Economist, which was developed to use
Earning Capacity outputs from MTSP 6.0, are empirically based tools, which can efficiently and effectively
suggest vocational possibilities and estimates of earning capacity and diminished earning cap city. The
programs are wonderful for use in vocational analysis and occupational exploration. However, reserved
clinical judgments, made with reasonable vocational and rehabilitation economic certainty, are an absolute
necessity if they are to be used effectively in expert witness testimony.
Billy J. McCroskey, Ph.D., Co-Editor, 09/24/02
Page: 8
Journal of Forensic Vocationology Vol. 7(1) Monograph
The McCroskey Vocational Quotient System (MVQS)
Theory of Transferable Skills: Revised, Extended and
Updated for the 21st Century
By
Billy J. McCroskey, Ph.D., CRE, CRC, CRV, ABVE,
Cynthia P. Grimley, MS, CRC, ABVE,
John M. Williams, D.Ed., ABVE, CRC, NCC, ABDA, CCM,
Steven J. Hahn, MS, CRC, QRC, CRV, CVE,
Jan Lowe, MS, CRC, QRC, ABVE,
William E. Wattenbarger, Ph.D., CRC, CRV,
David B. Stein, Ph.D., CRC, CDMS, LPC, LRC, and
Kenneth L. Dennis, Ph.D., LP, LMFT, CRC, CRV
Abstract
The McCroskey Vocational Quotient System 2001 (MVQS 2001) Transferable Skills
Program (MTSP 2001; McCroskey, 2001) provides Transferable Skills Analysis (TSA)
based on an equal interval Transferable Skills Percent (TSP) scale. Theoretically, this
interval scale ranks job matches in relevant labor markets of interest in terms of suitable
employability, from 0 to 97% in terms of Transferable Skills (TS), Occupational Values
and Needs Inventory (OVNI), Vocational Interests and Personality Reinforcer (VIPR)
Type, Vocational Quotient (VQ), Specific Vocational Preparation (SVP) and Earning
Capacity. MVQS TS Theory expands and extends the Minnesota Theory of Work
Adjustment [Dawis, England & Lofquist (1964); Dawis, Lofquist & Weiss (1968); Dawis
(1976);] with practical applications based on mathematical models underlying a wide
variety of vocational databases including the McCroskey Dictionary of Occupational
Titles-5th Edition DOT (McDOT 2000/2001; McCroskey: 2000, 2001), the US DOT (US
DOL: 1977 & 1991) and O*NET (US DOLETA: 1998) The intent of here is to examine
the underlying theory, methodology and rational of MVQS MTSP TSA TSP, its theoretical
underpinning and implications for reliable, valid measurement and quantification of
MVQS 2001 MTSP TSA TSP Levels, Earning Capacity Estimates and related Analyses.
Introduction
The theoretical model underlying MVQS 2001 Transferable Skills Analysis (TSA)
assumes the following key concepts:
1) Work Skills are those skills (knowledge and abilities) acquired by a worker
through successfully demonstrated specific vocational preparation (past relevant
college, technical, vocational, apprenticeship, and/or on-the-job training) for past,
current, and/or future, specific suitable semi-skilled or skilled jobs in the worker's
ongoing, evolving career development.
2) Transferable Work Skills are those work skills (knowledge and abilities) acquired
through successfully demonstrated specific vocational preparation, required by
one or more specific suitable semi-skilled or skilled job matches, to varying
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degrees of transferability, either in an upward, lateral or downward direction,
depending on qualifications (DOQ), in relevant labor markets of interest.2&3
3) Residual Transferable Work Skills are those work skills (knowledge and
abilities) successfully demonstrated through past relevant work history and
associated specific vocational preparation, which are retained from past relevant
work history based on the clients Residual Employability Evaluative Data Profile,
following injury or disability, and required by one or more specific suitable semiskilled or skilled post-injury or post-disability job matches, to varying degrees of
transferability, in one or more relevant geographic labor markets of interest.
The core databases, from which the MVQS 2000/2001 TSA TSP equal interval scale was
derived, are included in the McCroskey Dictionary of Occupational Titles 2000/2001 - 5th
Edition DOT Updates (McDOT 2000/2001, McCroskey, 2000/2001). McDOT 2000 and
McDOT 2001 were updated based on selected transformed O*NET 984 DOT replacement
worker-trait-element-level data variables (N=75), the United States Dictionary of
Occupational Titles-Revised 4th Edition (DOT; US DOL, 1991), post-1991 DOT errata
changes and additions and the 28 cluster analysis variables and mathematical models
[including outlier reclassification based on Ward's Minimum Variance Method5 (Ward,
1963)], used to produce the O*NET 98 Occupational Unit (OU) Classification Coded
DOT replacement Transferable Skills Groups (N=1,172). Prior to 1998, when the O*NET
98/OU Codes were adopted for use in MVQS MTSP TSA, all earlier MVQS, including the
DataMaster, Program series had used GOE6 codes in place of O*NET 98/OU Codes.
The MVQS 2001 TSA TSP scale expanded the O*NET 98 OU Transferable Skills Groups
database DOT replacement TS grouping paradigm (based on 28 grouping variables) to
include job-person matching on the 24 most vocationally significant worker traits, along
with an identified transferable skills gradient on an overall 5-point equal interval scale
across and a 0 to 97% equal interval scale within transferable skills groups.
2 Using the classic Vocational Diagnosis and Assessment of Residual Employability (VDARE; McCroskey, Wattenbarger, Field & Sink,
1977) approach to developing the client's Residual Employability Profile (REP) with appropriate modifications to the High
Across Work History Profile [the Unadjusted Vocational Profile (UVP)] based on expected permanent vocational limitations
stemming from expected permanent medical, psychiatric and/or psychological restrictions, MTSP TSA typically yields jobperson matches with varying degrees of transferability, with lateral and below TS Analysis for jobs with VQs at, or below,
those levels of overall job difficulty demonstrated by successful past relevant work history. This approach is most often used
in the analysis of SSDI and SSI applications for purposes of determining employment potential and earning capacity status.
3
The classic VDARE approach (when combined with a full or partial battery of vocational tests and ratings covering the 24
most vocationally significant worker traits) enhances MTSP TSA by allowing for job-person matches with varying degrees of
upward Transferable Skills Analysis, as well as job matches with lateral and below TS Analysis. Under these
conditions, MTSP TSA job-person matches may include job matches with VQs above, at, or below, those levels of overall
job difficulty demonstrated by successful past relevant work history. This approach is most often used in Worker's
Compensation, Personal Injury, Divorce and related cases with litigation potential.
4
O*NET 98 - The Occupational Information Network Version 1.0 [United States Department of Labor Employment and
Training Administration (US DOLETA), 1998)].
5
Ward's Method. This method is distinct from all other methods because it uses an analysis of variance approach to evaluate the
distances between clusters. In short, this method attempts to minimize the Sum of Squares (SS) of any two (hypothetical)
clusters that can be formed at each step. In general, this method is regarded as very efficient, however, it tends to create
clusters of small size.
6
Guide for Occupational Exploration (GOE) Codes were six-digits: interest area (1st 2), work group (1st 4), work group sub-group (1st 6).
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Literature Review
Decision-making about loss of earning capacity in civil litigation as well as appropriate
job placement for persons with or without disabilities for vocational experts, vocational
rehabilitation and other vocational counselors has been based, at least in part, on
transferable skills analysis (Maze & Williams, 1993; Williams and Maze, 1994). This has
been realistic and helpful to understanding and planning during vocational counseling.
The McCroskey Vocational Quotient System (MVQS) TSA TSP Programs have been
empirically tested and found to be extremely reliable (3-way inter-rater reliability:
Rxxx=0.9864; McCroskey, Haskins & Smolarski, 1995) and valid [i.e., the MVQS TSA
TSP 5-point scale was found to be in agreement with 5-point scale ratings from a national
sample of Vocational Experts (N=93; Rxy=0.96; SEE=0.357; Grimley, Williams, Hahn &
Dennis 2000a, 2000b)]. MVQS TSA TSP information can reliably and validly increase
the effectiveness and efficiency of vocational experts and rehabilitation service providers
seeking to provide realistic occupational counseling to their clients.
Landsea (1994) pointed out that it is individual-specific factors (e.g. age, education,
gender, occupation, industry, and geographical location), which impact the growth of
lifetime earnings. This suggests a linkage between acquired knowledge and skills and
lifetime earnings. Vocational experts have long viewed transferable skills analysis as a
way of determining suitability of specific occupations for a given individual and the
impact of occupational choices on earning capacity.
Cutler, Cutler and Ramm (1995) indicated that transferable skills analysis methodologies
offered in job-person matching software are commonly used by vocational experts and
rehabilitation counselors to identify specific occupations and earnings without any real
understanding of the limitations of the data. Cutler, et al opined that using the Dictionary
of Occupational Titles (U.S. Department of Labor, 1991) as the basis for identifying the
skills and functional capacities associated with jobs was problematic. There were too
many problems with the age and aggregate nature of the data.
Cutler, et al (1995) said that frequency of jobs by occupation (as a basis for determining
loss of number of jobs to which one may have access pre-/post-injury) was not
considered to be entirely accurate. It required too much reliance on aggregate census
data. In response, Grimley, et al (2000a, 2000b) reported that Cutler, et al failed to
address the fundamental issue regarding validity of the concept of transferable skills by
comparing construct validity along with content validity.
In theory and in practice, assessment of transferable skills acquired through work and
education has been seen as an effective method for identifying what occupations a worker
can perform (assuming their worker trait capacities profile meets or exceeds the job
requirement profiles) and what occupations an employer believes an individual has the
ability and transferable skills to perform. This forms the basis for suggesting a person can
reasonably be successfully placed in a specific job type and can reasonably be expected
to earn a reasonable level of income estimated for selected job types in the relevant
geographic labor market of interest (Grimley, et al, 2000a, 2000b).
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Earlier research involving VDARE7 profiling of client vocational capacities based on
work history requirements, and modified using counselor judgment following a review of
written case notes and general medical information, yielded inter-rater reliability
estimates as high as Rxxx=0.9944 (McCroskey, 1979, McCroskey & Perkins, 1981).
Research comparing counselor produced VDARE Profiles of Client Vocational Capacities
with those generated through Rehabilitation Facility Vocational Evaluation results,
counselor produced VDARE profiles with placement outcome profiles, facility evaluation
produced client profiles with previous facility evaluation results, and facility evaluation
produced client capacities profiles with placement outcomes based on VDARE profiles of
worker requirements yielded very high validity coefficients ranging from R xy=0.93 - 0.95
(Burge, 1978; McCroskey, 1979; McCroskey & Perkins, 1981).
Other related research (using job requirements/client capacities comparison profiling)
provides additional support for the rationale underlying the MVQS 2001 worker trait
factor approach to better understanding individual vocational potential and work history
related transferable skills, as a means to improving the quality of basic human services by
vocational rehabilitation counselors and evaluators (Colvin, 1972, Field, McCroskey,
Grimes & Wattenbarger, 1978; Hanman, 1951; Knowles, 1978; McCroskey, 1979,
Reinhardt, 1978; Teal, 1978, Tratner, Fine & Kubis, 1955; Wattenbarger & McCroskey,
1978 and Wattenbarger, 1981.) Most of these articles were cited with research summaries
and a full set of references in Chapter 3, Matching People with Jobs: Perspectives on Job
Analysis and the Typical Job in the United States (McCroskey & Perkins, 1981, The
Manual for the McCroskey Vocational Quotient System, pp 40-56).
Predictive validity of the MVQS MTSP 2000/2001 TSA TSP scale has been empirically
tested by comparing the MTSP 2000/2001 TSA TSP rankings with averaged rankings of
American Board of Vocational Experts (ABVE, N=93), through the use of the ABVE
Transferable Skills Evaluation Test8. Specifically, Grimley, et al, (2000a, 2000b) found
the MVQS McCroskey Transferable Skills Program (MTSP 2000/2001) Transferable
Skills Percent scale to be a valid predictor of averaged Vocational Expert responses on
the ABVE Transferable Skills Evaluation Test (TSET; McCroskey & Dennis, 2000).
The ABVE TSET instrument was developed based on the MTSP 2000/2001 TSA
algorithm. It incorporated O*NET 98 Transferable Skills groups and a series of related
job analysis codes to create and stratify transferable skills on a per cent scale ranging
from 0 to 97% across, and within, the updated O*Net 98 Transferable Skills Groupings.
In Grimley, et al, (2000a, 2000b), the ABVE TSET was subdivided into five broadly
defined ascending Transferable Skills Levels ranging from 1 to 5 across the TSP 0 to
97% continuum, and administered to 93 Vocational Experts at the ABVE 2000 Spring
7
Vocational Diagnosis and Assessment of Transferable Skills (VDARE, McCroskey, Wattenbarger, Field & Sink, 1977).
8
The ABVE Transferable Skills Evaluation Test (McCroskey & Dennis, 2000) was specifically developed for studies designed to
empirically determine the reliability, predictive validity and error rate for the MTSP 2000/2001 Transferable Skills Percent
relative to the opinions of Vocational Experts (N=93) through scientific research. Predictive validity (Rxy) between the two
variables was 0.96; the coefficient of determination (R2) was 0.92, and the standard error of estimate (SEE) was 0.357.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Conference in New Orleans, LA. The Predictive Validity Coefficient between the MTSP
2000 TSP rankings and the criterion-referenced Prediction Estimates of the Vocational
Experts (N=93) was extremely high (Rxy=0.96). Correspondingly, the Coefficient Of
Determination (R2=0.92) was extremely high, and the Standard Error of Estimate
(SEE=0.357) was found to be well within reason.
The Outdated Transferable Work Skills Definition
Williams (1998) stated that computer programs based on DOT (1991) ran the risk of not
controlling for methodological error variance if not basing the sorting functions of the
software on the appropriate factors. He went on to explain that the Revised Handbook for
Analyzing Jobs (HAJ-R, 1991) listed three variables that were relevant to assessment of
transferable skills: Work Fields, MPSMS and Specific Vocational Preparation (SVP).
1. Work Fields are Machine, Tools, Equipment and Work Aids (MTEWA) grouping
codes that reflect how work gets done, the result of work, and the purpose of the
job. Although these categories range from specific to general, they represent
homogeneous groups related to technologies or objectives. It is easy to justify the
inclusion of Work Fields into a Transferable Skills Analysis. People can acquire
skills in getting work done, and these skills can be transferred to another job.
2. Materials, Products, Subject Matter, and Services (MPSMS) grouping codes
describe what a worker does and what gets done to what. This coding structure is
similar to the Work Fields structure, and its use in Transferable Skills Analysis
appears logical. Skills related to what the worker does and how this work is
completed can realistically be transferred to another job.
3. Specific Vocational Preparation (SVP) is the amount of lapsed time required for a
typical worker to learn the techniques, acquire the information, and develop the
facility needed for average performance in a specific job-worker situation. Use of
SVP as a selection variable in Transferability of Skills Analysis (TSA) assumes
that the individual can perform all occupations, which have the same or lower
SVP, and that jobs requiring an SVP of no more than 2 (up to 1 month), are
unskilled jobs. Since SVP represents time required to learn a job (though on-thejob training, formal vocational or academic training) and not any inherent
knowledge or skill associated with the job, SVP cannot be transferred from job to
job; instead, it is used primarily in differentiating between unskilled jobs (with an
SVP level of either 1 or 2) and semi-skilled or skilled jobs. However, SVP is
highly correlated with Overall Job Difficulty as measured by the McCroskey
Vocational Quotient (VQ), Reasoning (R), Math (M) and Language (L)
Development, that can be identified in terms of individual capacities and job
requirements. Therefore, these factors may be more predictive of ability to
perform specific job tasks than SVP alone. Use of SVP as a discriminator of what
jobs are screened out during a TSA may not make sense when VQ, R, M and L
development are not considered. This is particularly true when injured persons are
limited by physical or mental impairments to sedentary or light work where the
preponderance of jobs requires higher SVP levels than many medium or higher
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exertion level occupations. SVP alone may be overly exclusive. However, SVP
may have significant value in determining those jobs which are more likely to be
available to a given individual based on employer perception of minimum
demonstrated knowledge and/or skills required for being hired. This is a different
issue than transferable skills, but an important part of any earning capacity
analysis.
Prior to the vastly improved O*Net Transferable Work Skills Groupings coupled with the
MVQS TSA TSP scale replacement data, the outdated SSA Transferable Work Skills
Definition stated in the Code of Federal Regulations (20CFR404.1568, 383-385), as it
related to Social Security Disability Insurance (SSDI) eligibility decision-making, was
generally accepted as the fundamental basis for most transferable skills analyses.
Restated for clarity, it said:
(1)
What we mean by transferable skills. We consider you to have skills that can be
used in other jobs, when the skilled or semi-skilled work activities you did in past
work can be used to meet the requirements of skilled or semi-skilled work
activities of other jobs or kinds of work. This depends largely on the similarity of
occupationally significant work activities among different jobs.
(2)
How we determine skills that can be transferred to other jobs. Transferability is
most probable and meaningful among jobs in which- The same or a lesser degree of skill is required;
 The same or similar tools and machines are used; and
 The same or similar raw materials, products, processes, or services are
involved.
(3)
Degrees of transferability. There are degrees of transferability of skills ranging
from very close similarities to remote and incidental similarities among jobs. A
complete similarity of all three factors is not necessary for transferability.
However, when skills are so specialized or have been acquired in such an isolated
vocational setting (like many jobs in mining, agriculture, or fishing) that they are
not readily usable in other industries, jobs, and work settings, we consider that
they are not transferable.
(4)
Transferability of skills for individuals of advanced age. If you are of advanced
age (age 55 or older), and you have a severe impairment(s) that limits you to
sedentary or light work, we will find that you cannot make an adjustment to other
work unless you have skills that you can transfer to other skilled or semiskilled
work (or you have recently completed education which provides for direct entry
into skilled work) that you can do despite your impairment(s). We will decide if
you have transferable skills as follows. If you are of advanced age and you have a
severe impairment(s) that limits you to no more than sedentary work, we will find
that you have skills that are transferable to skilled or semiskilled sedentary work
only if the sedentary work is so similar to your previous work that you would need
to make very little, if any, vocational adjustment in terms of tools, work processes,
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Journal of Forensic Vocationology Vol. 7(1) Monograph
work settings, or the industry. (See Sec. 404.1567(a) and Sec. 201.00(f) of
appendix 2.) If you are of advanced age but have not attained age 60, and you
have a severe impairment(s) that limits you to no more than light work, we will
apply the rules in paragraphs (d)(1) through (d)(3) of this section to decide if you
have skills that are transferable to skilled or semiskilled light work (see Sec.
404.1567(b)). If you are closely approaching retirement age (age 60-64) and you
have a severe impairment(s) that limits you to no more than light work, we will
find that you have skills that are transferable to skilled or semiskilled light work
only if the light work is so similar to your previous work that you would need to
make very little, if any, vocational adjustment in terms of tools, work processes,
work settings, or the industry. (20CFR404.1568, 383-385)
Updating the Outdated Transferable Skills Definition
Specific Vocational Preparation (SVP) is by definition on an ordinal (rank ordered) scale
of measurement which is highly correlated with overall job difficulty as measured by the
interval scale Vocational Quotient (VQ; McCroskey, 1981). Inclusion and use of more
appropriate equal-interval-scale variables, like the VQ, as an index of overall job
difficulty/work capacity, along with MVQS TSA TSP equal-interval-scale data, helps
overcome SVP ordinal scale limitations, and has strongly impacted conventional thinking
of many experts in our field about the theory and practice of Transferable Skills Analysis.
The McCroskey Transferable Skills Program (MTSP 2001) uses the Vocational Quotient
(VQ) to define vocationally significant worker trait job demands/requirements and
corresponding worker capacities. The VQ weights the complexity and difficulty of Job
Types based on their profiles of job demands, providing an appropriate control on
academic, intellectual, attending, physical capacity, environmental tolerance and related
worker trait job demands/requirements and people worker trait capacities covered under
the four most vocationally significant worker trait factors and their 24 most vocationally
significant worker traits.
Primary Considerations should first be given to the 24, Most
Vocationally Significant MVQS VQ Variables, before any other
Transferable Skills Indicator Variables are considered
The Worker's Four-Factor by 24 most vocationally significant MVQS Worker Trait Level
Evaluative Data Capacities Profile must match or exceed the updated McDOT 2001 5th
ed. DOT Job Demands Profile relative to any given jobs before Transferable Skills
should be considered relative to a worker's past relevant work history. These include:
1. General Educational Development (GED) / Intellectual Functioning
 (R) - Reasoning Development; Replaces (G) - General Intellectual
Aptitude, which was inappropriately truncated at the low end.
 (M) - Math Development; Replaces (N) - Numerical Aptitude, and
 (L) - Language Development; Replaced (V) - Verbal Aptitude;
2. Aptitudes
 Perception
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 (S) - Spatial Perception,
 (P) - Form Perception, and
 (Q) - Clerical Perception,
 Dexterity
 (K) - Motor Coordination/Bimanual dexterity Aptitude,
 (F) - Finger Dexterity Aptitude, and
 (M) - Manual Dexterity,
 Other
 (E) - Eye-Hand-Foot Coordination, and
 (C) - Color Discrimination;
3. Physical Demands
 (PD1) - Lift, carry, push, pull, sit, stand, walk,
 (PD2) - Climbing, balancing,
 (PD3) - Bend, stoop, crouch, squat, kneel, crawl,
 (PD4) - Reaching, handling, fingering, feeling,
 (PD5) - Talking, hearing, writing,
 (PD6) - See up-close and see far-away; and,
4. Environmental Tolerances
 (EC1) - Work location
 (EC2) - Extreme cold,
 (EC3) - Extreme Heat,
 (EC4) - Dampness, wetness, humidity,
 (EC5) - Noise, vibrations,
 (EC6) - Hazards: machinery, electrical, chemical, unprotected heights, and
 (EC7) - Adverse Atmosphere: dusts, fumes, odors, mists or gases.
VQ Research: Implications for Transferable Skills Analysis
VQ has been frequently validated (and cross-validated) as a reliable, valid and very high
predictor of Wages Offered on Job Service Work Orders as well as Workers (McCroskey
& Hahn, 1997; Mayer, 1998). VQ has also been found to be a reliable, valid, and a very
high predictor of earning capacity, as measured by reported Occupational Employment
Statistics (OES) income data at the Mean as well as at the 10th, 25th, 50th (Median), 75th
and 90th Percentile Wage Distributions (McCroskey, Hahn & Dennis, 2000).
The VQ (and all the scientific research data underpinning it) combined with SVP and
related considerations shores up the testimony of Vocational Experts. This combination
shores up their testimony foundation with much more defendable scientifically
underpinned explanations for TSA and Earning Capacity predictions along with known
error rates. For example, knowing that SVP has been established to have very high
predictive validity along with known very low error rates (Rxy=0.90, SEM=0.02,
SEE=0.92, McCroskey, 20019) and that VQ has been established to have even higher
predictive validity along with known very low error rates (Rxy=0.97, SEM=0.13,
9
McCroskey, Billy J. (2001). Statistical Analysis: Section 1. MVQS 2001Resources. Brooklyn Park, MN: Vocationology, Inc.
beginning page 45 (subsection - fifth page of the Statistical Analysis tables in Section 1).
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Journal of Forensic Vocationology Vol. 7(1) Monograph
SEE=3.81), it would be much easier to defend a job match with an SVP of 1 or 2, which
also has a VQ of 84 or less (in the below average job difficulty range at less than the 16th
percentile of overall job difficulty), as being unskilled, than a job with an SVP of 1 or 2
and a VQ of 100 (the 50th percentile of Overall Job Difficulty in the United States). In
MVQS Transferable Skills Analysis, this is a major consideration.
The O*NET Viewer and Transferable Skills Analysis
The O*NET Viewer contained its own, very crudely defined, transferable skills analysis
module (Related Occupations Module Option), which did not represent actual
occupations (Dennis & McCroskey, 1999). Instead, it represents fairly large groupings of
possibly related (and often unrelated) O*NET OU coded groups of jobs. Suitably related
jobs can be found in the O*NET data, but the information provided in the Related
Occupations module can only be used in the most general terms. For example, the OU
code of 34002E is Managing Editors. Related Occupations listed Book Editors (34002G),
Program Directors (34056H), Audiovisual Specialists (31508), Producers (31511C),
Technical Writers (34005), and Museum Research Workers (31511C).
The connection between some of the above-listed OU cluster analysis groupings is fairly
clear. However, it also listed (as part of its up to 10 groupings) Employee Relations
Specialist (21511C) as being related. It can be seen that the OU code changed from
starting with a 3, to starting with a 2. It would be hard to explain how the skills could be
easily transferred. Other occupations included were First Line Supervisors,
Administrative Support (51002B) and Appraisers, Real Estate (43011). Except for getting
people to draw pictures of houses for the newspaper, it is difficult to understand what
skills would transfer from a Managing Editor to a Real Estate Appraiser.
O*NET 98 transferable skills groups were created based on cluster analyses by
mathematically grouping jobs that had similar requirements on 28 variables, and
excluding other job groupings that were not similar enough based on these 28 variables.
The O*NET 98 Transferability Of Skills Grouping Paradigm10
The Job Analysis Variables (N=28) selected and used by O*NET 98 in their cluster
analyses and development of Transferable Skills Groups (N=1,172) included:
1) Primary Materials, Products, Subject Matter and Services (MPSMS) code,
2) Secondary Materials, Products, Subject Matter and Services (MPSMS) code,
3) Tertiary Materials, Products, Subject Matter and Services (MPSMS) code,
4) Primary Work Field: Machines, Tools, Equipment and Work Aids (MTEWA)
code,
5) Secondary Work Field: Machines, Tools, Equipment and Work Aids
(MTEWA) code,
6) Tertiary Work Field: Machines, Tools, Equipment and Work Aids (MTEWA)
code,
10
Farr, J. M., Ludden, L. & Mangin, P. (1998). Appendix C, Phase I: Developing Homogeneous Occupations for O*NET. In:
The O*NET Dictionary of Occupational Titles. Indianapolis, IN: JIST Works, Inc., pp. 573-579.
Journal of Forensic Vocationology Vol. 7(1) Monograph
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7) Specific Vocational Preparation (SVP),
8) (D)ata Complexity,
9) (P)eople Complexity,
10) (T)hings Complexity,
11) General Educational Development: (R)easoning,
12) General Educational Development: (M)athematical,
13) General Educational Development: (L)anguage,
14) (G)eneral Learning Ability Aptitude,
15) (V)erbal Aptitude,
16) (N)umerical Aptitude,
17) (S)patial Perception Aptitude,
18) (P) Form Perception Aptitude,
19) (Q) Clerical Perception Aptitude,
20) (K) Motor Coordination Aptitude,
21) (F)inger Dexterity Aptitude,
22) (M)anual Dexterity Aptitude,
23) (E)ye-Hand-Foot Coordination Aptitude
24) (C)olor Discrimination Aptitude
25) (D)irecting Temperament,
26) (P)eople Temperament,
27) (I)nfluencing Temperament, and
28) (E)xpressing Temperament.
O*NET 98 originally grouped 12,761 DOT occupations into 852 Occupational
Employment Statistics (OES) occupations. This number was considered too small, and
statistical clustering was employed to divide the OES codes into sub-categories.
Ultimately, this resulted in 1,122 rated and viewable OU groupings that were considered
to have more homogeneous skills that represented meaningful differences between
occupations. The OU groupings were structured to display Belongingness (matching
occupational definitions) and Homogeneity (more similar skills within groups than
between groups) for the purpose of determining skill transferability.
As a result of the Cluster Analyses which were completed and finalized for the new
O*NET 98 Transferable Skills Groupings, 1,172 Occupational Unit Classification (OUC)
Groups, or, as they have become better known, O*NET Code Transferable Skills (TS)
Groups were created.
O*NET Code TS Groups were based on USDOL Occupational Employment Statistics
(OES) Code Groups, which were identified by the first 5 digits, of each 5-digit or 6character O*NET 98 Code TS Group. Thus, OU Classification/O*NET 98 Coded TS
Groups were more highly refined, empirically derived, Transferable Skills (TS)
Subgroups, within the OES Code Group Classification Structure.
Of the 1,172 identified O*NET 98 Coded TS Groups, Means Data Profiles on 483 worker
trait elements for 1,122 groups were reported in the US DOLETA O*NET 98 Version 1.0
Program. Researchers at Vocationology, Inc. constructed Means Data Profiles with 483
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Journal of Forensic Vocationology Vol. 7(1) Monograph
worker trait elements for the remaining 50 O*NET 98 Coded Groups (not reported in the
O*NET 98 Vers. 1.0 Program) and added two new groups (each containing only 1 job),
bringing the total N to 1,174 TS groups. Vocationology researchers also reconstituted 12
jobs which were reclassified with different 9-digit DOT Codes by O*NET 98 researchers.
In the O*NET 98 Version 1.0 Viewer Program, there were 1,122 OU TS Groups for
which means data element profiles were viewable in the O*NET 98 Viewer. There were
12,761 9-digit DOT-Coded Job Types contained in the O*NET 98 DOT Crosswalk of
unduplicated jobs, and 12,797, 9-digit DOT-Coded Jobs contained in the O*NET 98
DOT Crosswalk when duplicated jobs are included. No specific job analysis worker trait
profiles were included for any job types listed in any of the O*NET 98 databases.
In the McDOT 2000/2001 programs, there are 12,775 specific unduplicated 9-digit DOTCoded Jobs and 12,811, 9-digit DOT-Coded Job Types contained in the McDOT
2000/2001 Crosswalk when duplicated jobs are included. In McDOT 2001, each job has
a specific worker traits/job requirements profile, with respect to the 24 most vocationally
significant worker traits and 3 aggregate variables (VQ, SVP and ZONE).
Many O*NET 98 OU codes were either too broad, or too narrow (on their own), to use as
stand-alone transferable skills groups. However, coupling together the 1,172 O*NET 98
database Occupational Unit (OU) transferable skills groups with the MVQS TSA TSP
scale, as major components of the MVQS MTSP 2000/2001 transferable skills analysis
algorithm, significantly improved the TSA TSP capabilities of the MVQS McCroskey
Transferable Skills Programs (MTSP 2000/2001; McCroskey, 2000, 2001), in terms of
distinguishing occupations most reasonably related via true transferability of skills.
Face, Content and Construct Validity became much more apparent in the MTSP 2001
program than in the stand-alone O*NET 98 OU code groups themselves. The clarity of
the MVQS MTSP transferable skills algorithm allowed the MTSP transferability scale to
be converted from its previous 1 to 46 level Raw Score Scale to its 5-point standardized
interval scale, Transferability of Skills Percent (TSP; 0-97%)scale. MTSP 2001 users
have found the percentage based TSP rated outputs for MTSP Job Matches much more
intuitive, understandable, easier to interpret, easier to defend and easier to explain.
TSA software programs are now faced with the challenge of integrating O*NET data
with traditional DOT data to predict actual jobs an individual can perform and the level of
expected earnings associated with these selected occupations. Traditional thinking about
TSA improvements offered via use of the Vocational Quotient (VQ; Williams, 1998;
McCroskey and Hahn 1997; 1998a; 1998b) and the 24 most vocationally significant
worker trait requirements from which it is constructed, must lead vocational experts and
counselors to ask the less technical and possibly more initially critical questions of
whether or not the construct of transferable skills has been sufficiently understood and
explained to the professional community and whether or not sufficient consensus on what
this construct means and how it should be used in TSA software has been developed.
Journal of Forensic Vocationology Vol. 7(1) Monograph
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Without construct validity, TSA software will have little value to predict suitable jobs
and associated earnings. To the end of determining if transferable skill is a meaningful
construct that can be used reliably to predict suitable occupations for individuals (i.e.,
predictive validity), the Grimley, et al (2000a, 2000b) was a meaningful first step. In that
study, it was hypothesized that if Vocational Expert opinion and the TSA TSP results of
MVQS MTSP 2000/2001 software were shown to be consistent, one could argue the
concurrent validity of the software and vocational expert opinion. To that end, the
Grimley, et al, study assessed the concurrent validity between vocational expert opinions
and the results of the McCroskey Transferable Skills Program (MTSP 2000/2001).
Specifically, in the Grimley, et al, study, the McCroskey Transferable Skills Program
(MTSP 2000/2001) Transferable Skills Percent (TSP) scale was found to be a valid
predictor of the averaged responses of 93 Vocational Experts tested using the ABVE
Transferable Skills Evaluation Test instrument. The Predictive Validity Coefficient
between the MTSP 2000/2001 TSP rankings and the criterion-referenced Prediction
Estimates of the Vocational Experts (N=93) was extremely high (Rxy = 0.96).
Correspondingly, the Coefficient Of Determination (R2 = 0.92) was extremely high.
Finally, the Standard Error of Estimate (SEE = 0.357) was found to be very reasonable. In
essence, the Grimley, et al, study found strong support for concurrent, predictive, and
underlying construct validity of the McCroskey Vocational Quotient System (MVQS)
Theory of Transferable Skills and the resulting MTSP TSA TSP algorithm embodied in
the MTSP 2000 and 2001 Programs.
The MVQS Transferable Skills Algorithm
MTSP 2001 first looks at VQ and then matches people with jobs on the 24 most
vocationally significant worker trait variables. Job-person matches with VQs of 85 or
greater (the semi-skilled to skilled work range), which match the worker's evaluative data
profile relative to profiles of job demands (in the geographically selected MTSP 2001 job
bank database of choice) on the 24 most vocationally significant variables extracted from
McDOT 2001 are first considered for potential TS level assignment during the MTSP
2001 TSA process. Job-person matches with VQs less than 85 are assumed to be
unskilled jobs and are therefore considered last during MTSP 2001 TSA process.
The MVQS McCroskey Transferable Skills Program (MTSP, McCroskey, 2000; 2001)
use a combination model to produce the TSA Transferable Skills Percent (TSP) values
for each job-person evaluative data profile match relative to similarities between selected
cross-walks associated with job person matches and successfully demonstrated past
relevant client work history. Numerical TSA TSP values are accumulated in the MTSP
2001 Program to produce a theoretical representation of the relative percent of total skills
that are transferable from past relevant work history to each identified job-person match.
Following comparison on the O*NET Occupational Unit Classification (OUC) Code
which produces a similar comparison on 28 vocational variables, the TSP Scale is
incorporated to produce a much more refined TSA Scale Ranking. Briefly, an overview
of the MTSP TSA TSP Scale Rankings Algorithm is described below.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
The MVQS MTSP TSA TSP Algorithm Overview
TSP Level TSP Range Verbal Scale
Level 5
80 - 97% = semi-skilled or skilled job(s) with high transferable skills
Level 4
60 - 79% = semi-skilled or skilled job(s) with moderate transferable skills
Level 3
40 - 59% = semi-skilled or skilled job(s) with low transferable skills
Level 2
20 - 39% = semi-skilled or skilled job(s) with few if any transferable skills
Level 1
0 - 19% = unskilled job(s) with no significant transferable skills
The Highest MVQS Transferable Skills Percent (TSP) Level
Jobs in the highest overall level of Transferable Skills (TS) fall in the 80 to 97 percent TS
range. These TS levels are reached when a worker returns to a past relevant work history
job, or a job-person match with the same (or highly similar) DOT Code (first 3 digits
must match exactly), OU Classification/O*Net Code (all 5 or 6 characters must match
exactly) and Work Duties. Job-person matches at the highest TS level (97%)11 must also
exactly match work history on six TS relevant Crosswalk Codes, (i.e., SIC, SOC, CEN,
IND, MPSMS & Work Field/MTEWA codes) to achieve a TS level of 97%.
The Semi-Skilled to Skilled Work Range
For each job-person match with a VQ of 85 or greater (the 16th to 99th+ Percentile range),
MTSP 2001 assumes the job match to be in the semi-skilled to skilled work range and
evaluates the cumulative number of all possible crosswalk code matches between the
person's identified work history set of jobs relative to each individually identified jobperson match. Following this, the Transferable Skills Percent (TSP) algorithm
accumulates a two-digit TS value. The first digit (tens value) of each TS value represents
job match similarities between work history and job-person matches on the DOT and
O*NET coding systems.
The Lowest MVQS Transferable Skills Percent (TSP) Level
If a job-person match has a Vocational Quotient (VQ) less than 85, that job is considered
unskilled and the MTSP 2001 TSA Transferable Skills Percent level is automatically
restricted to the 0 to 19 % unskilled TS range, depending on the number of crosswalk
code matches for that job relative to the client's past relevant work history.
In the Grimley et al, study, the verbal rating levels (from highest to lowest possible TSA
level) were attributed to the objective MTSP TSA TSP rankings, and they matched the 5point overall ranking scale on the ABVE Transferable Skills Evaluation Test instrument.
This scale was derived directly from and based specifically on the MVQS MTSP TSP
algorithm.
11
The 97 Percent TS level assumes a three percent potential error rate, even when the person takes a job with the same title as
one of the jobs in their past relevant work history. This top TS level represents the theoretical concept that while no two
jobs are ever 100 percent identical, they should approach unity. Theoretically, this allows for less than perfect TS due to
possible subtle differences, either temporally (over time), or between job demands or work duties, given the way the job may
be set up at different companies or work sites, that need to be bridged through new, typically short-term learning or
accommodation, before the worker can be expected to perform all essential job tasks, with or without, reasonable
accommodation.
Journal of Forensic Vocationology Vol. 7(1) Monograph
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The total MTSP TSA TSP TS value (Range = 0 to 46) was mathematically transformed to
produce a total percent of all available TSP TS scale points (Range = 0 to 97%). This
transformation does not exceed a total possible percent of 97. Theoretically, this top level
represents the theoretical assumption that no two job-worker situations are 100 percent
identical, but that they may approach such unity. MTSP 2001 TSP TS Levels are
transformed to a percent based scale based on 65 comparison variables.

Variable 1: Relative to VQ, only those job person matches with a VQ of 85 or
greater are considered relevant for TS analysis relative to the five-point, 0-97
Percent, MVQS 2001 TS analysis scale:
1. No Significant TS Rating (0-19 Percent TS Range). Job-person matches
with a VQ of less than 85 are assumed to fall in the unskilled work range
and automatically receive the lowest possible TS Range Rating.
2. Few, If Any, TS Rating (20-39 Percent TS Range),
3. Low TS Rating (40-59 Percent TS Range)
4. Moderate TS Rating (60-79 Percent TS Range) or
5. High TS Rating (80-97 Percent TS Range).

Variables 2-25: Relative to the 24 McDOT 2001 most vocationally significant
job-person matching variables, all 24 values in the person's evaluative data profile
must equal, or exceed, the 24 corresponding values in the job demands profile,
before any job-person matches can occur in a specific geographic job bank
database and be considered for a TS level assignment based on the degree of
similarity between job-person matches and jobs in past relevant work history.

Variables 26-28: Relative to the 9-digit McDOT DOT Code, three codes are
considered incrementally in sets:
1. The 1st McDOT Code digit, or DOT Occupational Category,
2. The 1st two McDOT Code digits, or DOT Occupational Division, and
3. The 1st three McDOT Code digits, or DOT Occupational Group.

Variables 29-31: Relative to the 5 or 6 digit OU Classification/O*Net Code, three
codes are considered incrementally in sets:
1. The 1st two digits of the OU Classification/O*Net Code,
2. The 1st four digits of the OU Classification/O*Net Code, and
3. All 5 or 6 digits of the OU Classification/O*Net Code).

Variables 32-59: The 28 O*NET TS grouping variables at the tens level, and

Variables 60-65: The 6 vocational crosswalk database codes at the ones or units
level (i.e., SIC, SOC, CEN, IND, MPSMS and Work Field/MTEWA Codes).
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Journal of Forensic Vocationology Vol. 7(1) Monograph
MTSP 2001 TSA TSP Scale: High, Moderate & Low TS Levels
MTSP 2001 TSA TSP High, Moderate & Low Level TS points accumulate as follows:
The First Digit (Tens Value)



The highest TSP levels (80 - 97%) require the target job to match the reference
job on the first 3 digits of the McDOT 2000/2001 code and all 5 or 6 of the
O*NET code.
The next TSP level (60 - 79%), the two jobs must match either the first 3 digits of
the McDOT 2000 code or all 5 or 6 digits of the O*NET code. Or, match the first
2 digits on the McDOT 2000/2001 code and the first 4 digits of the O*NET code.
To acquire the third level of TSP (40 - 59%), the jobs must match either the first 2
digits of the McDOT 2000/2001 code or the first 4 digits of the O*NET code. Or,
match the first digit of McDOT 2000/2001 code and the first 2 digits of the
O*NET code.
The Second Digit (Ones or Units Value)
The second digit (ones value) is of the Transferable Skills (TS) value for each job match
is a direct accumulation for matching on primary codes from six crosswalk code
databases. This digit is incremented for each match of the two jobs on the following
primary crosswalk codes:
 Standard Industrial Classification (SIC) Code
 Standard Occupational Classification (SOC) Code
 Census (CEN) Code
 Materials, Product, Subject Matter and Services (MPSMS) Code
 Work Field (MTEWA) Code
 Industry (IND) Code
Discussion, Conclusions and Recommendations
Research results cited in this article support very high Reliability, along with, very high
Face, Content, Construct, Predictive and Concurrent Validity for the MVQS MTSP
Transferability Skills Percent (TSP) Algorithm in the McCroskey Transferable Skills
Programs (MTSP 2000 & 2001). Professional consensus about what the construct of
transferability of skills entails and methodological consensus between experts and
software about TSA results has been found and reported in peer-reviewed research12.
Dennis and Dennis (1998) asked if any TSA software would stand the scrutiny of the
Daubert standard for admissibility of expert testimony in federal court. Collaterally they
asked if any TSA software, with its strengths and limitations, was a valuable tool for
making decisions about jobs and earning capacity in life and in courtrooms. They found
12
In 2003, the MVQS TSA Algorithm was modified to include 7 additional updated Crosswalks to the USDOL SOC Code. At
that time, MVQS TSA Levels 1 - 5 were modified to remove all range restrictions. Specifically, the 5 modified levels now
allow unskilled jobs to flow throughout the entire MVQS TSA distribution (See MVQS TSA.ppt Presentation; McCroskey,
2003). The net result: improved scientific prediction with more rungs within and across the MVQS TSA scale ladder.
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 23
MVQS 2000/2001 programs to fit the bill. If TSA software methodologies are to meet the
standard of science that is possible (given that the behavioral sciences typically lack the
inherent control of error variance found in the natural sciences) and are to be recognized
as a valid predictors of realistic job options and earning capacity for persons with or
without disabilities, more research on the validity of TSA methodologies needs to occur.
In the Grimley et al, study, 92% of the variance in TSA Expert Responses was controlled
or accounted for by the MVQS MTSP TSA TSP Algorithm. Vocational Expert consensus
has been reached about the theoretical constructs and the standardized MVQS MTSP TSP
algorithm embodied within the MTSP 2000 & 2001 TSA programs. In a nutshell, that
study empirically demonstrated that the opinions of vocational experts agree with and are
highly reflected in the parameters and outcomes embodied in the MTSP 2000/2001 TSA
TSP theory and methodology. Those findings clearly cross-validate and enhance the
credibility of clinical judgments made by vocational experts employing MTSP TSA TSP
theory and methodology in terms of assisting a Trier of Facts, an individual or other
interested parties, as they seek to make sound decisions regarding employability and
earnings capacity based on objective, reliable and accurate Transferable Skills Analyses.
Transferable skills (TS) are acquired through Specific Vocational Preparation Training
as well as On-The-Job Training during on-going successful Job Tenure. Thirty years ago,
the Minnesota Theory of Work Adjustment (MTWA) provided an excellent model for
Job-Person Matching leading to reasonable expectations of Job Satisfaction and
successful Job Tenure. It seems to have stopped there and needs to be revised, extended
and updated for the 21st Century.
The Minnesota Theory of Work Adjustment should be revised, extended and updated to
allow it to be viewed from the perspective of an on-going, reiterative, career development
process, leading from one job to the next over time during normal individual career
development, for it to adequately account for Transferable Skills Building and
Transferable Skills Analysis, and Earning Capacity Estimation. This article presents
strong support that theoretical expansion of the MTWA should be based on the content
and implications of this article, which reflect what has already been theoretically
developed, programmed, successfully demonstrated in the MVQS 2001 MTSP TSA TSP
approach to Transferable Skills Analysis and Earning Capacity Estimation. This approach
has been shown through scientific research to be leading edge technology in terms of jobperson matching, transferable skills analysis and earning capacity estimation. It has also
been shown through numerous scientific research articles to be highly reliable and highly
valid with very reasonable, relatively low error rates (See References and Bibliography).
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The MVQS13 Vocational Interest and Personality Reinforcer
(VIPR) Job-based Vocational Personality Type Indicator and
MTSP14 Jobs-Based Vocational Interest Personality Types
Crosswalk to Jung People-Based Personality Types
By
Billy J. McCroskey, Ph.D., Steven J. Hahn, M.S.,
Scott E. Streater, D.V.S., Larry L. Sinsabaugh, Ph.D.,
Lyndette L. Mayer, Ph.D., Eugene E. Van de Bittner, Ph.D.,
Janet K. Lowe, M.S. and Kenneth L. Dennis, Ph.D.
Abstract
According to Personality Theorist Carl Jung15, human motivation to act or behave in
predictable ways may be explained in terms of creative energy and classified into a
variety of Jung People-based Personality Types based on different combinations of four
dimensions of opposing personality trait continuums:
 Extravert vs. Introvert,


Sensing vs. iNtuiting,
Thinking vs. Feeling, and
Judgment vs. Perception.

Combining opposing personality dimensions by selecting one end of each of the four
continuums (E or I, and S or N, and T or F and J or P) yields 16 four-letter MVQS
Vocational Interest and Personality Reinforcer (VIPR) Job-based Personality Types16.
Each of these have a corresponding Jung People-based Personality Types (e.g., ESTJ,
ISTJ, ENTJ, INTJ, . . . ENFP, INFP). The 16 VIPR Job-based Personality Types can
be helpful in terms of describing and explaining vocational aspects of complex human
behavior. They can also be helpful in matching individuals with Specific Job Types, via
the MVQS2001 VIPR Job-based Personality Type Crosswalk, which optimally reinforce
(correspond with) their Vocational Interests, Occupational Values, Needs, and General
Jung People-based Personality Type.
© 2001 by Billy J. McCroskey, Ph.D.
All Rights Reserved.
Filename: C:\My Documents\VIPRART.doc
13
McCroskey, B. J. (2001). The McCroskey Vocational Quotient System (MVQS2001) Job-Person Matching Program. Brooklyn Park,
MN: Vocationology, Inc.
14
McCroskey, B. J. (2001). The McCroskey Transferable Skills Program (MTSP). In: The McCroskey Vocational Quotient System 2001
(MVQS2001) Job-Person Matching Program. Brooklyn Park, MN: Vocationology, Inc.
15
Jung, Carl Gustav - Swiss-born Personality Theorist (1875-1961).
16
VIPR Job Types are 16 General Job-based Personality Types. Each Job-based Personality Type corresponds with one of the 16
General Jung People-based Personality Types. In 2001, the US world of work as we know it has 12,775 Specific Job Types. Each
Specific Job Type fits best within one General VIPR Job-based Personality Type and it's correspondent General Jung People-based
Personality Type.
Page: 32
Journal of Forensic Vocationology Vol. 7(1) Monograph
Purpose of the Study
The purpose of this study was:
1) To reasonably order the 12,775 Specific Job Types described in the McDOT2001
into 16 General Vocational Interest and Personality Reinforcement (VIPR) Jobbased Personality Types. These General Job-based Personality Types would
include seemingly Independent, yet Dependent, Specific Job Types, within and
across, the 16 General VIPR Job-based Personality Types, when ordered by
Percent of Transferable Skills Valence17 across all 12,775 Specific Job Types.

Note: Retaining Specific Job Types with the highest TS Valence in each single best
General VIPR Job-based Personality Type, and eliminating all other duplicate, or
lower TS Valence Specific Job Types, within and across, the 16 General VIPR Jobbased Personality Types, brings more independent order to the world of work. By
forging a link between General Jung People-based Personality Types and their
single-best most correspondent General VIPR Job-based Personality Types, we can
better match people with jobs based on relevant Personality attributes. The 16
corresponding Job-Person Personality-based Job-Person Matching Types were
developed so Career Guidance and Counseling Professionals could provide
Personality-based career guidance and counseling to their clientele. How so? By
considering which General Jung People-based Personality Type best fits which
General VIPR Job-based Personality Type as defined by the set of Specific Job Types
nested within each VIPR Job-based Personality Type.
2) To develop an MVQS Vocational Interests & Personality (VIPR) Job-based
Personality Type Indicator (both a Paper and Pencil, and a Machine version)
which classifies all 12,775 Specific McDOT2001 Job Types into 16 General
VIPR Job-based Personality Types, corresponding with the 16 General Jung
People-based Personality Character Types18 identified in the literature.
17
Percent of Transferable Skills Valence is defined as the level of Transferable Skills (TS) between two or more Specific Job Types.
This is on an MVQS Percent Scale ranging from 0 to 97%. The higher the TS Valence between two or more Specific Job
Types, the greater the probability of skills transferability between those Job Types. Valence between two or more seemingly
independent Job Types, in any General VIPR Job-based Personality Type, is defined in terms of similarities of occupational interests,
values, needs, and other personality attributes common to People Personality Types drawn to Specific Job Types in that same General
VIPR Job-based Personality Type. Valence between two or more People in the same General Jung People-based Personality Type is
correspondingly defined in terms of similarities of occupational interests, values, needs, and other personality attributes common across
individuals in that same General Jung People-based Personality Type.
18
Source Web Site: http://www.doi.gov/octc/typescar.html
Journal of Forensic Vocationology Vol. 7(1) Monograph
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Review of Literature
The Origins of Job-Person Matching
In the early 1930s, the United States Department of Labor (US DOL) began widespread
studies of the requirements of jobs in America. These studies were initially prompted by
the need to understand job requirements to better match disabled veterans with jobs. The
goal was to reduce or eliminate the impact of vocationally handicapping conditions
(McCroskey, 1979; McCroskey & Lowe, 1986, 1987; McCroskey, Streater, Timming,
Wattenbarger & Lowe, 1989, 1991).
In 1933, the United States War Manpower Commission received congressionallyauthorized funding for job analysis research, which later produced the first edition of the
Dictionary of Occupational Titles (DOT, US DOL, 1939). That book was an attempt to
objectively describe all jobs in the United States. Eventually, the US DOT served as a
model copied by many countries around the world [(McCroskey & Lowe, 1986, 1987);
Shartle (1964)]. Subsequent editions of the DOT, Volumes II, III and IV, were researched
using objective, behaviorally-anchored Job Analysis Scales and Techniques, described,
quantified and published in 1949, 1965, and 1977, respectively.
In 1991, instead of researching and updating to a 5th edition DOT, the 1977 4th edition
DOT was simply revised and became known as the 1991 revised 4th edition DOT.
In 1998, the O*NET 98 Viewer (Version 1.0) was put out, not as an updated DOT, but as
a replacement for the DOT. It contained far too much general information and not nearly
enough specific to be of much use to Vocational Experts, Career Guidance Counselors,
Vocational Rehabilitation Counselors or Consultants, Vocational Evaluators,
Psychologists, Psychometrists, or similar professionals.
In 2000, since the USDOL had abandoned updating US DOT to a 5th edition, the
McCroskey Dictionary of Occupational Titles (McDOT) was updated to a 5th Edition
DOT by Vocationology, Inc., a private sector firm in Brooklyn Park, MN. The methods
used included a great deal of data mining of the O*NET 98 DOT replacement data along
with the data fusion necessary to rebuild the 24 most vocationally significant worker
trait profiles for the 12,775 Specific Job Types in McDOT2001.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
The Jung Connection to VIPR Type Indicators
The VIPR code was determined from Jung, C.G. (1971). Myers-Briggs resources from
Consulting Psychologist Press (CPP) were noted, but not used. We went back to the
original source (Jung, 1971). The VIPR types are numbered in order of frequency in the
McDOT. Number 1 is the most common type and number 16 is the least common type.
While a person may have a personality type (or Conceptual Type as we would describe
it), the desired VIPR type is specific to employment. VIPR does not say what type you
are. It says what type of job you prefer to have. For many people, personality type is the
same as the employment preference type. This cannot be assumed, however, for all
workers. Since the VIPR test asks the person to rate jobs on the basis of desirability, it
focuses on work preference rather than general personality. Therefore, in its
development, focus and prediction, VIPR is not related to Myers-Briggs. Carl Jung
remains the theoretical base for the 16 VIPR Type Indicators.
Journal of Forensic Vocationology Vol. 7(1) Monograph
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Evaluative Data Profiling
The 24 Most Vocationally Significant Worker Traits for
Manual and Computerized Job-Person Matching
Since the introduction of the formal Minnesota Theory of Work Adjustment in 1964, the
US DOL has collected a myriad of worker trait / job requirement element-level data, and
utilized that data to develop worker trait / job requirement traits-level data.
McCroskey (1982,1983, 1985, 1987, 1989 & 1990) and McCroskey & Lowe (1986,
1987) described the 24, most vocationally significant, traits-level worker traits, which
should be measured, or rated, and used for evaluative data profiling in McCroskey
Vocational Analysis. This 24 worker traits-level characteristics evaluative data profile
should be developed using the McPLOT TestPlot Program and then transferred to the
McCroskey Transferable Skills Program (MTSP; McCroskey, 2000) for the best, most
reliable and most well validated Job-Person Matching, Employability Determination, and
Earning Capacity Prediction Estimates. All of these worker traits have been operationally
defined on behaviorally anchored job analysis scales in the Handbook for Analyzing
Jobs-Revised (USDOL 1972, 1976, 1991). These 24, most vocationally significant,
worker trait characteristics are listed below:
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Journal of Forensic Vocationology Vol. 7(1) Monograph
The 24 Most Vocationally Significant Worker Traits For
MVQS2001 Job-Person Matching
General Educational Development Worker Traits
R - Reasoning
M - Math
L - Language
Aptitude Worker Traits
S - Spatial Perception
P - Form Perception
Q - Clerical Perception
K - Motor Coordination
F - Finger Dexterity
M - Manual Dexterity
E - Eye-Hand-Foot Coordination
C - Color Discrimination
Physical Capacity Worker Traits
PD1 - Lifting/Carrying/Pushing/Pulling/Sitting/Standing/Walking
PD2 - Climbing/Balancing
PD3 - Stooping/Bending/Crouching/Squatting/Kneeling/Crawling
PD4 - Reaching/Handling/Fingering/Feeling
PD5 - Talking/Hearing
PD6 - Seeing
Environmental Tolerance Worker Traits
EC1 - Work Location (Indoors/Both/Outdoors)
EC2 - Extreme Cold
EC3 - Extreme Heat
EC4 - Dampness/Humidity
EC5 - Noise/Vibrations
EC6 - Hazards (Mechanical/Electrical/Chemical/Heights)
EC7 - Fumes/Dusts/Mists/Gases/Odors
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Updating the Year 2000 Vocational Quotient (VQ1)
Relationship between the Old VQ versus the Year 2000 VQ1
The most comprehensive approach to bring order to the world of work and work
adjustment, in terms of understanding Overall Job Difficulty and Maximum Vocational
Potential, is the McCroskey Vocational Quotient (VQ). The Vocational Quotient was
developed from the U.S. Department of Labor (US DOL) Job Analysis behavioral anchor
ratings. There were 51 worker traits listed in the 1972 Handbook for Analyzing JobsRevised. In 1979, the 12,099 Job Titles described in the 1977 Dictionary of Occupational
Titles (DOT; USDOL, 1977) were arranged by VQ, based on those 51 worker traits
(McCroskey & Perkins, 1981), and published in the original four volumes of the
Encyclopedia of Job Requirements (McCroskey, 1979a, 1979b, 1979c, 1979d).
The 51 worker trait raw scores for each nine-digit DOT job type were added together to
produce a total or sum of scores. Some of these raw scores (N=20) had several possible
scale values. For example, Reasoning (R), Math (M), and Language (L) had scores that
ranged from one to six, and Spatial (S), Form Perception (P) and other aptitudes had
scores ranging from one to five. Other worker traits (N=31) such as Seeing (Physical
Demand #6) had only two possible values at the worker-trait level: a significant "1" or
not significant "0" job requirement. The original Vocational Quotient (VQ) distribution
had a mean of 57.1999 and a standard deviation of 14.4558 points.
The raw scores were divided into two groups based on whether they had three or more
possible values (scalar; N=20), or two possible values (dichotomous; N=31). After
recoding all worker trait profile values to place them on ascending scales, a multiple
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Journal of Forensic Vocationology Vol. 7(1) Monograph
regression analysis was completed on the scalar variables to predict the Total Raw Score
VQ (TRS-VQ). A second multiple regression was then completed using only the
dichotomous variables to predict the TRS-VQ. The relevant acquired regression weights
for each worker trait profile were multiplied by their corresponding worker trait profile
values and summed to produce the Scalar Variables Vocational Quotient (SVVQ). The
relevant acquired regression weights for each worker trait profile were also multiplied by
their corresponding worker trait profile values and summed to produce Dichotomous
Variables Vocational Quotient (DVVQ) estimates of overall job difficulty, as measured by
the TRS-VQ criterion, for each job described in the 1977, 4th ed. DOT.
The final SVVQ (Rxy=0.99+ with TRS-VQ) distribution had a mean of 57.1998 and a
standard deviation of 14.3741 points. The final DVVQ (Rxy=0.92+ with TRS-VQ)
distribution had a mean of 57.2299 and a standard deviation of 13.3208 points. When the
final sets of three Vocational Quotients (VQs) were printed to five decimal points, each
unique worker trait / job requirements profile pattern was found to be associated with a
unique and empirically precise set of VQs (McCroskey & Perkins, 1981).
Since 1979, all worker trait profiles and their VQs published in the original four volume
edition of the Encyclopedia of Job Requirements (McCroskey, 1979a, 1979b, 1979c,
1979d) have been updated several times. They are now electronically incorporated in the
McCroskey Dictionary of Occupational Titles (McDOT) and related McPLOT and MTSP
programs. The final product of the first regression analysis (the SVVQ) was ultimately
selected as the more robust and more precise estimate of the overall TRS-VQ. The SVVQ
was therefore selected as the final, single-best, most representative, Vocational Quotient
(VQ) estimate of overall job difficulty for each job.
In 1992, the VQ distribution was updated, recalculated, and transformed to produce a
distribution with a mean of 100 and a standard deviation of 15 to provide consistency
with the WAIS IQ distribution. People have IQs, Jobs have VQs. Put another way, VQ
can be thought of as an IQ for work. When the final transformed VQ was printed to five
decimal points, each unique job requirement pattern was found to be associated with a
unique and empirically precise VQ.
In 1995, 2,408 internal DOT inconsistencies identified through research were corrected.
This effected 1,913 of the 12,741 worker trait profiles for jobs described in the 1991
DOT. The VQ was re-calculated to adjust for these profile changes. Again, as expected,
when the final VQ was printed to five decimal points, each unique job requirement
pattern was again found to be associated with a unique and empirically precise VQ.
In 2000, the 12,775 worker trait profiles for jobs described in the McDOT2000 were
recalculated based on data fusion of 75 selected O*NET 98 worker trait elements with the
24, most vocationally significant, McDOT 8.0R worker traits to reconstitute the McDOT
2000 5th edition DOT - Extended Dataset Edition. Both SVP and VQ aggregate variables
were then re-calculated to adjust for the new, updated, Year 2000 worker trait level
profile changes. In these re-calculations, only the 24, most vocationally significant
(regardless of their scalar or dichotomous nature), worker trait values were used to
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 39
determine the final SVP1 and VQ1 for each job. Again, as expected, when the final VQ1
was printed to five decimal places, each unique job requirement profile pattern was again
found to be associated with a unique and empirically precise Vocational Quotient VQ1.
VQ (or VQ1), represents the Overall Job Difficulty level of Adaptive or Accommodative
Behavior (in terms of Satisfactoriness and Satisfaction) required for people to accomplish
meaningful Work Adjustment and develop Tenure for each of the 12,775 jobs described in
the McCroskey Dictionary of Occupational Titles (McDOT2001; McCroskey, 2001).
Many studies have reviewed the use of the Vocational Quotient based on both the
DOT and the O*NET. These studies included McCroskey & Lowe (1986, 1987),
McCroskey (1991, 1992), McCroskey & Hahn (1995, 1997, 1998), (McCroskey,
Hahn, Dennis & Streater (1995), McCroskey, Bohlke & Streater (1995), Hahn
(1997), Dennis & Dennis (1998), McCroskey, Dennis & Dennis(1998), Hahn &
Wells-Moran (1998), Dennis & Tichauer, (1998), Dennis & McCroskey (1999),
McCroskey & Dennis (1999), Mayer (1998), and Dennis & McCroskey, (2000).
The Vocational Quotient (VQ) has been shown repeatedly to be a valid predictor
of average starting and overall wages in California, Florida, Louisiana, Michigan,
Minnesota, Idaho, Indiana, Iowa, Nebraska, North Carolina Virginia, Wisconsin,
Washington, and other states. The Vocational Quotient (VQ) has been shown to
be reliable and valid within and across independent cross-validation replication
studies, longitudinal time frames (15+ years), and numerous geographic locations.
VQ has been repeatedly studied as a predictor of starting wages offered on Job Service
Work Order Openings, overall average wages across all workers in the US, and average
starting wages achieved by randomly selected rehabilitation clients at time of Status 26
closure in Indiana. VQ has been found to be highly predictive of average starting wages
[(Rxy=0.91; SEE=$0.50/hr; McCroskey & Hahn (1998)], overall average wages across
all workers in the United States [(Rxy=0.99+; SEE=$0.01/hr; McCroskey & Hahn
(1998)], and average post-rehabilitation services starting wages of clients at time of
Status 26 closure in Indiana [Mayer (1998, Rxy=0.68; SEE=$1.25/hr) and Dennis &
McCroskey (2000, independent replication study Rxy=0.83; R2=0.70; SEE=$1.12/hr)].
Dennis & McCroskey (2000) independently replicated, expanded and updated
Mayer’s (1998) Indiana labor market wage research, which was an update of her
original study19 of 132 randomly selected people that received State of Indiana
Division of Disability, Aging, and Rehabilitation services and were placed in jobs
in 1993 (Mayer, 1995). All clients in these studies met the criteria of being
successfully rehabilitated. Mayer's 1998 MTSP 7.11R follow-up of her original
study was replicated and expanded using MTSP 8.0R Program Earning Capacity
Estimates.
19
The authors wish to thank Dr. Lindette Mayer for providing raw data and permission to replicate her 1998 study.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Mayer (1998) found a gain of about $1.00 above the predicted average return-towork wage for Indiana Rehabilitation clients based on MTSP 7.11R Program
Earning Capacity Estimates. That gain was due in part to a small group of people
with exceptionally high incomes. When those Outliers were removed in the
Dennis & McCroskey (2000) follow-up study (Rxy=0.83; R2=0.70; SEE=$1.12 per
hour), average return-to-work wage for Indiana Rehabilitation clients was about
$1.00 below what had been predicted.
McCroskey & Dennis (2000), in an expansion of Mayer's 1998 study, included an
analysis of Temperaments. The expanded study of Indiana Job Services openings
and starting wages data from March, 1995 through February, 1996 compared
income predictions when Temperaments or Personality variables were added to
the MTSP VQ-Wage prediction formula. In the expanded study, Temperaments or
Personality variables did not improve the VQ-Wage prediction sufficiently to
overcome the increased variance inherent in these measures.
Building on previous research which assessed the validity of Vocational Quotient (VQ1)
as a predictor of criterion referenced Job Service work order wages to be very positive,
McCroskey, Hahn & Dennis (2000) established a new, expanded criterion-reference point
distribution for MVQS2001 earning capacity estimation: six-point earning capacity
prediction estimates. In their study20, McCroskey, Hahn & Dennis (2000) evaluated the
ability of the VQ1 to predict income reported for Occupational Employment Statistics
(OES) job groups. Linear regression was used to predict reported income at the Mean,
10th, 25th, 50th, 75th and 90th percentile of the OES-Wage distributions. When VQ1 was
used to predict to the middle of these six criterion-referenced distributions, it was found
to be a very accurate predictor of each of these OES-Wage distributions reported by the
US Department of Labor. Predictive Validity (Rxy) Coefficients were found to be 0.970,
0.973, 0.975, 0.974, 0.972 and 0.966, respectively. Standard Errors of Estimate (SEE)
were found to be $1.19, $0.45, $0.69, $1.08, $1.65 and $2.66 per hour, respectively.
McCroskey, Hahn & Dennis (2000) recommended the expanded 6-point VQ1-OES Wage
Algorithm, based on specific McDOT-VQ121 OES Wage Prediction, be used in the
MVQS2001 Program to expand the range of predicted earning capacity estimates,
increase overall reliability of predicted earning capacity estimates and reduce
aggregate SEEs associated with prediction estimates. Their recommendations were
peer-reviewed, found to be empirically sound, and subsequently implemented in the
MVQS2001 Program (McCroskey, 2001).
20
McCroskey, B. J., Hahn, S. J. & Dennis. (2000). MTSP VQ1-OES Aggregate & VQ1-McDOT Specific Wage Estimation.
The Journal of Forensic Vocationology, Vol. 6(1), pp. 107-134.
21
McCroskey Dictionary of Occupational Titles (McDOT; McCroskey, 2001) 5th Edition DOT: VQ-VQ1 Replacement Data.
Journal of Forensic Vocationology Vol. 7(1) Monograph
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Informal vs. Formal Job-Person Matching Theory
In his book entitled Choosing a Vocation, Frank Parsons22 (1909), known by many as the
father of vocational guidance, postulated three primary requirements for effective
vocational guidance:
1. A knowledge of the requirements and conditions for success in different lines
of work, as well as related advantages and disadvantages, compensation,
opportunities and prospects (knowledge of the world of work);
2. A clear understanding of the aptitudes, interests, ambitions, resources and
limitations of the individual (self-knowledge and insight); and,
3. Systematic techniques for integrating these two sources of information in the
vocational decision-making process (bringing the first two conditions
together).
Parsons’ informal, yet profound, three-part theory for vocational guidance continues to
guide the efforts of many theoreticians, researchers and clinical practitioners working to
develop and refine the methodologies, techniques and tools necessary to provide better
vocational counseling.
The Minnesota Theory of Work Adjustment (Dawis, England and Lofquist, 1964)
provided formal (testable) foundational underpinning for all worker trait factor job
person matching systems which later emerged. Many of these had their beginning in the
late 1970s with the development of the Vocational Diagnosis and Assessment of Residual
Employability [VDARE; (McCroskey, Wattenbarger, Field & Sink, 1977)], and
continuing through the 1980s and 90s. These worker-trait-factor job-person matching
TSA systems were all developed and computerized based on data describing job
requirements in terms of the objectively defined behaviorally- anchored rating scales
found in the Handbook for Analyzing Jobs-Revised (HAJ-R; USDOL, 1972; Reprinted,
1976; Re-revised 1991).
From 1900 to 1976, more than 20,000 mental and physical tests, covering a multitude of
worker traits, had been developed and utilized in an effort to better understand individual
differences in terms of basic human capacities and tolerances (Buros, eds. 1-8, 19381978). While many of these tests have been used as lone predictors of employability,
22
Parsons, F. (1909). Choosing a Vocation. (1st ed.); Boston: Houghton-Mifflin.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
research clearly supports the administration of a battery of vocationally relevant tests
used in combination for better prediction of the multifaceted criteria known as individual
employability (Anastasi, 1958, Anastasi, 1976, Bolton, 1976).
Flexible test batteries designed to allow for the systematic measurement of vocationally
significant worker traits, remain a priority. Continuing efforts should focus on
developing, refining and updating Ability and Work Context instruments and measures of
Worker Traits/Job Requirements primarily in these four, vocationally-significant Worker
Trait Factor areas:
1) General Educational Development, (3 worker traits)
2) Vocational Aptitudes, (8 worker traits)
3) Physical Capacities (6 worker traits, and
4) Environmental Tolerances (7 worker traits).
It is important that such measures be standardized with well-defined behavioral anchors,
which are reliable, valid and interpreted in terms of relevant job requirements relative to
general adult worker norms. Well-developed tests and measures with reasonable
approximations of general adult worker norms can subsequently be combined into a test
battery with their results combined and effectively utilized to accomplish Parsons’ (1909)
third recommendation for matching people with jobs (McCroskey, Streater, Timming,
Wattenbarger & Lowe, 1989; 1991).
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The Minnesota Theory of Work Adjustment
While informal Job-Person Matching Theory dates back to Parsons (1909), the
Minnesota Theory of Work Adjustment (Dawis, England and Lofquist, 1964) provided the
first formal foundational underpinning for all worker-trait-factor job person matching
systems. These later emerged, beginning in the late 1970s with the development of the
Vocational Diagnosis and Assessment of Residual Employability [VDARE; (McCroskey,
Wattenbarger, Field & Sink, 1977)], and continuing through the 1980s and 90s. These
worker-trait-factor job-person matching TSA systems were all developed and
computerized based on data describing job requirements in terms of the objectively
defined behaviorally-anchored rating scales found in the Handbook for Analyzing JobsRevised (HAJ-R; USDOL, 1972; Reprinted, 1976; Re-revised 1991).
Formal Propositions and Corollaries of the Minnesota Theory of Work Adjustment
The following was excerpted from The Minnesota Theory of Work Adjustment by Rene
V. Dawis (Bolton, 1976).
The following propositions, designed by the authors [(Dawis, Lofquist & Weiss
(1968), pp. 9-11)] to serve as a basis for research, state the Theory of Work
Adjustment more formally:
Proposition I. An individual's work adjustment at any point in time is
indicated by his concurrent levels of satisfactoriness and satisfaction.
Proposition II. Satisfactoriness is a function of the correspondence
between an individual's abilities and the ability requirements of the work
environment, provided that the individuals needs correspond with the
reinforcer system of the work environment.
Corollary IIa. Knowledge of an individual's abilities and of his
satisfactoriness permits the determination of the effective ability
requirements of the work environment.
Corollary IIb. Knowledge of the ability requirements of the work
environment and of an individual's satisfactoriness permits the
inference of an individual's abilities.
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Proposition III: Satisfaction is a function of the correspondence between
the reinforcer system of the work environment and the individual's needs,
provided that the individual's abilities correspond with the ability
requirements of the work environment.
Corollary IIIa. Knowledge of an individual's needs and of his
satisfaction permits the determination of the effective reinforcer
system of the work environment for the individual.
Corollary IIIb. Knowledge of the reinforcer system of the work
environment and of an individual's satisfaction permits the
inference of an individual's needs.
Proposition IV. Satisfaction moderates the functional relationship
between satisfactoriness and ability-requirement correspondence.
Proposition V. Satisfactoriness moderates the functional relationship
between satisfaction and need-reinforcer correspondence.
Proposition VI. The probability of an individual being forced out of the
work environment is inversely related to his satisfactoriness.
Proposition VII. The probability of an individual voluntarily leaving the
work environment is inversely related to his satisfaction.
Combining Propositions VI and VII, we have:
Proposition VIII. Tenure is a joint function of satisfactoriness and
satisfaction.
Given Propositions II, III, and VIII, this corollary follows:
Corollary VIIIa. Tenure is a function of ability-requirement and
need-reinforcer correspondence.
Proposition IX. Work personality-work environment correspondence
increases as a function of tenure. (pp. 234-235).
Basic Concepts of the Minnesota Theory of Work Adjustment
In an effort to simplify and further explain The Minnesota Theory of Work Adjustment,
Dawis (In: Bolton, 1976), said:
Speaking at a simple level, a theory is an account of what is happening or what
has happened. The Theory of Work Adjustment, then, is an account of what is
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 45
happening or what has taken place in work adjustment. As an account, the theory
is itself, quite simple.
Tenure, Satisfaction, and Satisfactoriness
When a person goes to work, one of the first objective observations that can be
made is that he/she continues on the job for a certain length of time. Tenure,
length of time on a job, is a basic concept of the Theory of Work Adjustment.
Tenure implies a minimal level of work adjustment in terms of correspondence
between Satisfactoriness and Satisfaction. If an employee's work adjustment were
to drop below this level, then it is presumed that he/she would be let go (fired)
from, or would otherwise leave (quit), the job.
...
Tenure, satisfaction, and satisfactoriness, then, are the basic outcomes, or
dependent variables, of work adjustment. To the extent that work adjustment has
taken place, tenure, satisfaction, and satisfactoriness would be manifested to some
commensurate extent. That is, they are indicators of work adjustment. These
indicators point to the basic factors involved in work adjustment. Satisfaction
suggests factors on the individual side, while satisfactoriness suggests factors on
the work side (viewing work adjustment as what happens when a person goes to
work)...." (pp. 229-230).
Concepts Linked to Measures Under the
Minnesota Theory of Work Adjustment
The Minnesota Theory of Work Adjustment provided formal foundational underpinning
for all worker-trait-factor job person matching systems which later emerged.
Worker Trait Factors, Worker Traits and Worker Trait Elements are operationalized as
Worker Characteristic/Job Requirement component elements on the Job Satisfactoriness,
Abilities, Ability Requirements and Occupational Aptitude Pattern (OAP) side of the
Minnesota Theory of Work Adjustment equation.
Worker Interests, Temperaments, Attitudes, Satisfaction, Needs, Values and Occupational
Reinforcer Patterns (ORP) are operationalized as Worker Characteristic/Job
Requirement component elements on the Job Satisfaction, Worker Needs and Work
Reinforcer Systems side of the Minnesota Theory of Work Adjustment equation.
Perhaps Dawis (Ch. 13, In: Bolton, 1976, pp. 227-248) said it best:
A formal test of a theory requires that the theory's concepts be operationalized,
i.e., stated in terms precisely and specifically describing the operations by which
observations are to be made in order to confirm or to disconfirm the theory or any
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Journal of Forensic Vocationology Vol. 7(1) Monograph
part of it. This requirement is usually fulfilled through the use of instruments in
data collection. (p. 235).
For the Theory of Work Adjustment, six instruments would be needed to make the
requisite observations, measures of the following six concepts (p. 234-240):
Concepts
1) Satisfactoriness,
Instruments/Measures*
Minnesota Satisfactoriness Scales (MSS)
2) Satisfaction,
Minnesota Satisfaction Questionnaire (MSQ)
3) Abilities,
General Aptitude Test Battery (GATB)
Related Extensions
*Maximum Least Demonstrated Worker Traits
Functioning across Successfully Demonstrated
Work History, extracted using the Vocational
Diagnosis and Assessment of Residual
Employability (VDARE) Process which was
based on Worker Traits/Job Requirements
Profiles rated on HAJ behaviorally anchored
Job Analysis Scales identified in the Realistic
Occupational Counseling (ROC) Handbook, or
the Encyclopedia of Job Requirements (EOJR).
*Many Aptitude, Achievement, & Ability test
results crunched with the McPLOT Program.
*Physical Capacities, Environmental Tolerances
rated on Handbook for Analyzing Jobs (HAJ)
behaviorally anchored Job Analysis Scales.
4) Ability Requirements, Occupational Aptitude Patterns (OAPs)
Related Extensions
*Worker Traits/Job Requirements Profiles
rated on HAJ behaviorally anchored Job
Analysis Scales identified using the Realistic
Occupational Counseling (ROC) Handbook, or
the Encyclopedia of Job Requirements (EOJR).
5) Needs, and
Related Extensions
MN Importance Questionnaire (MIQ)
MVQS Occupational Values & Needs Inventory
6) Reinforcer Systems
MN Job Description Questionnaire (MJDQ)
~Occupational Reinforcer Patterns (ORPs)
Related Extensions
MVQS Vocational Interest & Personality
Reinforcer (VIPR) Type Indicator
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Measures of satisfactoriness and satisfaction would be the outcome or criterion
measures. (Tenure is an outcome variable, too, but this can be observed without
the need for instrumentation.)
Measures of abilities and needs would be required to describe the person, while
measures of ability requirements and reinforcer systems would be needed to
describe the work environment.
To enable the measurement of correspondence, one approach would be to develop
parallel measures of people and work environments; that is, measures of abilities
and ability requirements should utilize the same set of ability dimensions, and
likewise, measures of needs and reinforcers should utilize the same set of
reinforcement dimensions. This approach was followed in the Work Adjustment
Project. . . . (pp. 234~240).
* [Selected emphases and Instruments/Measures added].
Original Extensions of the Minnesota Theory of Work Adjustment
Work adjustment (i.e., the achieving and maintaining of individual- environmental
correspondence) is an interactive process. Work adjustment mechanisms involving
Correspondence, Discorrespondence, Flexibility, Activeness, Reactiveness, and Rate of
Work Adjustment, on the part of both the individual and the work environment, were
early extensions of the theory, which were present, operationally definable, and
observable, relative to testable hypotheses regarding their impact on work adjustment
over time.
The Realistic Occupational Counseling (ROC) Handbook (Wattenbarger & McCroskey,
1978) was the first private sector supplement to the 1965 Dictionary of Occupational
Titles. It provided the original modal worker trait factor profiles used for work history
analysis and post-injury residual employability job-person matching using the VDARE
Process. The 114 modal worker trait job requirement profiles in the ROC Handbook
provided the original database for the Realistic Occupational Counseling Computerized
Job-Person Matching Transferable Skills Analysis (TSA) Program. The ROC TSA
Program was the first worker trait factor job-person-matching program developed for use
on mainframe computers at the University of Georgia (Wattenbarger & McCroskey,
1978).
Other Extensions of the Theory
Research on other Minnesota Theory of Work Adjustment Extensions (e.g., concepts,
constructs, link relatives, occupational values and needs, vocational interests and
personality reinforcer type indicators) impacting on our understanding of work
adjustment have been operationally defined, studied and found supportive of Propositions
identified in the Minnesota Theory of Work Adjustment.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Other Minnesota Theory of Work Adjustment Extensions include, but are not limited to,
Differential:
1) VDARE Residual Employability Profiling (McCroskey, Wattenbarger, Field &
Sink, 1977.
2) Vocational Potential Profiling in SSA Disability Determination (Wattenbarger,
1981).
3) The Vocational Quotient (VQ) as a Differential Measure of Overall Job Difficulty
and Maximum Vocational Potential (McCroskey & Perkins, 1981).
4) Job Value (McCroskey & Lowe, 1986, 1987).
5) Test Validity (McCroskey & Perkins, 1981; McCroskey, Streater, Timming,
Wattenbarger & Lowe, 1989; 1991).
6) Job Service Work Orders Starting Wage Prediction (McCroskey & Lowe, 1987).
7) Rehabilitation Clients Return-to-Work Wage Prediction (Mayer, 1995).
8) Overall Average and Typical Starting Wage Prediction (McCroskey, 1992)
9) Earning Capacity Link Relatives (ECLRs) to Enhance Pre-Injury Earning
Capacity Prediction at the Local Labor Market Level (McCroskey, 1992, 1997,
1998, 2000).
10) Earning Capacity Link Relatives (ECLRs) to Enhance Post-Injury Earning
Capacity Prediction at the Local Labor Market Level. (McCroskey, 1992).
11) Six point (Mean, 10th, 25th, 50th [Median], 75th and 90th Percentile) wage earning
capacity criterion-referenced relative to National Occupational Employment
Statistics (OES) prediction estimates (McCroskey, Hahn & Dennis, 2001).
12) The Occupational Values and Needs Inventory (McCroskey, 2001 - modeled
after the original Minnesota Importance Questionnaire and criterion-referenced
relative to specific 9-digit McDOT 5th Edition DOT Job Types).
13) The Vocational Interest and Personality Reinforcer (VIPR) Job Type Indicator
(McCroskey, 2001 - modeled after Jung-based People Personality Types and
criterion-referenced relative to specific 9-digit McDOT 5th Edition DOT Job
Types, cross-walked from corresponding Jung-based People Personality Types).
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 49
From Manual to Computerized Job-Person Matching
Job-Person Matching Systems initially began with manual matching systems.
Botterbusch (1986) informed us that efforts to develop job-person matching systems as
we know them today, actually began in the mid-1950s when Job Service personnel
developed several manual systems for matching General Aptitude Test Battery (GATB)
test results with jobs.
By the late 1970s, all three of Parsons (1909) informal tenets had been achieved in much
detail with the arrival of the Vocational Diagnosis and Assessment of Residual
Employability (VDARE) Process (McCroskey, Wattenbarger, Field & Sink, 1977).
The VDARE Process was based squarely on Proposition II and Corollary IIb with
reliance on Proposition III and Corollary IIIb of the Minnesota Theory of Work
Adjustment (Dawis, England and Lofquist, 1964; Formally restated and expounded on
through supporting research by Dawis, R.V., 1976, In: Bolton, 1976).
VDARE became an effective tool in the hands of vocational experts around the country.
This was especially true for Expert Witness testimony in Social Security Disability
Claims, where reference to previously demonstrated work history, residual functioning,
and transferable skills were major considerations [Botterbusch (1986)].
In 1978, the zeitgeist was ready for improvement and better utilization of existing jobperson-matching systems through the much more efficient use of computers (McCroskey,
Streater, Timming, Wattenbarger & Lowe, 1989; 1991). In 1978, the ROC TSA became
the first mainframe computerized job person matching system. It was developed at the
University of Georgia (Wattenbarger & McCroskey, 1978). It was used primarily as a
tool for reliable vocational expert analysis of Social Security Disability Insurance (SSDI)
applicant appeal cases. Primary considerations for these analyses included age, education,
past relevant work history and work restrictions stemming from medical and/or
psychological disabilities.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
With the advent of personal computers in the early 1980s, the Datamaster Transferable
Skills Analysis (TSA) Program (McCroskey, 1982) was developed, with many revisions
and updates to follow (McCroskey, 1982,1983, 1985, 1987, 1989 & 1990). It was the first
micro-computerized worker-trait-factor job-person matching TSA system designed
specifically for use on Personal Computers. Others soon followed.
Comparing Computerized Job-Person Matching Systems
Botterbusch (1983) identified, described and compared eight computerized worker-traitfactor job-person-matching systems. In his update, Botterbusch (1986) identified,
described and compared 15 such programs. Brown, McDaniel, Couch and McClanahan
(1994) expanded on the earlier works of Botterbusch in their publication entitled:
Vocational Evaluation Systems and Software: A Consumer’s Guide.
Dennis & Dennis (1998) in their article, Job Search Software under Daubert, informed us
that:
In the 1993 Daubert decision, the United States Supreme Court established
scientific knowledge as the standard for admissibility for expert testimony
(Feldbaum & McCroskey, 1995, Feldbaum, 1997). This standard can be
anticipated to have a significant impact on psychological, rehabilitation,
vocational and economic experts. One general expectation is that the
instruments used to assess disabilities and predict their consequence will
need to be reliable (provide consistent results), valid (measure what it is
expected to measure) and accurately predict outcomes with reasonable
certainty and known acceptable error rates (accuracy of predictions).
The developers of Job Search Software listed by Brown, McDaniel, Couch and
McClanahan (1994) were interviewed by phone to determine the scientific attributes of
their software. Where available, relevant research to their products was reviewed as well.
The responses of the vendors to the possibility of Daubert restricting the use of their
software were varied. All the respondents were aware of the 1993 Daubert decision. Only
two programs were found to have any research regarding reliability, validity and error
rate issues addressed in the Daubert decision. Some expected there to be major upheavals
in the future. Others took a more conservative or wait-and-see attitude.
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 51
Dennis & Dennis (1998) found one program, the McCroskey Vocational Quotient System
Transferable Skills Analysis Program (MVQS MTSP) to have 50+ validity research
publications, since 1986 to date and continuing. MTSP was underpinned with on-going
scientific research designed to address vitally important issues. These included
Reliability, Predictive Validity, and Standard Error of Estimate rates, identified by the US
Supreme Court in the Daubert decision, as being the key criteria to be used by judges in
their roles as gatekeepers for determining admissibility of expert witness testimony.
Clearly, many decades of patience, research and development have begun to produce a
fruitful realization of Parson’s intuitive direction. Efforts must continue with the
collection, analysis and synthesis of on-going research, into renewed development of
vocational theory and practice. Theories must be refined through research. New tools for
more efficiently and effectively matching workers with jobs must continue to be
developed and updated. New hypotheses must be empirically tested through research on
those tools. Results of those studies must be published in peer-reviewed journals to keep
our peers abreast of the research evidence if we are to continue understanding, refining
and providing evidence of the reliability and validity of our theories to the courts and
other interested parties.
The O*NET 98 Transferable Skills (TS) Paradigm
The 1998 USDOLETA-O*NET 98 Transferability of Skills Paradigm in the MVQS
MTSP 1998/2000/2001 Transferable Skills Analysis (TSA) Super Sort?
In 1998, 5- or 6-character Occupational Unit Classification (OUC) Coded Transferable
Skills Groups (often referred to as O*NET 98 TS Code Groups) were developed for
O*NET 98. The O*NET 98/OUC TS Code Groups were derived from USDOL
Occupational Employment Statistic (OES) Codes (which are identified by the first 5
digits, of the 5-digit or 6-character OUC Codes, found in Section 7, Part 2, of the MTSP
8.0R and MTSP 2000/2001 TSA Program Job Profile Reports).
OES Codes were empirically studied using Cluster Analysis for purposes of establishing
O*NET 98 Transferable Skill (TS) Groups. Outliers (jobs which didn't belong) were
statistically identified using Euclidean Distance Measures coupled with Ward's Minimum
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Variance Method (Ward, 1963) and reclassified into OES groups or subgroups, or
reassigned to other groups or subgroups, as necessary to assure:
1) Belongingness (where the work activities of each 9-digit DOT coded occupation had
to match the definition of the occupational category under which it was grouped),
2) Homogeneity (where differences within a single category had to be less than
differences between categories and all 9-digit DOT coded occupations within a
single category had to be less than differences between categories and all the 9-digit
DOT coded occupations within a single category had to show consistency of skill
transferability),
To accomplish Belongingness and Homogeneity, the three lone variables (MPSMS,
METWA and SVP), previously used by the United States Department of Labor
Employment and Training Administration (USDOLETA) to conceptually define their
Old Transferability Of Skills Paradigm, were, in 1998, operationally redefined with
28 Occupational Classification Codes, Worker Traits, Temperaments and
Aggregate variables in their New Expanded Transferability Of Skills Paradigm.
O*NET 98 Transferable Skills Groups
As a result of the Cluster Analyses which were completed and finalized for the new
O*NET 98 Transferable Skills Groupings, 1,172 Occupational Unit Classification
(OUC) Groups, or, as they have become better known, O*NET 98 Code Transferable
Skills (TS) Groups, were created.
O*NET Code TS Groups were based on USDOL Occupational Employment Statistics
(OES) Code Groups, which are identified by the first 5 digits, of each 5-digit or 6character O*NET 98 Code TS Group. Thus, OUC/O*NET Code TS Groups are highly
refined, empirically derived Transferable Skills Subgroups, within the OES Code Group
Classification Structure.
Of the 1,172 identified O*NET 98 Code TS Groups, Means Data Profiles for 1,122 were
reported in the US DOLETA O*NET 98 Version 1.0 Program. Researchers at
Vocationology, Inc. constructed Means Data Profiles for the remaining 50 O*NET 98
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 53
Code Groups (not reported in the US DOLETA O*NET 98 Version 1.0 Program), and
added two new groups (each containing only 1 job), bringing the total N to 1,174 groups.
Vocationology researchers also reconstituted 12 jobs which were reclassified with
different 9-digit DOT Codes by O*NET 98 researchers.

In the McDOT2001 program, there are 12,775 specific unduplicated 9-digit DOTCoded Jobs and 12,811, 9-digit DOT-Coded Jobs contained in the McDOT 2000
Crosswalk when duplicated jobs are included. In McDOT2001 each job has a
specific worker traits/job requirements profile with respect to the 24 vocationally
significant worker traits and 3 aggregate variables (VQ, SVP and ZONE).

In the O*NET 98 Version 1.0 Viewer Program, there were 12,761, 9-digit DOTCoded Jobs contained in the O*NET 98 DOT Crosswalk of unduplicated jobs
(1,124 of which means data element profiles are not viewable in the O*NET 98
Viewer) and 12,797, 9-digit DOT-Coded Jobs contained in the O*NET 98 DOT
Crosswalk, when duplicated 9-digit DOT-Coded Jobs are included.
A total of 1.3 percent of the OUC/O*NET Code TS groups (N=15) constitute 38 percent
(N=4,889) of the 12,811 (counting all duplications), 9-digit DOT coded jobs. Such a
large collection of jobs within so few OU Code groups drastically limits how precise you
can be about specific jobs, job tasks, or work sites. The O*NET 98 Occupational Unit
(OU) Code by DOT job count distribution is a grossly skewed distribution, which clearly
requires job-person matching be accomplished at the job specific worker traits/job
requirements profiles level, if we are to avoid overstating transferable skills for any given
client.
The type of downsizing accomplished in the development of O*NET 98 (Version 1.0)
Program by failing to reanalyze specific 9-digit DOT-Coded jobs was neither
theoretically nor practically sound. Analyzing O*NET TS groups of jobs and reporting
group means data only, may have been cost-efficient for O*NET, but their failure to
collect and deliver specific job analysis data has not been good for vocational experts and
related vocational professionals, who need job specific, not grouped means, data. While
their development of O*NET TS Groups deserves a great deal of credit, they should have
stuck with the original plan and developed a 5th edition DOT versus trying to replace it.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Figure 1: Counts of 9-Digit DOT Codes w/Duplicates (N=12,797)
Across All 1,172 O*NET 98 Occupational Units (OUs) Including the 50
All Other OUs
800
9-Digit DOT Codes at each O*NET OU Code Group
700
600
500
DOTCOUNT
400
300
200
100
81
260
32
5
14
3
4
1
1
1
1
1
1
1
1
1
1
1
1
1
0
Number of O*NET 98 OU Code Groups (N=1,172) at each DOT Count
MVQS2001 MTSP Transferable Skills Analysis (TSA) Valence Levels
The 1998 US DOLETA-O*NET 98 Transferability of Skills Paradigm was expanded to
include Percent of Transferability (Valence) and incorporated in the MTSP 8.0R
Program on 10/15/98. It was included in the MTSP 2000 Transferable Skills Analysis
(TSA) Super Sort on 01/01/2000. Following the very high validity findings identified in
the Grimley, Williams, Hahn & Dennis (2000) validation study23, it was included as a
part of the MVQS2001 MTSP Transferable Skills Analysis (TSA) Super Sort on
01/01/2001.
Percent of Transferable Skills Valence
80 - 97%
60 - 79%
40 - 59%
20 - 39%
00 - 19%
23
Level of Transferable Skills
5 - High Percentage of Skills
4 - Moderate Percentage of Skills
3 - Low Percentage of Skills
2 - Skills Required Not Available
1 - Skills Not Required
Grimley, Williams, Hahn & Dennis. (2000). Scientific Prediction of Transferable Skills. Journal of
Forensic Vocationology Vol. (6)1, pp. 7-16. Simultaneously printed for broader distribution as: A Scientific
Approach to Transferable Skills. (2000). Journal of Forensic Vocational Analysis. Vol. 3(1), pp. 47-54.
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 55
The Expanded MVQS2001 MTSP Transferable Skills (TS) Paradigm
The Expanded MVQS2001 Transferability of Skills Paradigm includes consideration
of the same 28 codes and scales used for developing the 1,172 O*NET TS Groups:
1) Primary Materials, Products, Subject Matter and Services (MPSMS) code,
2) Secondary Materials, Products, Subject Matter and Services (MPSMS) code,
3) Tertiary Materials, Products, Subject Matter and Services (MPSMS) code,
4) Primary Work Field: Machines, Tools, Equipment and Work Aid (MTEWA) code,
5) Secondary Work Field: Machines, Tools, Equipment & Work Aid (MTEWA) code,
6) Tertiary Work Field: Machines, Tools, Equipment and Work Aids (MTEWA) code,
7) Specific Vocational Preparation (SVP),
8) (D)ata Complexity,
9) (P)eople Complexity,
10) (T)hings Complexity,
11) General Educational Development: (R)easoning,
12) General Educational Development: (M)ath,
13) General Educational Development: (L)anguage,
14) (G)eneral Learning Ability Aptitude,
15) (V)erbal Aptitude,
16) (N)umerical Aptitude,
17) (S)patial Perception Aptitude,
18) (P) Form Perception Aptitude,
19) (Q) Clerical Perception Aptitude,
20) (K) Motor Coordination Aptitude,
21) (F)inger Dexterity Aptitude,
22) (M)anual Dexterity Aptitude,
23) (E)ye-Hand-Foot Coordination Aptitude
24) (C)olor Discrimination Aptitude
25) (D)irecting Temperament,
26) (P)eople Temperament,
27) (I)nfluencing Temperament, and
28) (E)xpressing Temperament.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Theory, Reliability, Predictive Validity and
Error Rates Associated with the VDARE
Process and MVQS Vocational Analysis
Theoretical Underpinning: VDARE Vocational Analysis
Under the VDARE Vocational Analysis Process, Proposition I of the Minnesota Theory
of Work Adjustment is assumed to be true, therefore:

The VDARE Vocational Analysis Process is based squarely on
Proposition II and Corollary IIb with reliance on Proposition III and
Corollary IIIb of the Minnesota Theory of Work Adjustment (Dawis,
England and Lofquist, 1964; Formally restated and expounded on
through supporting research by Dawis, R.V., 1976, In: Bolton, 1976).
Theoretical Underpinning: MVQS Vocational Analysis
The MVQS Vocational Analysis Process relies heavily on the VDARE Vocational
Analysis Process, and thus, under MVQS Vocational Analysis Process, Proposition I of
the Minnesota Theory of Work Adjustment is also assumed to be true, therefore:

The MVQS Vocational Analysis Process is also based squarely
on Proposition II and Corollary IIb with reliance on Proposition III and
Corollary IIIb of the Minnesota Theory of Work Adjustment (Dawis,
England and Lofquist, 1964; Formally restated and expounded on
through supporting research by Dawis, R.V., 1976, In: Bolton, 1976).
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 57
Reliability, Validity and Error Rates for the VDARE Process
Research supports the following Reliability, Validity and Error Rate statistics as
applicable to the VDARE Process and to the MVQS Vocational Analysis Process (which
incorporates and expands VDARE using the McDOT, McPLOT and MTSP Programs),
with reasonable vocational, rehabilitation economic and statistical certainty:

Inter-Rater Reliability Using the VDARE Vocational Analysis Process Three-Way Inter-Rater Reliability for using the VDARE Process for Residual
Employability Profiling has been found to be in the Extremely High range at
Rxxx=0.9944 (McCroskey, 1979).

Predictive Validity & Known Error Rates Using the VDARE Process - In a
dissertation level validation study on the VDARE Process, McCroskey
(1979), found support for the implications in Proposition II, Corollary IIb,
Proposition III and Corollary IIIb, that maximum least demonstrated
satisfactoriness and satisfaction assumptions, reliably derived and implied
(Rxxx = 0.9944) from residual employability worker traits/job requirements
profiles of successful client work history, modified by medical restrictions,
provided excellent prediction [(93.3% (without testing) and 96.1% (with
testing) agreement, with an error rate from 3.8% (with testing) and 8.4%
(without testing)] of post rehabilitation services job requirement profiles of
jobs at time of placement, in which tenure ensued. A series of follow-up
validation studies (Burge, 1978; Field, McCroskey, Grimes & Wattenbarger,
1978; Knowles, 1978; Reinhardt, 1978; Teal, 1978; and Wattenbarger, 1981)
with similar findings regarding the validity of the VDARE Process were
described in [McCroskey & Perkins (1981), pp. iii-iv & 41-44].

Vocational Potential in SSA Disability Determination Using VDARE - In a
second dissertation level validation study of the VDARE Process,
Wattenbarger (1981), found support for Proposition II, Corollary IIb,
Proposition III, and Corollary IIIb of the theory, that maximum least
demonstrated satisfactoriness and satisfaction assumptions, reliably derived
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Journal of Forensic Vocationology Vol. 7(1) Monograph
and implied from residual employability worker traits/job requirements
profiles of successful client work history, modified by medical restrictions,
provided excellent prediction of Social Security Administration (SSA)
eligibility decisions made using the SSA Grid System. In his case review
research, Wattenbarger compared independent hypothetical findings based on
the VDARE Process, with actual eligibility decisions made by Georgia
Disability Determination Unit (DDU) examiners. He found 67% agreement
with DDU examiners Grid System based decisions and 33% disagreement
with their decisions.
Reliability, Validity and Error Rates for the
MVQS Vocational Analysis Process

Inter-Rater Reliability Using the MVQS Vocational Analysis Process: ThreeWay Inter-Rater Reliability for using the MVQS McDOT, McPLOT and
MTSP Programs in tandem has been found to be in the Extremely High range
at Rxxx=0.9864 (McCroskey, Smolarski & Haskins, 1995).

Predictive Validity and Known Error Rate Using VQ-Wage Data: Predictive
Validity associated with Pre- and Post-Injury VQ-Wage Earning Capacity
Predictions relative to Job Service Work Order Openings and Wage Offered
data has been found to be Extremely High at around Rxy=0.91, with an error
rate (SEe=$0.50/hr) of plus or minus $0.50/hr with 67% confidence and plus
or minus $1.00/hr with 95% confidence. (McCroskey & Hahn, 1998,
McCroskey, 2000).

Predictive Validity & Known Error Rate Using VQ-OES Wage Data:
Predictive Validity associated with Occupational Employment Statistics
(OES) Employment and Wage Estimates has been found to be Very High at
Rxy=0.68, with a known error rate (SEe=$1.01/hr) of plus or minus $1.01/hr
with 67% confidence and plus or minus $2.02/hr with 95% confidence
(Dennis & McCroskey, 1999; McCroskey & Dennis, 1999).
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 59
Validity Studies: The MVQS Vocational Quotient
(VQ) as a Predictor of Earning Capacity
In their study, Dennis & Dennis (1998) identified one program (MVQS MTSP) with 50+
validity research publications, since 1986 to date and continuing (See graphic
representations below). Researchers at Vocationology, Inc., are aggressively moving
forward with on-going scientific research to address the vitally important issues
pertaining to Inter-Rater Reliability, Predictive Validity, and known Standard Error of
Estimate rates identified by the US Supreme Court in the Daubert decision, as being the
key criteria, to be used by judges in their gatekeeper role, for determining admissibility of
expert witness testimony.
Graphically displayed below are three figures summarizing the results from the majority
of the 50+ studies which have been completed and presented in white paper presentations
at national conferences, workshops, seminars, and/or published in books or national peerreviewed journals between 1986 and 1999:
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Journal of Forensic Vocationology Vol. 7(1) Monograph
MTSP Rxy Studies of VQ as a Predictor of Earning Capacity
1.00
0.90
0.80
Rxy Predictive Validity Coefficient
0.70
0.60
0.50
0.40
0.30
0.20
0.10
1997
w
1997t
1997q
1997n
1997k
1997h
1997e
1997b
1995d
1995a
1991d
1991a
1987b
1987b
1987b
1986
0.00
Year
Removal of Outliers
As reflected in the above graph, in 1997 these regression analysis studies began removing
statistical Outliers (standard method, where the Actual Wage was greater than + or - 2
Standard Deviations of the Predicted Wage) from the VQ-Wage data distributions. This
resulted in significant improvements in predictive validity coefficients (Rxy) and
coefficients of determination (R2), along with corresponding decreases in standard errors
of estimates (SEE) for prediction estimates within the + or - two standard deviations of the
regression line of best fit (95% Confidence Level).
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 61
MTSP Rxy & R2 Studies of VQ as a Predictor of Earning Capacity
1.00
0.90
0.80
0.70
0.60
Rxy
0.50
R2
0.40
0.30
0.20
0.10
0.00
0
10
20
30
40
50
The above graph depicts the improvements in Predictive Validity (Rxy) and Coefficients
of Determination (R2) achieved over time.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Standard Error of Estimate (SEE) Results in Chronological Order from
MTSP VQ-Wage Data Earning Capacity Studies: 1984-1997
$7.00
$6.00
US Dollars Per Hour
$5.00
$4.00
SEE67
SEE95
$3.00
$2.00
$1.00
$0
10
20
30
40
50
SEE Plots at the 67% and 95% Levels of Confidence
In reviewing the above graphical representations of 50+ Predictive Validity research
studies, it is clear that VQ-Wage Predictive Validity Coefficents (Rxy), Coefficients of
Determination (R2), and Standard Errors of Estimate (SEE) have improved over time
(from High-Level in 1986, to Extremely High-Level Validity by the late 1990s). The most
recent MVQS MTSP combined 7-state (California, Florida, Louisiana, Minnesota,
Virginia, Washington & Wisconsin) studies confirm these trends as continuing patterns.
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 63
Using MTSP2001 Job Bank Filters to Predict State Job Types
and Job Openings: Reliability, Validity and Error Rates
Data used in the analysis of the reliability, validity and error rates of using MTSP2001
Job Bank Filters to Predict State Job Types and Job Openings was collected from Work
Force Development Centers covering seven States. Only the most current data available
was used to assure reasonable timeliness of the prediction. The Seven State Job Bank
databases containing specific MVQS 9-digit McDOT Coded Job Types and Job Openings
used in this study were compiled from the seven Database Sets indicated below:
1.
2.
3.
4.
5.
6.
7.
California Data = Fiscal Year 1999.
Florida Data = Program Year 1999.
Idaho Data = 4th quarter 1999, 1st, 2nd and 3rd quarters 2000.
Louisiana Data = Fiscal Year 1999.
Minnesota Data = 4th quarter 1997, 1st, 2nd and 3rd quarters 1998.
North Carolina Data = Program Year 1999.
Virginia Data = Fiscal Year 1999.
Tabled Summary Data
Table 124
Table 1: Actual DOTCodes vs. Predicted DOTCodes - Accuracy & Error in MVQS2001 Job Banks
CA
Actual DOTCodes:
FL
ID
LA
MN
NC
VA
5,650
4,147
2,318
2,022
2,861
4,716
3,060
Predicted DOTCodes:
3,033
2,399
1,686
1,350
1,898
1,956
1,963
Percent Accuracy:
53.68
57.85
72.74
66.77
66.34
41.48
64.15
Percent Error:
46.32
42.15
27.26
33.23
33.66
58.52
35.85
Mean Percent of DOT Codes Accurately Predicted:
60.43
Mean Percent of DOT Codes Not Predicted (Error):
39.57
Table 2
Table 2: Actual Openings vs. Predicted Openings - Accuracy & Error in MVQS2001 Job Banks
CA
Actual Openings:
FL
ID
LA
MN
NC
VA
1,908,434
240,961
152,721
76,887
105,625
308,025
182,733
Predicted Openings:
1,899,700
221,543
149,321
69,525
102,800
242,964
149,853
Percent Accuracy:
99.54
91.94
97.77
90.42
97.33
78.88
82.01
Percent Error:
0.46
8.06
2.23
9.58
2.67
21.12
17.99
24
Mean Percent of Job Openings Accurately Predicted:
91.13
Mean Percent of Job Openings Not Predicted (Error):
8.87
Yearly sets of Work Order Job Openings Data from these seven states were incorporated, along with data from 8 other
states covering c. 42.6% of the yearly work order openings in the US during the time frame 1996-2000, were used in
combination with data from the Job Openings Labor Turnover (JOLT; US DOL, 2002) Study, the SOC Employment
and Wage data (JOLT; US DOL, 2002) and US Geo-Location Population from the US Census (US Census Bureau, 2000) in
the derivation of MVQS Volcano III and IV (McCroskey, 2003-2004) Occupational Density Estimates for specific jobs in
3,298 MVQS Geo-Location Job Banks.
Page: 64
Journal of Forensic Vocationology Vol. 7(1) Monograph
Table 3: MVQS MTSP2001 Job Banks Accuracy
CA
FL
ID
LA
MN
NC
VA
Opens
99.54
91.94
97.77
90.42
97.33
78.88
82.01
Titles
53.68
57.85
72.74
66.77
66.34
41.48
64.15
Figure 1: MTSP2001 Job Banks Accuracy
MTSP2001 Job Banks Accuracy
120
Percent Accuracy
100
80
60
40
20
0
CA
FL
ID
LA
MN
NC
VA
The Seven States Compared
Opens
Titles
Conclusions
On average, MTSP2001 State Job Banks captured 60.43% of the 9-digit McDOT Coded
DOT Job Types (See above, Table 1), which, on average, represented 91.13% of the Job
Openings (See above, Table 2) found in the Seven State Job Banks used in this study.
These findings fit normal expected representation patterns quite well and lend strong
support for predictive validity generalization of the identified very high levels of validity,
reliability and representative content of all MVQS MTSP2001 Job Banks.
Filename: C:\My_Documents\States7.doc
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 65
The Four Jung People-based Personality Scales and
Preferences typically associated with those Scales:
1. Energizing - How a person is energized25


Extroversion (E): Preference for drawing energy from the outside world of people,
activities or things.
Introversion (I): Preference for drawing energy from one's internal world of ideas,
emotions, or impressions.
2. Attending - What a person pays attention to


Sensing (S): Preference for using the five senses to determine what is real.
Intuition (N): Preference for using the imagination to envision what is possible - to look
beyond the five senses26.
3. Deciding - How a person decides


Thinking (T): Preference for organizing and structuring information to decide in a
logical, objective way.
Feeling (F): Preference for organizing and structuring information to decide in a
personal, value-oriented way.
4. Living - What Lifestyle a person prefers27


Judgement (J): Preference for living a planned and organized life.
Perception (P): Preference for living a spontaneous and flexible life.
Interpreting VIPR Vocational Personality Types Relative to Jung-based People Types
The sixteen Vocational Interest and Personality Reinforcer (VIPR) Types represent
clusters of jobs, which Jung People-Based Personality Types of the same or similar
persuasion tend to enjoy doing. The VIPR Type job reinforcers are similar to the
personality reinforcer preference tendencies of the various Jung People-Based Types. It is
important to remember that MTSP Job Profile Reports do not list all possible jobs under
the headings, only those which match the client's post vocational potential profile.
It is very important to remember that people can, and frequently do, fill jobs in VIPR
Type clusters that are dissimilar to their Jung People Personality Type... this happens all
the time... and sometimes works out quite well.
VIPR Type Job Clusters are sorted in descending order of Client Values Agreement
(VA) to provide clients of the same Jung People-based Personality Type, ordered lists of
job matches they would typically tend to enjoy. Put another way, the Job at the top of any
given VIPR Type list not only matches client Vocational Potential Profile on the 24 most
vocationally significant worker traits, but would also tend to satisfy client Occupational
Values and Needs more than any other job down the list.
Energizing is only one facet of this scale. It is also a measure of an individual's whole orientation towards either
the Inner world (I) or the External world (E).
26
Jung called this unconscious perceiving.
27
Alternatively, this scale may be defined in terms of Closure - whether or not a person prefers an openended lifestyle.
25
Page: 66
Journal of Forensic Vocationology Vol. 7(1) Monograph
Excerpts from the US Department of the Interior (DOI) Web Site28
Describing Personality Instruments and their Potential Vocational Uses
"Personality instruments are tools that give continuing insight into
ourselves and others. They are frequently used to help individuals see their
preferences, potential strengths and weaknesses, and how they relate to
different occupations. They can be a powerful tool in helping an individual
select a potentially satisfying occupation and/or field of study.
Two of the most well-known personality instruments are the Keirsey
Temperament Sorter29 and the Myers-Briggs Type Indicator®30. Both deal
with four very strong categories for taking in and processing information,
plus interacting with the world31. These instruments are based on the work
of the Swiss psychiatrist, Carl Jung.
This unit uses the Keirsey Temperament Sorter to identify a basic
personality type. You may then use this information to direct you to
different careers.
Before completing the Keirsey, it is important to be aware of some
important points:




The Keirsey measures preferences, not skills. We can all do things we
do not prefer. This is about what you do when you have your druthers.
There are no right or wrong responses, only those that fit you and
those that do not!
One personality type is not better than another. Each has a richness
and potential as great as the others. You are the final judge.
After you receive your 4-letter type, you'll be able to weigh whether
the description fits you and make changes. Read an explanation of
what the letters represent." (See next Page).
28
http://www.doi.gov/octc/personal.html
29
http://www.keirsey.com/
30 Myers-Briggs Type Indicator® is a Registered Trademark of Consulting Psychologists Press (CPP).
31
Keirsey Temperament Sorter® & AdvisorTeam FAQ #1 (Source: http://www.keirsey.com/faq/html)
Question 1: Is the Myers-Briggs Type Indicator the same as the Keirsey Temperament Sorter.
No. The Myers-Briggs Type Indicator is trademarked and copyrighted by Consulting Psychological Press.
The Keirsey Temperament Sorter is trademarked and copyrighted by Prometheus Nemesis Book Company
Inc. They are different personality inventories. However, they are very similar in result (approximately .80
correlation) because they are both based on the work of the Psychologist, Carl Jung. The MBTI measures 4
scales of personality and categorizes individuals into 1 of 16 types using a 4 letter score. Each scale is
viewed independently of the others. Dr. Keirsey believes that these four scales are not independent of each
other and that the most important feature of Jung's Character Types are the two letter pairings that make up
what Keirsey has popularized as Temperament. Dr. Keirsey coined the terms Artisan, Guardian, Rational
and Idealist to describe these 4 main personality types. He then further subdivided the 4 temperaments into
four character variants [each] and popularized the terms used to describe them [the 16 character variants].
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 67
Definitions Relating to Personality Type (P-Type) Letter Designations
What Do Those Letters Represent...?32
Refers to how a person is energized
Extraversion
Introversion
Shows a preference for drawing energy Shows a preference for drawing energy
from the outside word of people,
from one's internal world of emotions
activities or things.
or impressions.
Refers to what a person pays attention to
Sensing
Shows a preference for trusting
information received through the five
senses and noticing what is actual.
iNtuition
Shows a preference for trusting
information received through a "sixth
sense" and noticing what might be.
Refers to what a person most trusts when making a decision
Thinking
Feeling
Shows a preference for trusting
Shows a preference for trusting logical
personal and value-oriented
and objective information.
information.
Refers to the life style a person adopts
Judgment
Shows a preference for living
a planned and organized life.
32
Perception
Shows a preference for living a
spontaneous and flexible life.
Source: Department of Interior Web Site: http://www.doi.gov/octc/scales/htm
Page: 68
Journal of Forensic Vocationology Vol. 7(1) Monograph
Selected Additional Background Information, Insight and
Recommendations Posted on the US Department of the
Interior (DOI) Web Site Regarding Connecting Personality
Types With Careers and Jobs
Connecting Personality Types
With Careers and Jobs33
Before looking at the lists below...
The lists represent careers and jobs people of various types tend to
enjoy doing. The job requirements are similar to the personality
tendencies of the various types. It is important to remember that
these do not list all the jobs possible under the headings. And it is
very important to remember that people can, and frequently do, fill
jobs that are dissimilar to their personality... this happens all the
time... and sometimes works out quite well.
Why then should we even consult these lists?
The lists are just another tool to give you ideas about careers and
jobs you might enjoy. Use the lists as [a] tool, not a box!
Source: careerwebmaster@ios.doi.gov
U.S. Department of the Interior
Revised: Monday, 06-Nov-2000 09:42:48 EST
(Source Web Site: http://www.doi.gov/octc/typescar.html)
33
Source: U.S. Department of the Interior Web Site: http://www.doi.gov/octc/typescar.html
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 69
Partial Lists of Extravert Reinforcer Careers and Jobs from the
US Department of the Interior (DOI) Web Site
ESTP
ESFP
ENFP
ENTP
real estate broker
chef
land developer
physical therapist
stock broker
news reporter
fire fighter
promoter
entrepreneur
pilot
budget analyst
insurance agent
management consultant
franchise owner
electrical engineer
aircraft mechanic
technical trainer
EEG technologist
radiological technician
emergency medical tech.
corrections officer
flight attendant
veterinarian
flight attendant
floral designer
real estate agent
child care provider
social worker
fundraiser
athletic coach
musician
secretary
receptionist
special events producer
teacher: preschool
teacher: elementary
emergency room nurse
occupational therapist
exercise physiologist
team trainer
travel sales
public relations specialist
waiter/waitress
labor relations mediator
conference planner
speech pathologist
HR development trainer
ombudsman
clergy
journalist
newscaster
career counselor
housing director
character actor
marketing consultant
musician/composer
artist
information-graphics
...designer
human resource manager
merchandise planner
advertising account
manager
dietitian/nutritionist
speech pathologist
massage therapist
editor/art director
systems designer
venture capitalist
actor
journalist
investment broker
real estate agent
real estate developer
strategic planner
political manager
politician
special projects developer
literary agent
restaurant/bar owner
technical trainer
diversity manager
art director
personnel systems developer
computer analyst
logistics consultant
outplacement consultant
advertising creative director
radio/TV talk show host
ESTJ
ESFJ
ENFJ
ENTJ
government employee
pharmaceutical sales
auditor
computer analyst
technical trainer
project manager
officer manager
factory supervisor
credit analyst
electrical engineer
stockbroker
regulatory compliance
...officer
chief information officer
construction worker
general contractor
paralegal
industrial engineer
budget analyst
data base manager
funeral director
cook
security guard
dentist
nurse
social worker
caterer
flight attendant
bookkeeper
medical/dental assistant
exercise physiologist
elementary school teacher
minister/priest/rabbi
retail owner
officer manager
telemarketer
counselor
special education teacher
merchandise planner
credit counselor
athletic coach
insurance agent
sales representative
massage therapist
medical secretary
child care provider
bilingual education teacher
professional volunteer
entertainer
recruiter
artist
newscaster
writer/journalist
recreation director
librarian
facilitator
politician
psychologist
housing director
career counselor
sales trainer
travel agent
program designer
corporate/team trainer
child welfare worker
social worker (elderly
...services)
interpreter/translator
occupational therapist
executive: small business
alcohol/drug counselor
sales manager
program designer
attorney
administrator
office manager
chemical engineer
sales manager
logistics consultant
franchise owner
new business developer
personnel manager
investment banker
labor relations
management trainer
credit investigator
mortgage broker
corporate team trainer
environmental engineer
biomedical engineer
business consultant
educational consultant
personal financial planner
network integration
...specialist
media planner/buyer
careerwebmaster@ios.doi.gov
U.S. Department of the Interior
Revised: Monday, 06-Nov-2000 09:42:48 EST
(Source Web Site: http://www.doi.gov/octc/typescar.html)
Page: 70
Journal of Forensic Vocationology Vol. 7(1) Monograph
Partial Lists of Introvert Reinforcer Careers and Jobs from the
US Department of the Interior (DOI) Web Site
ISTJ
ISFJ
INFJ
INTJ
management
accounting
auditing
efficiency expert
engineer
geologist
bank examiners
organization
development
electricians
dentists
pharmacist
school principals
school bus drivers
file clerk
stock broker
legal secretary
computer operator
computer programmer
technical writer
chief information officer
police officer
real estate agent
counseling
ministry
library work
nursing
secretarial
curators
bookkeepers
dental hygienists
computer operator
personnel administrator
paralegal
real estate agent
artist
interior decorator
retail owner
musician
elementary school teacher
physical therapist
nurse
social worker
personnel counselor
alcohol/drug counselor
career counselor
psychologist
educational consultant
special education teacher
librarian
artist
playwright
novelist/poet
editor/art director
information-graphics
...designer
HRM manager
merchandise planner
environmental lawyer
marketer
job analyst
mental health counselor
dietitian/nutritionist
research
educational consultant
architects
interpreter/translator
management consultant
economist
scientist
computer programmer
environmental planner
new business developer
curriculum designer
administrator
mathematician
psychologist
neurologist
biomedical researcher
strategic planner
civil engineer
intellectual properties attorney
designer
editor/art director
inventor
informational-graphics
...designer
financial planner
judge
ISTP
ISFP
INFP
INTP
surveyor
fire fighter
private investigator
pilot
police officer
purchasing agent
chiropractor
medical technician
securities analyst
computer repair person
race car driver
computer programmer
electrical engineer
legal secretary
coach/trainer
commercial artist
carpenter
paralegal
dental assistant
radiological technician
marine biologist
software developer
bookkeeper
clerical supervisor
dental assistant
physical therapist
mechanic
radiology technologist
surveyor
chef
forester
geologist
landscaper designer
crisis hotline operator
teacher: elementary
beautician
typist
jeweler
gardener
potter
painter
botanist
marine biologist
social worker
information-graphics
...designer
college professor
researcher
legal mediator
social worker
holistic health
...practitioner
occupational therapist
diversity manager
human resource
...development specialist
employment development
...specialist
minister/priest/rabbi
missionary
psychologist
writer: poet/novelist
journalist
editor/art director
organizational development
...specialist
strategic planning
writer
staff development
lawyer
architect
software designer
financial analyst
college professor
photographer
logician
artist
systems analyst
neurologist
physicist
psychologist
research/development
...specialist
computer programmer
data base manager
chemist
biologist
investigator
careerwebmaster@ios.doi.gov
U.S. Department of the Interior
Revised: Monday, 06-Nov-2000 09:42:48 EST
(Source Web Site: http://www.doi.gov/octc/typescar.html)
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 71
Excerpts from the Kelly Web Site34 Describing Five Personality
Typologies (PTypes) and Corresponding Crosswalks of those
Five Personality Typologies
PType Personality Types
PTypes
Anyone is free to use any part of this chart with or without credit.
Noteworthy Examples
Correspondence of five personality typologies
PTypes
personality
type1
Keirsey's/
MyersBriggs’
type2
Riso's
Enneagram
type3
PTypes
personality
disorder4
Brau's
astrological
type5
Conscientious
Sensitive
Vigilant
Dramatic
ENFJ
INFJ
ENFP
INFP
1
4+(5)
6+(5)
4+(3)
Obsessive-Compulsive
Avoidant
Paranoid
Histrionic
Aquarius
Pisces
Scorpio
Leo
Aggressive
Idiosyncratic
Inventive
Solitary
ENTJ
INTJ
ENTP
INTP
8
5+(4)
3+(4)
5+(6)
Sadistic
Schizotypal
Compensatory Narcissistic
Schizoid
Aries
Aries
Gemini
Gemini
Leisurely
Serious
Self-sacrificing
Devoted
ESTJ
ISTJ
ESFJ
ISFJ
9+(8)
9+(1)
2
6+(7)
Passive-Aggressive
Depressive
Masochistic
Dependent
Taurus
Taurus
Cancer
Virgo
Self-confident
Adventurous
Mercurial
Artistic
ESTP
ISTP
ESFP
ISFP
3+(2)
7+(8)
7+(6)
7+(6)
Narcissistic
Antisocial
Borderline
Cyclothymic
Capricorn
Sagittarius
Libra
Libra
References35 Cited on Kelley's Web Site for the above listed Crosswalks
34
35
http://www.geocities.com/HotSprings/Resort/5446/correspondence.html
Reference
1. Cf. Oldham, John M., and Lois B. Morris. The New Personality Self-Portrait: Why You Think, Work,
Love, and Act the Way You Do. Rev. ed. New York: Bantam, 1995.
2. Keirsey, David, and Marilyn Bates. Please Understand Me: Character and Temperament Types. 3rd
ed. Del Mar: Prometheus Nemesis, 1978.
3. Riso, Don Richard, and Russ Hudson. Personality Types: Using the Enneagram for Self-discovery.
Rev. ed. New York: Houghton, 1996.
4. Cf. Millon, Theodore, and Roger Davis. Disorders of Personality: DSM-IV and Beyond. 2nd ed. New
York: Wiley, 1996.
5. Brau, Jean Louis, Helen Weaver, and Allan Edmands. Larousse Encyclopedia of Astrology. New
York: McGraw-Hill, 1980.
Page: 72
Journal of Forensic Vocationology Vol. 7(1) Monograph
Vocational Interest and Personality Reinforcer (VIPR) General Job-based Personality
Types with Correspondent Crosswalks to General Jung People-based Personality Type
ORD36 TYPE37 DESCRIPT38
01
ESTJ
Vocational Interest and Personality Reinforcer Type: ESTJ. Out of the 12,775
specific 9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition
Dictionary of Occupational Titles, 5,063 (39.63%) were classified ESTJ.
01
ESTJ
Jung-based Personality Type Correspondent: ESTJ. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/
Temperament Web Site, 689,423 (11.01%) were classified ESTJ as of 12/15/2000.
02
ISFP
Vocational Interest and Personality Reinforcer Type: ISFP. Out of the 12,775
specific 9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition
Dictionary of Occupational Titles, 3,690 (28.88%) were classified ISFP.
02
ISFP
Jung-based Personality Type Correspondent: ISFP. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/
Temperament Web Site, 187,154 (2.99%) were classified ISFP as of 12/15/2000.
03
ESFP
Vocational Interest and Personality Reinforcer Type: ESFP. Out of the 12,775
specific 9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition
Dictionary of Occupational Titles, 919 (7.19%) were classified ESFP.
03
ESFP
Jung-based Personality Type Correspondent: ESFP. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/
Temperament Web Site, 300,792 (4.80%) were classified ESFP as of 12/15/2000.
04
ESTP
Vocational Interest and Personality Reinforcer Type: ESTP. Out of the 12,775
specific 9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition
Dictionary of Occupational Titles, 541 (4.23%) were classified ESTP.
04
ESTP
Jung-based Personality Type Correspondent: ESTP. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/
Temperament Web Site, 169,604 (2.71%) were classified ESTP as of 12/15/2000.
05
ISTJ
Vocational Interest and Personality Reinforcer Type: ISTJ. Out of the 12,775
specific 9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition
Dictionary of Occupational Titles, 474 (3.71%) were classified ISTJ.
05
ISTJ
Jung-based Personality Type Correspondent: ISTJ. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/
Temperament Web Site, 662,915 (10.58%) were classified ISTJ as of 12/15/2000.
06
ESFJ
Vocational Interest and Personality Reinforcer Type: ESFJ. Out of the 12,775
specific 9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition
Dictionary of Occupational Titles, 439 (3.44%) were classified ESFJ.
06
ESFJ
Jung-based Personality Type Correspondent: ESFJ. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/
Temperament Web Site, 763,261 (12.19%) were classified ESFJ as of 12/15/2000.
07
ISTP
Vocational Interest and Personality Reinforcer Type: ISTP. Out of the 12,775
specific 9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition
Dictionary of Occupational Titles, 377 (2.95%) were classified ISTP.
07
ISTP
Jung-based Personality Type Correspondent: ISTP. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/
Temperament Web Site, 135,786 (2.17%) were classified ISTP as of 12/15/2000.
08
ENTJ
Vocational Interest and Personality Reinforcer Type: ENTJ. Out of the 12,775
specific 9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition
Dictionary of Occupational Titles, 237 (1.86%) were classified ENTJ.
08
ENTJ
Jung-based Personality Type Correspondent: ENTJ. Out of 6,263,334 people taking the
36
Order of VIPR Job Type Categories based on Frequencies of Specific Job Types within VIPR Categories.
37
VIPR General Job-based Personality Types to Jung People-based General Personality Types.
38
Descriptive Statistics for VIPR Job-based Types & Corresponding Jung People-based Personality Types.
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 73
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/
Temperament Web Site, 198,653 (3.17%) were classified ENTJ as of 12/15/2000.
Vocational Interest and Personality Reinforcer (VIPR) General Job-based Personality
Types with Correspondent Crosswalks to General Jung People-based Personality Type
ORD TYPE DESCRIPT
09
ISFJ Vocational Interest and Personality Reinforcer Type: ISFJ. Out of the 12,775 specific
9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition Dictionary of
Occupational Titles, 235 (1.84%) were classified ISFJ.
09
ISFJ
Jung-based Personality Type Correspondent: ISFJ. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/ Temperament
Web Site, 605,759 (9.67%) were classified ISFJ as of 12/15/2000.
10
ENTP Vocational Interest and Personality Reinforcer Type: ENTP. Out of the 12,775 specific
9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition Dictionary of
Occupational Titles, 215 (1.68%) were classified ENTP.
10
ENTP Jung-based Personality Type Correspondent: ENTP. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/ Temperament
Web Site, 139,331 (2.22%) were classified ENTP as of 12/15/2000.
11
INTJ Vocational Interest and Personality Reinforcer Type: INTJ. Out of the 12,775 specific
9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition Dictionary of
Occupational Titles, 173 (1.35%) were classified INTJ.
11
INTJ Jung-based Personality Type Correspondent: INTJ. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/ Temperament
Web Site, 328,426 (5.24%) were classified INTJ as of 12/15/2000.
12
INTP Vocational Interest and Personality Reinforcer Type: INTP. Out of the 12,775 specific
9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition Dictionary of
Occupational Titles, 120 (0.94%) were classified INTP.
12
INTP Jung-based Personality Type Correspondent: INTP. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/ Temperament
Web Site, 193,629 (3.09%) were classified INTP as of 12/15/2000.
13
ENFJ Vocational Interest and Personality Reinforcer Type: ENFJ. Out of the 12,775 specific
9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition Dictionary of
Occupational Titles, 91 (0.71%) were classified ENFJ.
13
ENFJ Jung-based Personality Type Correspondent: ENFJ. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/ Temperament
Web Site, 465,565 (7.43%) were classified ENFJ as of 12/15/2000.
14
INFP Vocational Interest and Personality Reinforcer Type: INFP. Out of the 12,775 specific
9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition Dictionary of
Occupational Titles, 90 (0.70%) were classified INFP.
14
INFP Jung-based Personality Type Correspondent: INFP. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/ Temperament
Web Site, 426,896 (6.82%) were classified INFP as of 12/15/2000.
15
ENFP Vocational Interest and Personality Reinforcer Type: ENFP. Out of the 12,775 specific
9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition Dictionary of
Occupational Titles, 70 (0.55%) were classified ENFP.
15
ENFP Jung-based Personality Type Correspondent: ENFP. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/ Temperament
Web Site, 538,008 (8.59%) were classified ENFP as of 12/15/2000.
16
INFJ Vocational Interest and Personality Reinforcer Type: INFJ. Out of the 12,775 specific
9-digit DOT-Coded Job Types in the MVQS2001 McDOT 5th Edition Dictionary of
Occupational Titles, 40 (0.31%) were classified INFJ.
16
INFJ
Jung-based Personality Type Correspondent: INFJ. Out of 6,263,334 people taking the
Temperament Sorter and the Character Sorter on the http://www.keirsey.com/ Temperament
Web Site, 458,132 (7.31%) were classified INFJ as of 12/15/2000.
Page: 74
Journal of Forensic Vocationology Vol. 7(1) Monograph
Methodology
To accomplish the purposes of this study required the reduction of 16 sets of General
Careers and Job Type lists to 16 Most Appropriate, Independent, Mutually Exclusive,
sets of Specific Vocational Interest and Personality Reinforcer (VIPR) Job Types
The 16 DOI lists referred to General sets of Careers and Jobs (around 21 or 22 per list).
They were non-specific and contained duplicate Careers and Jobs listings within and
across the 16 lists. Since duplicates were unclassifiable to a single, most appropriate,
Specific Vocational Interest and Personality Reinforcer (VIPR) Job Type Lists, they were
eliminated. This left 11-12 non-duplicated General sets of Careers and Jobs.
Eliminating duplicate Careers and Jobs from the 16 General Careers and Jobs lists
transformed them into 16 Tip of the Iceberg lists of Careers and Jobs Clusters. These
Tip of the Iceberg lists were reviewed for purposes of identifying the 16 most reasonable
center-of-the-cluster, mutually exclusive sets of Specific 9-digit McDOT Coded VIPR Job
Types (N=23 - 43). These sets were required to re-expand each list to include up to 35
Specific VIPR Job Types with very high Transferable Skills (TS level 97) Valences.
These 16 very high TS Valence VIPR lists were then processed through MTSP using the
MTSP2000 Transferable Skills (TS) Algorithm (Grimley, Williams, Hahn, & Dennis,
2000). The purpose was to establish and rank order all possible Specific VIPR Job Type
lists (N=12,775 each) in descending order by TS Valence by VQ1 by SVP1 and in
ascending order by McDOT Code, for the 16 lists. The final sort for Vocational Interest
and Personality Reinforcer (VIPR) Job-based Personality Types was completed prior to
the removal of duplicates to reduce the 16 VIPR Sets of Job Types to a final N of 12,775
Specific Job Types, across the 16 Independent, Mutually Exclusive, VIPR Job Types.
Put another way, after removal of initially identified duplicates, all Specific jobs were
duplicated 16 times each, with variable Transferable Skills (TS) Valence levels, relative
to each of the 16 VIPR Sets of Job Types. Then, the final 16, most appropriate Vocational
Interest and Personality Reinforcer (VIPR) lists of Job Types were constructed by
retaining only those Specific Job Types with the highest possible TS Valence across each
set of 16 job duplicates. After eliminating the 15 other jobs with duplicate or lesser TS
Valences across the 16 VIPR lists, the goal of establishing the final 16 most appropriate,
seemingly independent & mutually exclusive, VIPR lists of Job Types was accomplished.
Design and Development of the MVQS VIPR Job-based Type Indicator:
A Criterion-Referenced, Paired Associates Jobs Typology Instrument
In designing the MVQS VIPR Job-based Type Indicator Instrument, it was decided that
108 sets of optimally balanced, paired associate VIPR Job-based Specific Job Types
would be required to assure reasonable expectations of moderate to very high Test-Retest
Reliability (i.e., in the Rxx=0.80-0.99 range).
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 75
Therefore, from each of the final 16 VIPR Job-based Personality Type Lists, 13-16
Specific Job Types with TS Valences of 97 were selected for inclusion in the MVQS
VIPR Job-based Type Indicator sets of optimally balanced, paired associates (N=213).
The final N of 108 sets of optimally balanced, paired associates required that three
additional Specific VIPR Job Types from the VIPR INFJ Set of Job Types, with slightly
lower TS Valences (two with TS Valences of 94 and one with a TS Valance of 91), be
incorporated. The addition of these three Specific VIPR INFJ Job Types completed the
list of 216 Specific VIPR Job Types (selected across all 16 VIPR Job Types lists)
required to finalize the 108 sets of optimally balanced, paired associates for the MVQS
VIPR Job-based Personality Type Indicator.
Following the development of 108 sets of optimally balanced, paired associates, the
MVQS VIPR Job-based Personality Type Indicator instrument was placed in an MS
Excel Spreadsheet complete with instructions and scoring formulas designed to yield an
individualized, single-best VIPR Type for each person completing the MVQS VIPR Jobbased Personality Type Indicator. This instrument was then sent via e-mail attachment to
a number of Expert Vocational Practitioners for peer-review and field-testing.
Results
Face and Content Validity were established by Expert Vocational Practitioners to be in
the very high range for the paper and pencil version of the MVQS VIPR Job-based
Personality Type Indicator during the peer-review and field-testing phase.
Suggestions for improvements were solicited from Expert Vocational Practitioners
completing peer-review and field-testing of the Instrument. All recommendations from
Expert Vocational Practitioners completing peer-review and field-testing of the paper
and pencil version of the instrument, as well as the programming format suggestions for
the MVQS VIPR Job-based Personality Type Indicator instrument to be included in the
MVQS2001 McPLOT Sub-Program were reviewed and considered.
The best suggestions were selected, incorporated and implemented in the final versions of
both MVQS VIPR Job-based Personality Type Indicator instruments. The paper and
pencil version was printed for distribution (See next 5 pages). Programming for the
MVQS VIPR Job-based Personality Type Indicator instrument to be included in the
MVQS2001 McPLOT Sub-Program was initiated and completed.
Page: 76
Journal of Forensic Vocationology Vol. 7(1) Monograph
The MVQS VIPR Job-Based Vocational Personality Type Indicator
MVQS Vocational Interest and Personality Reinforcer (VIPR) Type Indicator
(A McCroskey 5th Ed Dictionary of Occupational Titles (McDOT) Paired-Associates, Criterion-Referenced Test)
© 2001 by Billy J. McCroskey, Ph.D. All Rights Reserved.
NAME:
DATE:
Instructions: Review each Pair of Jobs and Indicate your Preference by Placing a "1" in either the "E" or "I" box.
E TITLES
1 Teacher, Industrial Arts
2 Production Engineer
3 Machinist
4 Business-Enterprise Officer
5 Director, Labor Standards
6 Teacher, Secondary School
7 Zoo Veterinarian
8 Aerodynamicist
9 Field-Service Engineer
10 Cardiopulmonary Technologist, Chief
11 Special Procedures Technologist, CT Scan
12 Radiologic Technologist
13 Nurse Anesthetist
14 Teacher, Adventure Education
15 Speech Pathologist
16 Biomedical Engineer
17 Tax Attorney
18 Lawyer
19 Electrical Engineer, Power System
20 Induction-Coordination Power Engineer
21 Mechanical-Design Engineer, Facilities
22 Optometrist
23 Dietitian, Teaching
24 Dietitian, Consultant
25 Teacher, Art
26 Musician, Instrumental
27 Manager, Advertising
E Score:
E
I TITLES
1 Plant Pathologist
2 Botanist
3 Forester
4 Supervisor, Transcribing Operators
5 Jeweler
6 Jeweler Apprentice
7 Periodontist
8 Pediatric Dentist
9 Accountant, Tax
10 Electrical-Prospecting Engineer
11 Electrical Test Engineer
12 Electrical Engineer
13 Internist
14 Ophthalmologist
15 Family Practitioner
16 Animal Scientist
17 Airport Engineer
18 Hydraulic Engineer
19 Aquatic Biologist
20 Radiopharmacist
21 Statistician, Mathematical
22 Illustrator, Medical and Scientific
23 Acupuncturist
24 Sociologist
25 Occupational Therapist
26 Educational Specialist
27 Writer, Prose, Fiction and Nonfiction
I Score:
I
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 77
MVQS Vocational Interest and Personality Reinforcer (VIPR) Type Indicator
NAME:
DATE:
Instructions: Review each Pair of Jobs and Indicate your Preference by Placing a "1" in either the "S" or "N" box.
S TITLES
1 Documentation Engineer
2 Contractor
3 Industrial Engineer
4 Tool Planner
5 Production Planner
6 Economic Development Coordinator
7 Landscape Architect
8 Experimental Aircraft Mechanic
9 Supervisor, Personnel Clerks
10 Veterinarian, Laboratory Animal Care
11 Aeronautical Engineer
12 Aeronautical-Design Engineer
13 Radiologic Technologist, Chief
14 Special Procedures Technologist, Angiogram
15 Echocardiograph Technician
16 Dentist
17 Quality Control Engineer
18 Management Analyst
19 Nurse Practitioner
20 Nurse Supervisor, Evening-or-Night
21 Nurse, School
22 Chiropractor
23 Electronics Technician
24 Athletic Trainer
25 Radiologist
26 Pediatrician
27 Proctologist
S Score:
S
N TITLES
1 Environmental Analyst
2 Reliability Engineer
3 Computer Systems Hardware Analyst
4 Lawyer, Admiralty
5 Manager, Personnel
6 Director, Industrial Relations
7 Chemical Engineer
8 Protection Engineer
9 Illuminating Engineer
10 Materials Scientist
11 Irrigation Engineer
12 Sanitary Engineer
13 Physicist
14 Electro-Optical Engineer
15 Nematologist
16 Home Economist
17 Clergy Member
18 Dietitian, Clinical
19 Psychiatrist
20 Medical Physicist
21 Hearing Officer
22 Arranger
23 Faculty Member, College or University
24 Composer
25 Consultant, Education
26 Broker-and-Market Operator, Grain
27 Industrial Therapist
N Score:
N
Page: 78
Journal of Forensic Vocationology Vol. 7(1) Monograph
MVQS Vocational Interest and Personality Reinforcer (VIPR) Type Indicator
NAME:
DATE:
Instructions: Review each Pair of Jobs and Indicate your Preference by Placing a "1" in either the "T" or "F" box.
T TITLES
1 Manufacturing Engineer
2 Chef De Froid
3 Welder Apprentice, Arc
4 Welder, Arc
5 Machine Setter
6 Pharmaceutical Detailer
7 Magnetic Resonance Imaging (MRI) Techo
8 Technologist, Cardiac Catheterization
9 Polysomnographic Technician
10 Endodontist
11 Pharmacist
12 Writer, Technical Publications
13 Electrolysis-and-Corrosion-Control Engineer
14 Automobile Racer
15 Electronics Mechanic
16 Maintainability Engineer
17 Title Attorney
18 Lawyer, Criminal
19 Electrical-Design Engineer
20 Applications Engineer, Manufacturing
21 Power-Distribution Engineer
22 Architect
23 Poultry Scientist
24 Civil Engineer
25 Biologist
26 Physicist, Theoretical
27 Plant Engineer
T Score:
T
F TITLES
1 Physical Therapist
2 Painter
3 Landscape Gardener
4 Automobile Mechanic
5 Bookkeeper
6 General-Ledger Bookkeeper
7 Aeronautical Project Engineer
8 Aeronautical-Research Engineer
9 Stress Analyst
10 Nurse, Head
11 Nurse, Supervisor
12 Mohel
13 Urologist
14 Obstetrician
15 Physiatrist
16 Nurse, Instructor
17 Photojournalist
18 Sales-Promotion Representative
19 Illustrator
20 Patent Agent
21 Appeals Referee
22 Orchestrator
23 Psychologist, Chief
24 Clinical Therapist
25 Occupational Analyst
26 Supervisor, Education
27 Humorist
F Score:
F
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 79
MVQS Vocational Interest and Personality Reinforcer (VIPR) Type Indicator
NAME:
DATE:
Instructions: Review each Pair of Jobs and Indicate your Preference by Placing a "1" in either the "J" or "P" box.
J TITLES
1 Time-Study Engineer
2 Factory Lay-Out Engineer
3 Real-Estate Agent
4 Director, Arts-and-Humanities Council
5 Director, Unemployment Insurance
6 Director, Consumer Affairs
7 Public-Health Dentist
8 Manager, Quality Control
9 Manager, Records Analysis
10 Nurse, General Duty
11 Nurse-Midwife
12 Nurse, Staff, Occupational Health Nursing
13 Chemical-Test Engineer
14 Director, Media Marketing
15 District Attorney
16 Allergist-Immunologist
17 Cardiologist
18 Gynecologist
19 Railroad Engineer
20 Dairy Scientist
21 Transportation Engineer
22 Director of Institutional Research
23 Community Dietitian
24 Social Worker, School
25 Job Analyst
26 Playwright
27 Screen Writer
J Score:
J
P TITLES
1 Printmaker
2 Airframe-and-Power-Plant Mechanic
3 Supervisor, Dairy Farm
4 Assembler and Tester, Electronics
5 Typing Section Chief
6 Sample Maker I
7 Veterinarian
8 Nurse, Private Duty
9 Fashion Designer
10 Emergency Medical Technician
11 Ultrasound Technologist
12 Battalion Chief
13 Electrical-Research Engineer
14 Motorcycle Racer
15 Electronics-Mechanic Apprentice
16 Power-Transmission Engineer
17 Mechanical-Design Engineer, Products
18 Mechanical Engineer
19 Chemist
20 Chemist, Food
21 Statistician, Applied
22 Doctor, Naturopathic
23 Graduate Assistant
24 Counselor, Nurses' Association
25 Planner, Program Services
26 Clinical Psychologist
27 Counselor
P Score:
P
Page: 80
Journal of Forensic Vocationology Vol. 7(1) Monograph
MVQS-VIPR Type Indicator Results
NAME:
DATE:
(E)xtroversion Score:
(I)ntroversion Score:
(S)ensing Score:
I(N)tuiting Score:
(T)hinking Score:
(F)eeling Score:
(J)udgement Score:
(P)erception Score:
MVQS-VIPR TYPE
<-:Insert Highest Letter Scores from each above pair (e.g. INTJ).
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 81
Discussion, Conclusions and Recommendations
Clearly, many decades of patience, research and development have begun to produce a
fruitful realization of Parson’s informal, yet intuitive, direction. Efforts must continue
with the collection and synthesis of on-going research into renewed development and
refining of our tools, methods, procedures, theories and standards for vocational practice.
1. Theories must be refined through research.
2. New tools for more efficiently and effectively matching workers with jobs
must continue to be developed and updated.
3. New hypotheses must be empirically tested through research on those
tools.
4. The results of our studies must be published in peer-reviewed journals to
keep our peers abreast of mounting research evidence and the need to
update our standards for vocational practice.
As we increase our own understanding through scientific research:

Developing and refining our tools, methods, procedures, theories and
standards for vocational practice will become more routine, and

Providing credible evidence of the reliability and validity of our tools,
methods, procedures, theories and standards for vocational practice, to the
courts and other interested parties, will become more second nature.
Face and Content Validity were established by Expert Vocational Practitioners to be in
the very high range for the paper and pencil version of the MVQS VIPR Job-based
Personality Type Indicator during the peer-review and field-testing phase. Additional
studies to more firmly establish expected moderate to high level Test-Retest Reliability
and Predictive Validity for both the paper and pencil and machine versions of the MVQS
VIPR Job-based Personality Type Indicator instruments are recommended. Studies are
currently in the research design phase and should be completed in the near future.
Page: 82
Journal of Forensic Vocationology Vol. 7(1) Monograph
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Emotional Trauma: Its Impact on Vocational Analysis
By
Billy J. McCroskey, Ph.D., CRC, CRE, CRV, ABVE, and
Kenneth L. Dennis, Ph.D., LP, LMFT, CRC, CRV
Abstract
Emotional Trauma may be defined to include the effects of external events, which can
produce undesirable physical changes in the brain. Emotional Trauma causes symptoms
that produce measurable declines in occupational performance and represent vocational
deficits, which can be tracked and identified by way of and in terms of diminished
capacities on 10 of the 24 most vocationally significant worker trait variables.
Introduction
Emotional Trauma may be defined to include the effects of external events, which can
produce undesirable physical changes in the brain. In this respect, emotional trauma is a
type of learning. If there is a small information gap between what is known and what is
observed, very little learning can be expected to occur. If this gap is moderate, however,
intense curiosity is produced that either results in long-term memory or frustration
(Lawrence & Nohria, 2002, pp. 112-113). Learning associated with emotional trauma
occurs when the gap is perceived to be large or when a very large gap is not perceived.
The learning (single event) produces fear or possible repression. In emotional trauma, the
extreme of no response becomes emotional blockage. Learning from curiosity expands
behavior. Learning from emotional trauma narrows and restricts the range of behaviors.
Time is lost to re-experiencing past traumatic events. Response numbing produces
avoidance of stimuli. Concentration is impaired by persistent and increased arousal to
traumatic events. Symptoms commonly include sleep problems, fatigue, irritability,
emotional outbursts, depression, hypervigilance, exaggerated startle response, and
difficulty concentrating (American Psychiatric Association, 1994, pp. 424-429).
Emotional Trauma causes symptoms that produce measurable declines in occupational
performance and represent vocational deficits, which can be tracked and identified by
way of and in terms of selected diminished worker trait capacity levels.
Review of Literature
Goodwin (1987) specified what can be expected when a person has posttraumatic stress
disorder (PTSD). While he focused his research on Vietnam Veterans, he presented
symptoms common across a wide variety of people with this disorder. These included:
1. Chronic and Delayed Depression. Most people with PTSD are depressed, and
many of them have been depressed for a long period of time. Symptoms include
sleep disturbance, psychomotor retardation, feelings of worthlessness, difficulty
concentrating, etc. Along with depression is a very well developed sense of
helplessness. Substance abuse may be present during depressive periods. Self-
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2.
3.
4.
5.
6.
7.
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medication may be a coping response, with alcohol often being the drug of
choice.
Isolation. People with PTSD have few friends. They report feeling isolated and
distant from other people.
Rage. Extreme emotions such as rage are frightening to people with PTSD. They
may strike out for no apparent reason.
Avoidance of Feelings and Alienation. Loved ones often complain of emotional
coldness, uncaring and distance. People with PTSD may report an inability to
experience the joys of life and feeling emotionally dead.
Anxiety Reactions. People with PTSD often describe themselves as being very
vigilant. They are tuned to anything out of the ordinary. Unexpected noises,
sights, etc. can produce dramatic and unexpected responses.
Sleep Disturbance. People with PTSD might find the hours before sleep very
comfortable. They stay awake as long as possible. Activities that keep them
awake might include drinking or smoking cannabis to dull uncomfortable
thoughts. When there is nothing to occupy their minds, thoughts wander and
traumatic thoughts can easily intrude.
Intrusive Thoughts. Traumatic memories often play a role in daytime
functioning. Obsessive thoughts might be triggered by common, everyday
experiences (sights, smells, touches, sounds, etc.).
There are evolutionary advantages to learning from threatening experiences. When
danger is perceived, it is a good idea to avoid it in the future. Emotional trauma, however,
can result in changes to a person’s functioning to the point that response becomes more
debilitating than the original trauma. A person can be unable to function in the
community, family, or at work in response to trauma even after the other physical
damage has healed. These physical alterations in brain processes (structure and
functioning) become a diagnosable condition. A simple model for the destructive impact
of trauma could include the following components.
1. External information is perceived that is greatly different from what the person
expects to receive. The information is difficult to understand and process.
2. The value placed on this information is of imminent destruction to the person’s
emotional, spiritual, intellectual, or physical existence.
3. The information and its value represent state-based learning. The emotional value
of the information causes the person to narrow and restrict cognitive processing.
Instead of thinking about a variety of responses, explanations options, the
person’s perception, association, memory, and response options are very narrowly
constricted.
4. Cues from the external environment or within the person’s internal cognitive
process can reinitiate or access this state-based learning in the future. A sight,
sound or other stimulus causes the person to relieve the trauma again and again.
The vocational limitations of emotional trauma cannot be directly measured; although,
new brain imaging and matching techniques are increasing the chance of this occurring in
the future. Vocational Evaluation can assess and document changes in cognitive
processes following emotional trauma by carefully comparing pre- and post-measures of
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functioning. Cognitive capacity is most likely documented by measuring the ability to
abstract information, attend to detail, and to stay on task and well as more common
limitations to concentration associated with emotional and physical fatigue.
Previous Trauma
Following its classification as a psychiatric diagnosis in 1980, posttraumatic stress
disorder (PTSD) has been given as a legal defense of murders and other crimes
committed by war veterans, battered wives, or abused children. It is uncertain why some
people develop severe psychiatric symptoms following traumatic experiences that limit
their lives and employment, when others seem relatively unimpaired. Gurvits, Gilbertson,
Lasko, Tarhan, Simeon, Macklin, Orr, and Pitman, (2000) compared 21 women (sexually
abused as children) with 38 Vietnam Veterans. Both groups had people with and without
PTSD symptoms. Neurological and psychological tests indicated, people with symptoms
"reported more neurodevelopmental problems and more childhood attention deficit
hyperactivity disorder symptoms and had lower IQs, all of which were significantly
correlated with neurologic soft signs." These results remained true after the researchers
accounted for alcoholism or head injury.
The conclusion was that, "Neurologic compromise is evident from subject history and
findings from physical examination in both women and men with chronic PTSD who had
experienced different kinds of traumatic events in childhood and adulthood." Early abuse
increases the symptoms of PTSD. The "diminished ability to cope with a traumatic event
and its consequences owing to lower intelligence may increase the likelihood of the
PTSD outcome." It was also noted that neurological soft signs might be associated with
psychiatric disorders such as schizophrenia or obsessive-compulsive disorder. Schnurr,
Friedman, and Rosenberg (1993) found high pre-combat levels of neurological
dysfunction related to paranoia, depression, psychopathology, and other psychiatric
problems predicted PTSD symptom development or severity.
Brain Functioning
It is estimated there are 23 million Americans with anxiety disorders (Lawrence &
Nohria, 2002, p. 134). Research indicates that anxiety is sometimes related to alarm
messages moving from sense organs directly to the cognitive centers of the cortex and
then to the amygdala. Other messages appear to move directly to the amygdala. Patients
with panic disorders seem to benefit from medications that weaken the amygdala’s action
combined with cognitive therapy. Experienced threats activate a pain avoidance reflex
from the amygdala. People then experience intense fear or anger (fight or flight) in a state
of temporary irrationality. As a fundamental, inborn reflex mechanism, the ability of the
cortex to operate rationally is temporarily shut down. "It can be thought of as a coping
mechanism of last resort."
Amen (1998) reported on a patient (Mark) that was tormented by past memories. A
SPECT study was ordered because of a head injury in Vietnam. There was increased
activity on the left side of his basal ganglia. After being prescribed Depakote, Mark’s
headaches disappeared and he was able to benefit from therapy for his posttraumatic
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stress disorder. There was no indication that the activity on the left side of his basal
ganglia was associated with his stress reaction, only that it impeded therapy.
Teicher (2002) suggested that early trauma might stimulate the left side of the amygdala
to the point of damaging it and the left side of the hypocampus through excessive
exposure to stress hormones. He reported findings that the left side of the hippocampus of
patients with PTSD was 12 percent smaller than for non-PTSD patients. EEG research
found a higher percentage of anomalies in people with PTSD compared to Controls, and
the left hemisphere of the frontal and temporal lobes were most involved.
The brain research by Harry F. Harlow at the University of Wisconsin – Madison showed
a similar pattern for monkeys raised with a wire and terrycloth surrogate mothers. The
impact of a surrogate mother was less severe if the mother swung from side to side. This
movement appeared to be transferred to the middle part of the cerebellum (cerebullar
vermis). This structure is located at the back of the brain just above the brain stem.
Abnormalities in the vermis have been found related to bipolar disorder, schizophrenia,
autism and attention deficit disorder.
The impact of back and forth movement on ameliorating the impact of emotional trauma
was noted in the treatment of PTSD reported by Shapiro (1995). She discovered Eye
Movement Desensitization and Reprocessing (EMDR) while moving her eyes back and
forth during a walk in the park. The eye movement appeared to stop her continuous
disturbing thoughts. This therapeutic approach has become a standard treatment for
PTSD, and it utilizes eye movement, lateral touch or lateral sounds to stop looping
traumatic thoughts from controlling a person’s experience. EMDR appeared to be an
expansion of Neurolinguistics Programming (NLP) originally presented by Bandler &
Grinder (1975).
Employment Problems
Anxiety is often experienced as both an emotional and a physical state. A person
suffering from anxiety experiences intense dread and racing thoughts, along with heart
pounding and increased breathing rate. The person suffering from PTSD may experience
acute anxiety when presented with a stimulus similar to those associated with the original
traumatic situation (Cavaiola & Lavender, 2000). The sound of a helicopter, loud noise or
the smell of diesel fuel may result in the worker hitting the ground in response to a past
trauma. The smell of burning food may trigger a worker to scream hysterically in reaction
to past sexual assault when a similar smell was present. There have been cases when
workers have panicked from feelings of suffocation when placed in a MRI or CT Scan as
part of a Worker’s Compensation Evaluation. Past traumas can seem so real that workers
become terrified to return to work. Physical injury can make PTSD and other stressrelated disorders worse. Specifically, life-threatening injuries combined with chronic pain
can produce more devastating symptoms (Meek, 1990).
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Faking Emotional Trauma
It is difficult to diagnosis an emotional trauma. Clients provide most of the evidence for
its existence, and considerable secondary gain may be present when there is litigation.
Being a detective is not part of the evaluation process (Early, 1990). Data provide the
best documentation of client abilities. The major focus is the level of match between the
test results and self-reports. Test results should be consistent across concepts. Lack of
concentration from fatigue would be more likely on longer rather than shorter tests; or,
likewise, at the end of testing compared to the beginning. Concentration should be better
earlier in the day compared to later in the day. It is not an inconsistency for a short
Clerical test early in the process to yield higher results compared to a longer Clerical test
completed after 3.5 to 4.5 hours of testing. Reactions to emotional trauma differ a great
deal between people. Test results that do not make clinical sense (given expected abilities
and responses) should be reported as inconsistencies rather than assuming they represent
imagined or exaggerated emotional trauma or malingering.
Emotional Trauma: Primary Impact is on 10 Worker Traits
There are 24 Vocationally Significant Variables (VSV), or worker traits, which have
been found highly related to overall job difficulty, as measured by the Vocational
Quotient (VQ, McCroskey & Perkins, 1981; McCroskey & Lowe, 1986, 1987). These 24
VSV are used to construct a worker's Four-Factor by 24 most vocationally significant
worker trait level Post-Injury Evaluative Data Profile. That profile must match, or
exceed, updated McCroskey 5th ed. Dictionary of Occupational Titles 2001 (McDOT
2001 5th ed. DOT, McCroskey, 2001) Job Demands Profile to identify Post-Injury jobperson matches in the McCroskey Transferable Skills Program 2001 (MTSP 2001;
McCroskey, 2001). Relevant MTSP geographic Job Bank Databases are used for
Transferable Skills Analysis (TSA) of job-person matches relative to a worker's past
relevant work history. Of the 24 VSV listed below, the first 10 Worker Trait, under
Worker Trait Factors 1 and 2, are most likely to be affected by emotional trauma.
1. Worker Trait Factor 1: General Educational Development (GED R, M & L)
 Reasoning Development (R),
 Math Development (M), and
 Language Development (L).
2. Worker Trait Factor 2: Aptitudes (S, P, Q, K, F, M, & E)
 Perception
 Spatial Perception (S),
 Form Perception (P), and
 Clerical Perception (Q).
 Dexterity
 Motor Coordination (K),
 Finger Dexterity Aptitude (F),
 Manual Dexterity (M), and
 Eye-Hand-Foot Coordination (E).

Other Aptitudes (C)
 (C) - Color Discrimination.
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3. Worker Trait Factor 3: Physical Demands (PD1-PD6)






(PD1) - Lift, carry, push, pull, sit, stand, walk,
(PD2) - Climbing, balancing,
(PD3) - Bend, stoop, crouch, squat, kneel, crawl,
(PD4) - Reaching, handling, fingering, feeling,
(PD5) - Talking, hearing, writing,
(PD6) - See up-close and see far-away; and,
4. Worker Trait Factor 4: Environmental Tolerances







(EC1) - Work location
(EC2) - Extreme cold,
(EC3) - Extreme Heat,
(EC4) - Dampness, wetness, humidity,
(EC5) - Noise, vibrations,
(EC6) - Hazards39: machinery, electrical, chemical, unprotected heights, and
(EC7) - Adverse Atmosphere: dusts, fumes, odors, mists or gases.
General Educational Development (GED R, M & L) variables are likely to show a drop
in ability from fatigue and concentration problems associated with emotional trauma.
These variables are on an ascending scale of 1 to 6 (1 < 7th percentile, 6 > 81st percentile,
and 4 ranges from the 42nd to the 57th percentiles). Being distracted makes it more
difficult to perform well on Reasoning, Math and Language tests. The single GED
variable that is expected to show the greatest drop, however, is Math or Arithmetic
(written and verbal), as it requires the highest level of abstract thought of the three GED
variables.
Perception (S, P and Q) variables become impaired when a person is distracted and
depressed. These variables are on an ascending scale of 1 to 5 (1 < 5th percentile, 5 > 79th
percentile, and 3 and 4 range from the 39th to the 79th percentiles). Diminished Clerical
(Q) stemming from diminished attention to detail in written and tabular material is the
single Perception variable typically expected to show the most impairment as a result of
emotional trauma. Similarly, like falling dominoes, as a result of emotional trauma
affecting GED and Perception Variables, Motor Coordination (K), Finger Dexterity (F),
Manual Dexterity (M) and Eye-Hand-Foot Coordination (E) tend to drop measurably.
Case Study of an Emotional Trauma Client
In this example case study, the Emotional Trauma Client (TC) was a female employed in
the medical field as a Registered Nurse (RN). TC was in her early 30s with a graduate
degree at the time of an automotive accident that left her with scars, lifting restrictions,
chronic pain (partially controlled by medication) and symptoms of posttraumatic stress
disorder (including fatigue and impaired concentration). At the time of evaluation, TC
had reached Maximum Medical Improvement and had returned (with significant
accommodation) to her previous employment with her previous employer. TC was able to
work a two to three hours per day at her previous employment prior to exhibiting a
decrease in concentration and an increase in fatigue. Her workday was split between her
duties as a Nurse (between two and four hours per work day) and other work tasks
39
Hazards (0=No; 1=Yes) was also dropped in the TC's Post-Injury Evaluative Data Profile, based on clinical judgment
regarding her diminished levels of Concentration and Attention-to-Detail.
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involving training and supportive duties that did not require medication management or
strenuous physical activity. Partially due to her ongoing litigation, her employer made no
change in her job description or salary following her return to work. However, it was well
documented that she was not able to fulfill the essential duties of her employment. Her
immediate supervisor was supportive of her efforts and position. However, he was
scheduled to retire in a couple of years.
Vocational Analysis of the 10 VSV Most Affected by Emotional Trauma
Comparing TC's previous jobs and plotting the highest job demands across all of these
jobs produced Table 1. Reasoning, Math and Language were above average (5) as were
other ability scales (4). Her work history included a variety of job tasks that combined to
produce the below listed High Across Jobs Profile on the 10 VSV most likely to be
affected by Emotional Trauma, along a Vocational Quotient of 156.61 across all 24 VSV.
TABLE 1
Pre-Injury High Across Jobs Profile
R
M
L
S
P
Q
K
F
M
E
5
5
5
4
4
4
4
4
4
4
When the Evaluative Data Profile of TC was adjusted to include worker trait functioning
derived from reported pre-injury college grades, the resulting High Across Jobs Profile
(Table 1) combined with academic grades produced significantly higher levels of ability
(Table 2). Her Academic Pre Profile showed a substantial improvement compared to her
High Across Jobs Profile. TC was not only a dedicated worker across multiple jobs in her
work history; she had also been an exceptional student. Her resulting Vocational Quotient
was noted to be 176.93.
TABLE 2
Pre Injury Profile (from Pre-Injury Academics)
R
M
L
S
P
Q
K
F
M
E
6
6
6
5
5
5
5
5
5
4
TC's Pre-Injury Academic Profile (Table 2) provided a more complete indication of her
maximum vocational potential prior to her injury than did her High Across Work History
Profile of successfully demonstrated worker trait functioning. Post-Injury Vocational
Testing on the 10 VSV of interest (Table 3) for TC showed several interesting findings in
terms of diminished worker trait level functioning. Since she had been successful in her
past employment, her High Across Jobs Profile (Table 1) represented the baseline (at
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least minimum levels) of demonstrated worker trait functioning prior to her injuries. Her
Pre-Injury Academic grades expanded the at least minimums to likely maximum levels.
As noted earlier, mathematical ability is a skill that is vulnerable to trauma. It represents
the highest level of abstract thought, and it is easily destroyed. Significant brain injury,
chemical dependency, depression, or emotional trauma can all be expected produce a
decline in mathematical ability. Unfortunately, many people are math phobic. They lock
up or become anxious when they do math problems. It is not always easy to distinguish
fear of math from a decline in functioning associated with emotional trauma. In this case,
TC had a drop from successfully demonstrated well above average to tested low average
or below math ability (Table 3). Pre-Injury, TC had to have above average math skills,
because that level of functioning was an essential work history job demand and her math
ability was well documented by Pre-Injury undergraduate and graduate school grades.
Except for Form Perception (P) and Finger Dexterity (F), the other Evaluative Data
(Table 3) showed a decline from her expected ability, if not injured, to a lower skill level
following her injuries. Manual Dexterity and Eye-Hand-Foot Coordination appeared to
show the greatest change because these variables were heavily influenced by residual
physical limitations and pain. However, the nature of her vocational impairments was not
fully disclosed by her test scores. Test patterns across tests also needed to be considered.
TABLE 3
Initial Post-Injury Evaluative Data Profile
R
M
L
S
P
Q
K
F
M
E
5
3
5
4
5
4
4
5
3
2
Order of testing is very important in cases involving emotional trauma. For example, if
Clerical or other tests requiring intense concentration are given at the start of the testing
day, results may be quite higher than results of tests requiring intense concentration
obtained later in the testing day (especially near the end of the testing day). Fatigue and
concentration problems typically begin to affect test results more during the 4th or 5th
hour of testing and continue to affect results to the end of the day. Placing tests requiring
more intense concentration (e.g., the MCT, MMPI-2, WAIS-III, or ANAM), toward the
end of the testing day will likely provide more accurate vocational information for
emotional trauma clients than placing such tests at the start of the testing day.
TC was able to maintain concentration on short tasks as evidenced by her Automated
Neuropsychological Assessment Metrics (ANAM; Dennis & McCroskey, 1995, 1997)
Continuous Performance Task scores, which were at the 60th and 75th percentile levels.
This was a short test (about three minutes) given near the start of testing and TC was able
to do well on it. However, her across-tests temporal patterns showed TC significantly less
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effective on the Minnesota Clerical Test (MCT), a classic test of concentration and
attention to detail, which was given later in the testing day. On the MCT, which takes 15
minutes to complete, TC's performance declined significantly to the 30th and 40th
percentile levels compared to the earlier ANAM results (60th and 75th percentile levels).
The full impact of emotional trauma on TC became clearer on the WAIS-III Digit Span
subtest results. Digit Span was also a short test, but the need to concentrate was stressed
more on it, than on any of the other short tests she was administered. Her performance on
Digit Span fell to the 27th percentile. The WAIS-III Arithmetic subtest was extremely
sensitive to her ability to manipulate abstract symbols in her mind. On it, TC scored her
lowest results: Arithmetic was at the 2nd percentile. It was clinically judged as probably
the best predictor of her ability to fulfill the medication administration and management
requirements of her RN job, where other information indicated she did poorly in her
return to RN work. The most telling result was on the WAIS-III Working Memory where
her composite score for Working Memory was found to be at the 9th percentile.
Clerical (Q) skills tend to be a part of many tests. Therefore, the ability to concentrate on
Clerical tasks can become artificially elevated for some clients, when they do well on
short tests that do not adequately reflect lengths of time found in actual job environments.
One such case is when a client displays concentration problems due to emotional trauma.
Adjusting her Evaluative Data Profile (Table 4) due her inability to work for more than a
few hours per day and low scores on relevant tests produced a more consistent picture of
the true work capacity of TC. Access to the labor market dropped from about 88 percent
Pre-Injury to 1 percent Post-Injury. Inability to concentrate reduced her ability to benefit
from new training from above average (Pre-Injury), to well below average (Post-Injury).
She had few (if any) transferable skills for any job in the McDOT. She was not qualified
for any jobs related by transferable skills to her work as a Registered Nurse.
TABLE 4
Final Adjusted Post-Injury Evaluative Data
R
M
L
S
P
Q
K
F
M
E
5
1
5
4
5
1
4
5
3
2
Note: Math (M1) and Clerical Perception (Q), which were averaged across indicators
from Post-Injury Test Results were clinically lowered to enhance profile accuracy
relative to RN job requirements, based on differential across-test patterns over time.
Post Return To Work Follow-Up
Three years post-injury, TC was still employed at the same position with the same
employer as before her injury. Her yearly post-injury wage was approximately 20%
higher than her yearly pre-injury wage despite the fact that she could not perform
approximately two-thirds of the essential duties of RN position. She had reached
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Maximum Medical Improvement (MMI). Her overall test results and across-test patterns
(along with the fact she had reached MMI), made it clear that the impairments caused by
the emotional trauma suffered by TC, which were evidenced in her significantly
suppressed post-injury worker trait profile of employability and earning capacity, were
permanent and stable. Given the fact there were no likely indicators to suggest she would
improve her concentration in the future and re-establish her pre-injury employability, the
most likely outcome of her injury (after the litigation is finished) was estimated to be at
least re-assignment to a job more in line with TC's post-injury employability and earning
capacity range, or termination with cause, based on the increased likelihood of patient
endangerment in the area of patient care tasks dealing with administering and managing
medication which require good concentration and close attention-to-detail.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
MTSP VQ1-OES Aggregate & VQ1-McDOT Specific Wage Estimation40
By
Billy J. McCroskey, Ph.D., CRE, CRC, CRV-Diplomate, ABVE-Diplomate
Steven J. Hahn, MS, CRC, CVE, QRC, CRV-Diplomate
Kenneth L. Dennis, Ph.D., LP, LMFT, CRC, CRV-Diplomate
Abstract
Previous research has assessed the validity of the Vocational Quotient (VQ) as a predictor of
income. This study evaluated the ability of the VQ1 to predict income reported by Occupational
Employment Statistics (OES) groups. Linear regression was used to predict reported income at
the Mean, 10th, 25th, 50th, 75th and 90th percentile of the OES-Hourly Wage distribution. VQ1 was
used to predict to the middle of these six distributions, and was found to be a very accurate
predictor of the OES-Wage distribution as reported by the US Department of Labor. Relative
Predictive Validity (Rxy) Coefficients were determined to be 0.970, 0.973, 0.975, 0.974, 0.972
and 0.966, respectively. Relative Coefficients of Determination were 0.950, 0.948, 0.951, 0.948,
0.944 and 0.934, respectively. Relative Standard Errors of Estimate (SEE) were $1.19, $0.45,
$0.69, $1.08, $1.65 and $2.66 per hour, respectively. It was recommended that specific McDOTVQ141 Wage Prediction be used to increase overall reliability and reduce aggregate SEEs. This
was done, and the subsequent Follow-Up Study Addendum (at the end of this study) was
completed in 2002 with similar recommendation for MVQS 2003.
Literature Review
From 1996 through 1999, there were a series of predictive validity studies that used the
Vocational Quotient (VQ) to predict income. These were regression studies that used the
VQ as the independent variable and Job Service work orders or actual income of
Vocational Rehabilitation clients as the dependent variable. When the Vocational
Quotient was used as a predictor to the middle of the income distribution, it was found to
predict 80 to over 90 percent of the variance in actual income data. In 1999, the
Vocational Quotient (VQ) was grouped by O*NET code subgroups. Again, the VQ was
found to be an accurate predictor of Job Service work order income. This study expanded
earlier research by grouping linking the new McDOT VQ1 (McCroskey, 2000) mean for
each Occupational Information Network (O*NET) Occupational Unit Classification
(OUC) Occupational Employment Statistics (OES) code subgroup with the six mean
OES Hourly Wage distribution data points. The McDOT VQ1 means were then used to
predict Mean OES Hourly Wages (1998 US DOL) at the Mean, the 10th, 25th, 50th, 75th
and 90th percentiles.
40
This study led to the follow-up study, presented at the end of this study, with minor editing, is a repeat of the original study
by McCroskey, Hahn & Dennis (2000), originally published in the Journal of Forensic Vocationology, Vol 6(1), 2000, pp. 107-134.
The follow-up extended the linear regression findings presented in the first study, using a curvilinear regression VQ-Wage
prediction model. The MVQS Rehabilitation Economist presented at the end of the second study was developed as a tool for
Vocational Experts, Vocational Rehabilitation Counselors and related Consultants for use in their work with clients. Based
on the scientific research from the first two studies, regarding the relationship between employment potential and earnings
capacity prediction, it bridges the gap between vocational rehabilitation and rehabilitation economics. As with any tool, the
quality of the inputs determines the quality of the outputs.
41
McCroskey Dictionary of Occupational Titles (McDOT; McCroskey, 2000) 5th Edition DOT VQ replacement data.
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 105
OES Wage Data Overview
Occupational Employment Statistics (OES)
Information provided by: Occupational Employment Statistics Bureau of Labor Statistics
http://www.oesinfo@bls.gov.
The Occupational Employment Statistics (OES) survey is an annual mail survey
measuring occupational employment and wage rates for wage and salary workers in
nonfarm establishments, by industry. The survey samples approximately 400,000
establishments per year, taking 3 years to fully collect the sample of 1.2 million
establishments. BLS and the Employment and Training Administration (ETA) provide
the funding for the survey. BLS provides the procedures and technical support, while the
State Employment Security Agencies (SESAs) collect the data. The SESAs produce
occupational estimates by detailed industries for local areas and the states. BLS produces
similar industry-specific estimates for the nation as well as employment and wage
estimates for 750 occupations across all industries for the nation, each of the 50 states
plus the District of Columbia, and Metropolitan Statistical Areas (MSAs).
Selected OES Survey Definitions and Concepts
Many of the concepts and definitions used in the OES Survey are comparable to those in
the Current Employment Statistics survey (a monthly BLS payroll survey of
nonagricultural establishments). Others, however, are unique to this survey. Key
definitions are as follows:
An establishment is an economic unit (factory, mine, or store) which produces goods or
services. It is generally a single location and engaged predominantly in one economic
activity.
Employment is the number of workers who can be classified as follows:
 Full time or part-time
 Employees on paid vacations or other type of leave
 Workers on unpaid short-term absences
 Salaried officers or executives and staff members of incorporated firms
 Employees temporarily assigned to other units
 Employees for whom the reporting unit is their permanent duty station regardless
of whether that unit prepares their paycheck.
 Self-employed, owners or partners of unincorporated firms, and unpaid family
workers.
Employees are reported in the occupation in which they are working, not necessarily for
which they were trained. Employment represents the estimate of total wage and salary
employment in an occupation across the industries in which it was reported. The OES
survey form sent to an establishment contains between 50 and 225 OES occupations. The
number of occupations listed on a form depends on the industry classification and size
class of the sampled establishments. To reduce paperwork and respondent burden, no
survey form contains every OES occupation.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
The OES classification system uses seven occupational divisions to categorize workers in
one of 750 detailed occupations. The seven divisions are as follows:
1.
2.
3.
4.
5.
6.
7.
Managerial and administrative occupations
Professional, paraprofessional, and technical occupations
Sales and related occupations
Clerical and administrative support occupations
Service occupations
Agriculture, forestry, fishing, and related occupations
Production, construction, operating, maintenance, and material handling
occupations
Wages for the OES survey are straight-time, gross pay, exclusive of premium pay.
Included are base rate, cost-of-living allowances, guaranteed pay, hazardous-duty pay,
incentive pay including commissions and production bonuses, and on-call pay. Excluded
are back pay, jury duty pay, overtime pay, severance pay, shift differentials,
nonproduction bonuses, and tuition reimbursements. The OES survey collects wage data
in 11 intervals. Employers report the number of employees in an occupation per each
wage range. The wage intervals used for the 1998 survey were as follows:
Interval
Range A
Range B
Range C
Range D
Range E
Range F
Range G
Range H
Range I
Range J
Range K
Hourly Wages
Under $6.75
$6.75 to $8.49
$8.50 to $9.99
$10.00 to $11.24
$11.25 to $13.24
$13.25 to $15.74
$15.75 to $19.24
$19.25 to $24.24
$24.25 to $43.24
$43.25 to $60.00
$60.01 and over
Annual Wages
Under $14,040
$14,040 to $17,659
$17,660 to $20,779
$20,780 to $23,399
$23,400 to $27,559
$27,560 to $32,759
$32,760 to $40,039
$40,040 to $50,439
$50,440 to $89,959
$89,960 to $124,820
$124,821 and over
Hourly versus annual wage reporting: For each occupation, respondents are asked to
report the number of employees paid within specific wage intervals. The intervals are
defined both as hourly rates and the corresponding annual rates, where the annual rates
are constructed by multiplying the hourly wage rate for the interval by the typical work
year of 2,080 hours. In reporting, the respondent can reference either the hourly or the
annual rate, but is instructed to report the hourly rate for part-time workers.
Annual wage: Most of the annual mean wage estimates in this release are calculated by
multiplying the mean wage by a year-round, full-time hours figure of 2,080 hours per
year (52 weeks by 40 hours). Most employees are paid at an hourly rate by their
employers and may work less than or more than 40 hours per week. Thus, the annual
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 107
wage estimates may not represent the actual annual pay received by the employee. There
are a small number of occupations where only an annual wage figure is provided. The
workers in these occupations generally work less than the usual 2,080 hours per year.
Since the survey does not collect the actual hours worked, the hourly rate cannot be
calculated with a reasonable degree of confidence from the annual wages. For these
occupations, therefore, only the annual salary is reported, which has been calculated
directly from the data (rather than by multiplying an hourly figure by 2,080 hours).
Occupations that typically have a work-year of less than 2,080 hours include musical and
entertainment occupations, pilots and flight attendants, and teachers.
The Unemployment Insurance (UI) Address File is a micro-level employer file prepared
quarterly by each State's Employment Security Agency and submitted to the Bureau of
Labor Statistics. For 1998, the file from the second quarter of 1997 is used as a sampling
frame while the fourth quarter of 1998 is used as a source of population values for
employment (the second quarter of 1998 is used as a source of population employment
values for New Jersey).
Industry classifications are based on the 1987 Standard Industrial Classification Manual,
Office of Management and Budget, 1987. Industry is classified on the basis of the major
product or activity of the establishment, as determined by total sales or receipts of the
calendar year prior to classification.
Scope of the OES Survey
The survey included private establishments in SIC codes 07, 10, 12-17, 20-42, 44-65, 67,
70, 72, 73, 75, 76, 78-84, 86, 87, and 89 covering agricultural services; mining;
construction; manufacturing; transportation and public utilities; wholesale and retail
trade; finance, insurance, and real estate; and services. The survey also covered private
and government establishments in SIC codes 806, 821, 822, 824, and 829, the Postal
Service (SIC 43), as well as all remaining state and local government establishments.
Data for the Postal Service are universe counts obtained from the United States Postal
Service (USPS). Federal government data are obtained from the Office of Personnel
Management (OPM); these data exclude information from selected agencies.
The reference date of the 1998 survey was the week that included October 12, November
12, or December 12 of 1998. The reference date for a particular establishment in this
survey is dependent on its two-digit SIC code. See below.
Reference Date
October 12
November 12
December 12
Industries Surveyed
07, 15-17, 41, 46, 50-62, 67, 70, 73, 79, 84
26-28, 30, 35, 36, 40, 42, 45, 47, 48, 63-65, 75, 76, 78, 80, 81, 83, 86, 87, 89
10, 12-14, 20-25, 29, 31-34, 37-39, 44, 49, 72, 82, and state and local governments
Survey Sampling Procedures
The sampling frame for this survey was the list of establishments which reported to the
state Unemployment Insurance (UI) files for the two-digit SICs listed above. For the
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Journal of Forensic Vocationology Vol. 7(1) Monograph
1998 survey, the frame's reference date was the second quarter of 1997. This frame was
supplemented with a list supplying establishment information on Railroads (SIC 401).
Establishments in the universe were stratified by Metropolitan Statistical Area (MSA),
three-digit SIC, and size of firm (i.e., size class). Size classes were defined as follows:
Size class
1
2
3
4
5
6
7
8
9
Number of employees
1 to 4
5 to 9
10 to 19
20 to 49
50 to 99
100 to 249
250 to 499
500 to 999
1,000 or more
In 1996 and 1997, establishments in size classes 2 to 6 were selected based on a
probability sample. The sampling weights in size class 2 were adjusted to account for the
employment in size class 1. In 1998, the OES Survey began sampling establishments in
size class 1; thus, establishments in all size classes are now represented in the probability
sample. UI reporting units with 250 or more employees are sampled with certainty across
the three year cycle of the survey. Approximately one third of these units are selected
within each MSA/SIC/Size class each year. The above allocation resulted in a total initial
sample size of 409,347, 408,805, and 400,405 UI reporting units or establishments for
1996, 1997, and 1998. The combined initial sample size for 1996, 1997, and 1998 is
1,206,964 UI reporting units or establishments. (Note that the combined sample size is
not a simple sum of the three year's samples. Some State government establishments are
included in the survey each year. In the tabulations for the combined survey these
establishments are only included once, from the most recent year. Federal government
units are also included in the combined tabulation.)
Method of Collection
Survey schedules were initially mailed to virtually all sampled establishments. Personal
visits, however, were made to some of the larger establishments. Two additional mailings
were sent to nonresponding establishments at approximately three week intervals.
Telephone follow-ups and, in some cases, personal visits were made to nonrespondents
considered critical to the survey because of their size.
Response
Subsequent to the close-out date for National estimates, additional data were collected by
the states and used to prepare their own estimates. Consequently, the response rates in
most states are higher than the response rate used to develop estimates of all-industry
wage rates for each MSA.
Journal of Forensic Vocationology Vol. 7(1) Monograph
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Estimation Methodology
The OES survey samples approximately 400,000 establishments each year and, over a 3year period, contacts approximately 1.2 million establishments. Each single-year sample
represents one-third of both the certainty and non-certainty strata for the full 3-year
sample plan. While estimates can be made from a single year of data, the OES survey has
been designed to produce estimates using the full 3 years of data. The full 3-year sample
allows the production of estimates at fine levels of geography, industry, and occupational
detail, while estimates using any one year of data would be subject to a higher sampling
error (due to the smaller sample size) and the limitations associated with having only 1/3
of the certainty units. Producing estimates using the 3 years of sample data provides
significant sampling error reductions (particularly for small geographic areas and
occupations); however, it also has some quality limitations in that it requires the
adjustment of earlier years' data to the current reference period--a procedure referred to as
wage updating.
The 1996 OES survey estimates, which were published in December 1997, were from the
first year of the new OES wage survey and were developed using only a single year (i.e.,
400,000 sample units) of data. The initial estimation methodology used a weighting-class
adjustment procedure for nonrespondents and an employment benchmark at the
state/industry level. Since multiple years of data were not available for the 1996
estimates, the estimation procedure did not involve wage updating.
The 1997 OES survey estimates represent the second year of OES estimates and have
been developed using both the 1996 and 1997 surveys. The 1997 estimates also represent
the first year of using a wage-updating methodology in developing the OES survey
estimates. In addition to the wage-updating procedure, the 1997 estimates used an
improved estimation methodology, utilizing a nearest neighbor imputation approach for
nonrespondents and applying employment benchmarks at a detailed MSA by 3-digit
industry and broad size-class level. A variant of the imputation procedure is also used to
account for item nonresponse. Note: Because of the difference in estimation methods for
these first 2 years of OES estimates, the data from 1997 are not strictly comparable with
those published from 1996.
The 1998 OES survey estimates are developed from the full three years of the OES
sample. The combined 1996, 1997, and 1998 data cover approximately 1.2 million
sample units. The 1998 estimates use the wage-updating methodology introduced in
1997, which uses the over-the-year fourth-quarter wage changes from the Bureau's
Employment Cost Index to adjust prior years' data before combining them with data from
the current year. In addition, the 1998 estimates use the estimation methodology
introduced in 1997, which uses a nearest neighbor imputation approach for
nonrespondents and applies employment benchmarks at a detailed MSA by 3-digit
industry and broad size class level.
The wage-updating procedure is used to adjust prior year wages to reflect increases
between the previous data and current year data. For wage-updating purposes, the Bureau
has used the national over-the-year wage changes from the fourth quarter of 1996 to the
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Journal of Forensic Vocationology Vol. 7(1) Monograph
fourth quarter of 1997 and from the fourth quarter of 1997 to the fourth quarter of 1998
for the nine occupational divisions for which ECI estimates are available. These factors
are applied to both the 1996 and 1997 survey data to update them to the fourth-quarter
1998 level before combining them with the 1998 survey data. Such a procedure assumes
that each occupation's wage, as measured in the earlier years, moves according to the
average movement of its occupational division and that there are no major geographic or
detailed occupational differences--and this may not be the case. Research is being
conducted to develop procedures that may account for differences in the rate of change at
more detailed levels, than the nine ECI occupational divisions.
The hot deck (nearest neighbor) imputation procedure imputes for unit nonresponse. This
type of nonresponse occurs when a unit reports no employment data. In hot decking, units
in the sample are stratified into 'year/State/4-digit industry/size class' cells. Within each
cell, a donor (i.e., responding unit) is selected to represent each nonrespondent under the
proviso that a donor can not be selected twice. The sampling frame employment is used
to match donors with nonrespondents. Once a donor and nonrespondent are matched, the
occupational employment totals from the donor are copied over to the nonrespondent. In
the event that a donor is not available at the 'year/State/4-digit industry/size class' cell
level, the procedure advances to succeeding higher level cells until a donor is found.
Occasionally a responding establishment may provide employment information, but omit
wage distribution information for selected occupations. The OES survey currently uses a
variation of the mean imputation procedure to impute for item nonresponse. This type of
nonresponse occurs when a unit reports the total-employment for its occupations but not
the corresponding employment by wage intervals. In this procedure, units in the sample
are stratified into 'year/MSA/3-digit industry/size class' cells. A wage-employment
distribution is then calculated for those occupations with missing wage-employment
based on the usable data in the cell. Missing wage-employment is imputed using the just
calculated wage-employment distribution to prorate the total-employment of those
occupations with missing wage-employment.
A separate ratio estimator is used to develop estimates of occupational employment in
each wage interval. The auxiliary variable is the population value of total employment
obtained from the refined Unemployment Insurance files for the 1998 reference month.
Within each MSA, the estimated employment for an occupation at the reported threedigit SIC/wage interval level was calculated by multiplying the weighted employment by
its ratio factor. The estimated employment for an occupation at the all-industry level was
obtained by summing the occupational interval employment estimate across all industries
within an MSA reporting that occupation. A further adjustment to each occupational
employment total was made as described in the Reliability of the Estimates section. This
adjustment did not affect the mean or median wage rates. The employment and wage data
for federal government workers in each occupation were added to the survey derived
data.
A mean wage and a median wage are calculated using wage data from establishments in
the industries that reported employment for an occupation.
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Mean wage is the estimated total wages for an occupation divided by its weighted survey
employment. For the upper open-ended wage interval, a Winsorized mean procedure is
used to estimate the mean wage. That is, the mean wage value for the upper open-ended
wage interval is set at its lower bound ($60.01). For the other intervals, a mean wage
value was calculated based on occupational wage data collected by the Office of
Compensation and Working Conditions. These interval mean wage values are then
attributed to all workers reported in the interval. For each occupation, total weighted
wages in each interval (i.e., mean wages times weighted employment) are summed across
all intervals and divided by the occupation's weighted survey employment to obtain a
mean wage.
Median wage is the estimated 50th percentile of the distribution of wages; 50 percent of
workers in an occupation earn wages below, and 50 percent earn wages above the median
wage. The wage interval containing the median wage is located using a cumulative
frequency count of employment across wage intervals. After the targeted wage interval is
identified, the median wage rate is then estimated by a linear interpolation procedure
Reliability of the Estimates
The occupational wage rates in this report are estimates derived from a sample survey.
Two types of errors are possible in an estimate based on a sample survey - sampling error
and nonsampling error. Sampling error occurs because the observations are based on a
sample, not on the entire population. Nonsampling error is due to response, nonresponse,
and operational errors.
Nonsampling Errors: Estimates are subject to various response, nonresponse, and
operational errors during the survey process. Sources of possible errors are data
collection, response, coding, transcription, data editing, nonresponse adjustment, and
estimation. These errors would also occur if a complete census was conducted under the
same conditions as the sample survey. Explicit measures of their effects are not available.
However, it is believed that the important response and operational errors were detected
and corrected during the review and validation process.
The employment total and wage data for the occupation reflects only those industries that
reported the occupation. This occurs primarily in those industries where the occupation
appeared on the survey form. Since every occupation does not appear on every industryspecific form, there may be a bias in the employment and wage data for some
occupations. The extent of this bias is unknown.
Another source of potential bias is the limitations placed on the size of the benchmark
factors. A benchmark factor is the ratio of a known employment value to a samplederived employment estimate. This factor is used to make a post-stratification adjustment
that makes the total weighted employment estimate at the state / three-digit SIC industry /
Metropolitan Statistical Area (MSA) / employment size class level match the population
employment at that level. The source of the population employment data is the states'
Quarterly Unemployment Insurance files for the reference period of the survey. In cases
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Journal of Forensic Vocationology Vol. 7(1) Monograph
where a small sample was taken, the ratio factor can become large or small. In order to
prevent an establishment from contributing either too much or not enough to an MSA's
wage rate estimates, the benchmark factor was not allowed to exceed a predetermined
value. The total employment count for those MSAs where the benchmark factor was
limited by this ceiling will be biased to a small degree in those strata. The employment
not assigned to those strata because of this ceiling was then distributed across the other
MSAs in the state / three-digit industry, so that the estimated employment of the State /
three-digit industry would match the known employment totals at that level.
Sampling Errors: The particular sample used in this survey is one of a large number of
possible samples of the same size that could have been selected using the same sample
design. For example, occupational wage rate estimates derived from the different samples
will differ from one another. The deviation of a sample estimate from the average of all
possible sample estimates is called the sampling error. The standard error of an estimate
is a measure of the variation of estimates across all possible samples and thus is a
measure of the precision with which an estimate from a particular sample approximates
the average result of all possible samples.
Quality Control Measures
Quality control measures implemented in the OES survey include:









review of the specific occupations to be collected for each industry, and those to
be collected in residual categories
creating and validating the sample frame for all states at BLS-Washington
allocating and selecting the sample for all states at BLS-Washington
follow up solicitations of nonrespondents (especially critical nonrespondents)
review of survey schedules to verify the accuracy and reasonableness of the
reported data
adjustments of atypical reporting units on the data file
validation of the nonresponse adjustment factors
validation of the population employment and ratio factors
standardized data processing programs and activities
Employment Estimates
Employment represents the estimate of total wage and salary employment in an
occupation across the industries in which it was reported. The OES survey form sent to
an establishment contains between 50 and 225 OES occupations. The number of
occupations listed on a form depends on the industry classification and size class of the
sampled establishments. To reduce paperwork and respondent burden, no survey form
contains every OES occupation.
Wage Estimates
Wages for the OES survey are straight-time, gross pay, exclusive of premium pay.
Included are base rate, cost-of-living allowances, guaranteed pay, hazardous-duty pay,
incentive pay including commissions and production bonuses, and on-call pay. Excluded
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 113
are back pay, jury duty pay, overtime pay, severance pay, shift differentials,
nonproduction bonuses, and tuition reimbursements.
Annual Wage
Most employees are paid at an hourly rate by their employers and may work less than or
more than 40 hours per week. The annual wage estimates on this website are calculated
by multiplying the hourly wage estimates by a year-round, full-time hours figure of 2,080
hours per year (52 weeks by 40 hours). Thus, the annual wage estimates may not
represent the actual annual pay received by the employee. There are a small number of
occupations where hourly wages are not published. For these occupations the annual
wages have been directly calculated from the reported survey data. The workers in these
occupations are paid based on an annual amount, but generally work less than the usual
2,080 hours per year. Since the survey does not collect the actual hours worked, the
hourly rate cannot be calculated with a reasonable degree of confidence from the annual
wages. Occupations that typically have a work-year of less than 2,080 hours include
musical and entertainment occupations, flight attendants and pilots, and teachers.
Mean Hourly Wage
The mean hourly wage is the estimated total wages for an occupation divided by its
weighted survey employment.
Median Hourly Wage
The median hourly wage is the estimated 50th percentile of the distribution of wages; 50
percent of workers in an occupation earn wages below, and 50 percent earn wages above
the median wage.
Relative Standard Error (RSE)
The particular sample used in this survey is one of a large number of all possible samples
of the same size that could have been selected using the same sample design. Estimates
derived from different samples would differ from each other.



The variance of a survey estimate is a measure of the variation among the
estimates from all possible samples.
The standard error of a survey estimate is the square root of its variance
The relative standard error is the ratio of the standard error to the estimate itself.
The sample estimate and its standard error allows the construction of an interval estimate
with a prescribed level of confidence. The interval will include the mean value of the
estimates from all possible samples. To illustrate, if all possible samples were selected,
and if each of these were surveyed under essentially the same conditions, and an estimate
and its estimated sampling error were calculated from each sample, then:

Approximately 90 percent of the intervals from 1.6 standard errors below to 1.6
standard errors above the derived estimate would include the average value of the
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Journal of Forensic Vocationology Vol. 7(1) Monograph
estimates from all possible samples. This interval is called a 90-percent
confidence interval.
Approximately 95 percent of the intervals from two standard errors below to two
standard errors above the derived estimate would include the average value of the
estimates from all possible samples. This interval is called a 95-percent
confidence interval.
For example, suppose that an estimated occupational employment total is 5,000 with an
associated relative standard error of two percent. Based on this data, the standard error of
the estimate is 100 (= 5,000 X 0.02) and the 95-percent confidence interval for the
estimate is (5,000 + 200) or (4,800 to 5,200). This confidence interval is one of many that
could be constructed based on the same sample design. Approximately 95 percent of
these confidence intervals would encompass the average value of the estimates from all
possible samples.
Methodology
All 12,775 McDOT 2000 DOT codes were grouped into OES code groups. One
regression formula was derived for each of the six 1998 data points of interest (the Mean,
10th, 25th, 50th (Median) 75th, and 90th percentiles reported by the government). The OES
code groups were averaged for each whole number Vocational Quotient. The small
number of mean wage estimates not provided in the OES data were derived using linear
regression. Wage estimates not provided for jobs with incomes larger than $60.00 per
hour were likewise extrapolated using straight-line regression incorporating available
OES wage estimates. For the final analysis, there were no missing wage estimates.
Each regression formula predicted the average wage for each of the six OES code group
data points for each whole number Vocational Quotient (N=88). Therefore, each
percentile distribution represented a different study. Since the goal of this research was to
predict to the middle of each distribution, outliers (wages beyond two standard deviations
from the mean) were removed using standard outlier removal analyses in each study.
Results
Initial VQ-Wage Data (Tables 1-15) and Final VQ-OES Wage Data (Tables 16-33)
results are tabled and graphed below.
Journal of Forensic Vocationology Vol. 7(1) Monograph
Initial Results: Tables 1-15 (No Outliers Removed)
Table 1: Raw Data Inputs For Descriptive Statistics (vqhn88.sta): No Outliers Removed
VQ1
HMEAN
WPCT10
WPCT25
HMEDIAN WPCT75
WPCT90
1
68
9.73
6.1
7.225
9.1
11.245
14.25
2
70
9.2136
5.89
6.8445
8.4009
10.4173
13.4773
3
71
10.1758
6.1875
7.4117
9.3583
11.8875
15.4567
4
72
9.6165
6.0081
7.0486
8.7651
11.0198
14.3572
5
73
9.7253
5.9693
6.9777
8.785
11.243
14.9907
6
74
9.7012
6.0158
7.0714
8.8378
11.1974
14.6823
7
75
9.7267
6.0228
7.1029
8.8715
11.2115
14.6391
8
76
10.0996
6.177
7.3737
9.2536
11.7497
15.2629
9
77
9.8701
6.131
7.2433
9.0611
11.4777
14.8331
10
78
10.0346
6.1617
7.3264
9.1961
11.6958
15.159
11
79
10.1153
6.2326
7.4305
9.3165
11.8127
15.1396
12
80
10.3125
6.309
7.5462
9.4924
12.062
15.5826
13
81
10.412
6.3366
7.6237
9.5887
12.221
15.7806
14
82
10.6157
6.4363
7.7825
9.7753
12.4872
16.053
15
83
10.6261
6.437
7.7784
9.7794
12.5174
16.1456
16
84
10.7048
6.4429
7.8037
9.8588
12.6369
16.2885
17
85
10.6504
6.434
7.7884
9.8195
12.5368
16.1243
18
86
10.6184
6.4341
7.7916
9.8138
12.5073
16.0178
19
87
10.7278
6.472
7.8298
9.8723
12.6535
16.3139
20
88
10.8312
6.5501
7.9226
9.9896
12.7547
16.3724
21
89
10.7139
6.5283
7.9041
9.9182
12.6003
16.1303
22
90
10.8296
6.5488
7.9464
10.0261
12.7798
16.3418
23
91
10.8929
6.5663
7.9678
10.0628
12.8765
16.493
24
92
11.3422
6.7205
8.2668
10.4803
13.5009
17.2667
25
93
11.0943
6.668
8.1141
10.2426
13.1578
16.8283
26
94
10.9059
6.7375
8.0999
10.1439
12.9004
16.309
27
95
11.5393
6.9519
8.5043
10.7343
13.7185
17.3213
28
96
11.8409
7.0502
8.735
11.0258
14.1364
17.9488
29
97
11.991
7.1098
8.7741
11.1406
14.3294
18.2302
30
98
11.8345
7.0438
8.6603
10.9845
14.0585
17.9407
31
99
11.9257
7.0509
8.6963
11.0405
14.2635
18.2101
32
100
12.1313
7.2131
8.9128
11.3045
14.451
18.3139
33
101
12.4593
7.3283
9.0539
11.566
15.0011
19.4077
34
102
12.802
7.544
9.3582
11.8902
15.3347
19.8597
35
103
13.5012
7.8776
9.8631
12.5727
16.2175
20.9602
36
104
13.8515
7.9063
9.9628
12.8569
16.8351
22.1937
37
105
15.1152
8.3217
10.6398
13.8983
18.4558
25.0224
38
106
14.9219
8.3597
10.6903
13.9624
18.3069
23.9848
39
107
15.2356
8.5077
10.832
14.1515
18.7268
24.8185
40
108
16.4957
9.034
11.5769
15.2209
20.3391
27.8359
41
109
17.1877
9.2028
11.9097
15.8378
21.5053
29.5336
42
110
17.8079
9.4559
12.2975
16.4381
22.2815
30.8589
43
111
17.0608
9.2828
11.9776
15.8358
21.2156
28.6204
44
112
17.7987
9.7114
12.5156
16.5898
22.3348
30.4912
45
113
17.3508
9.4748
12.2229
16.1105
21.4409
29.5455
46
114
18.1909
9.958
12.8837
17.0316
22.8715
30.9717
47
115
18.1465
9.7334
12.64
16.9008
22.958
31.3403
48
116
17.925
9.74
12.5646
16.6605
22.3463
30.8436
49
117
17.9615
9.8531
12.6848
16.8014
22.5428
30.2833
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50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
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Journal of Forensic Vocationology Vol. 7(1) Monograph
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
17.5842
18.5207
19.5909
19.0001
18.9973
19.545
18.3497
20.3958
20.164
21.2247
21.3318
21.4662
21.7976
22.1807
23.2925
24.2717
25.8587
24.3427
23.619
22.0263
25.36
24.1246
26.74
26.7591
24.4583
22.27
22.766
24.8933
27.9075
32.6833
29.2133
39.766
47.5
46.26
39.1633
43.9033
49.05
47.8875
46.1517
9.7227
10.1664
10.4901
10.4703
10.4073
10.7323
10.4021
11.084
11.1314
11.1828
12.4024
11.2693
12.3994
13.05
13.6275
13.9114
15.2353
13.5762
13.0034
12.7374
15.482
13.9915
16.4962
16.0273
14.1867
12.41
13.016
13.2033
16.865
15.2467
17.8667
18.896
19.9367
20.646
18.0633
19.2717
19.05
19.715
18.6717
12.4955
13.1481
13.6225
13.5323
13.411
13.9023
13.2547
14.4751
14.2407
14.4375
15.6669
14.5533
15.7652
16.32
17.2334
17.9172
19.7916
17.5062
16.711
16.0421
19.411
17.7508
20.3125
20.0045
18.315
15.8933
16.74
16.81
21.265
26.8233
21.8467
33.694
41.7267
38.404
32.0733
37.2517
45.88
42.765
42.0217
16.4916
17.3923
18.3658
17.8094
17.7865
18.5622
17.3597
19.37
18.805
20.0333
20.4704
20.3433
20.7191
21.4624
22.8166
24.0548
26.2559
23.8762
23.0603
20.8689
25.131
23.2031
26.035
25.8445
23.84
20.9667
22.05
22.2767
27.7175
39.19
27.76
50.89
66.1267
60.86
49.2833
58.0917
72.71
67.7725
65.8083
21.9655
23.1641
25.1944
24.0781
23.9495
24.9141
22.9063
26.2007
25.4102
28.2503
27.1085
28.0948
28.1891
29.0184
30.4753
31.9603
35.1825
32.6654
32.0128
28.4632
33.199
31.5362
35.9763
35.73
32.52
29.0667
30.076
33.4
37.42
55.28
38.91
68.62
88.7867
80.184
66.48
78.5
99.54
91.475
90.1217
29.6067
31.5677
34.2459
32.26
32.1453
33.2764
30.2852
34.267
34.1633
38.2089
35.3922
38.086
37.0888
37.1569
37.7884
41.1634
43.9406
42.7335
41.8569
36.8853
41.415
40.9031
44.095
44.8982
40.4633
38.97
37.816
46.8433
45.5975
71.96
51.1433
86.142
111.8833
100.294
83.5433
99.345
126.37
115.505
114.7533
Table 2: Descriptive Statistics Results-All Variables: No Outliers Removed from vqhn88.sta
Descriptive Statistics (vqhn88.sta)
Standard
Valid N
Mean
Minimum
Maximum Std.Dev.
Error
Skewness
VQ1
88 112.4886
68
156 25.56713 2.725465
-0.00272
HMEAN
88 19.14521
9.2136
49.05 10.04983 1.071316 1.509612
WPCT10
88 10.38945
5.89
20.646 4.198199 0.447529 0.863975
WPCT25
88 14.50219
6.8445
45.88 9.061727 0.965983 1.979139
HMEDIAN
88 19.88438
8.4009
72.71 14.69945 1.566966 2.243501
WPCT75
88 26.64598
10.4173
99.54 20.15005 2.148002 2.179085
WPCT90
88
34.4874
13.4773
126.37
25.3532
2.70266 2.129078
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 117
Table 3: Correlation Results for all Variables: No Outliers Removed from vqhn88.sta
Correlations (vqhn88.sta)
Red Marked correlations are significant at p < .05000
N=88 (Casewise deletion of missing data)
VQ1
VQ1
HMEAN
WPCT10
WPCT25
HMEDIAN
WPCT75
WPCT90
1
0.881048
0.938424
0.821285
0.780183
0.792332
0.800457
HMEAN
WPCT10
WPCT25
HMEDIAN WPCT75
WPCT90
0.881048 0.938424 0.821285 0.780183 0.792332 0.800457
1
0.96484 0.991111
0.97976 0.983001 0.984092
0.96484
1 0.930783 0.899275 0.905291 0.905269
0.991111 0.930783
1 0.996533 0.997419
0.99586
0.97976 0.899275 0.996533
1
0.99939 0.997568
0.983001 0.905291 0.997419
0.99939
1 0.999103
0.984092 0.905269
0.99586 0.997568 0.999103
1
Table 4: Regression Results for HMEAN: No Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: HMEAN (vqhn88.sta)
R= .88104822 R²= .77624597 Adjusted R²= .77364418
F(1,86)=298.35 p<.00000 Std.Error of estimate: 4.7814
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-19.8118 2.312267
-8.56811
3.73E-13
VQ1
0.881048 0.051008 0.346319
0.02005 17.27283
1.06E-29
Table 5: Prediction Results for HMEAN: No Outliers Removed from vqhn88.sta
Predicting Values for (vqhn88.sta)
Predicting Values for (vqhn88.sta)
variable: HMEAN
variable: HMEAN
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.346319
0
0
VQ1
0.346319
0
0
Intercpt
-19.8118
Intercpt
-19.8118
Predictd
-19.8118
Predictd
-19.8118
-95.0%CL
-24.4084
-95.0%PL
-30.37
+95.0%CL
-15.2151
+95.0%PL
-9.25355
Table 6: Regression Results for WPCT10: No Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: WPCT10 (vqhn88.sta)
R= .93842408 R²= .88063975 Adjusted R²= .87925184
F(1,86)=634.51 p<.00000 Std.Error of estimate: 1.4588
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-6.94415 0.705483
-9.84311
9.48E-16
VQ1
0.938424 0.037255 0.154092 0.006117 25.18944
1.84E-41
Table 7: Prediction Results for WPCT10: No Outliers Removed from vqhn88.sta
Predicting Values for (vqhn88.sta)
Predicting Values for (vqhn88.sta)
variable: WPCT10
variable: WPCT10
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.154092
0
0
VQ1
0.154092
0
0
Intercpt
-6.94415
Intercpt
-6.94415
Predictd
-6.94415
Predictd
-6.94415
-95.0%CL
-8.3466
-95.0%PL
-10.1655
+95.0%CL
-5.5417
+95.0%PL
-3.72279
Page: 118
Journal of Forensic Vocationology Vol. 7(1) Monograph
Table 8: Regression Results for WPCT25: No Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: WPCT25 (vqhn88.sta)
R= .82128465 R²= .67450848 Adjusted R²= .67072369
F(1,86)=178.22 p<.00000 Std.Error of estimate: 5.1999
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-18.2418 2.514633
-7.25425
1.66E-10
VQ1
0.821285 0.061521 0.291087 0.021805 13.34975
1.14E-22
Table 9: Prediction Results for WPCT25: No Outliers Removed from vqhn88.sta
Predicting Values for (vqhn88.sta)
Predicting Values for (vqhn88.sta)
variable: WPCT25
variable: WPCT25
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.291087
0
0
VQ1
0.291087
0
0
Intercpt
-18.2418
Intercpt
-18.2418
Predictd
-18.2418
Predictd
-18.2418
-95.0%CL
-23.2407
-95.0%PL
-29.724
+95.0%CL
-13.2429
+95.0%PL
-6.75953
Table 10: Regression Results for HMEDIAN: No Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: HMEDIAN
R= .78018270 R²= .60868505 Adjusted R²= .60413488
F(1,86)=133.77 p<.00000 Std.Error of estimate: 9.2486
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-30.5729 4.472578
-6.83564
1.12E-09
VQ1
0.780183 0.067455 0.448555 0.038782 11.56598
3.29E-19
Table 11: Prediction Results for HMEDIAN: No Outliers Removed from vqhn88.sta
Predicting Values for (vqhn88.sta)
Predicting Values for (vqhn88.sta)
variable: HMEDIAN
variable: HMEDIAN
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.448555
0
0
VQ1
0.448555
0
0
Intercpt
-30.5729
Intercpt
-30.5729
Predictd
-30.5729
Predictd
-30.5729
-95.0%CL
-39.4641
-95.0%PL
-50.9955
+95.0%CL
-21.6817
+95.0%PL
-10.1503
Table 12: Regression Results for WPCT75: No Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: WPCT75 (vqhn88.sta)
R= .79233195 R²= .62778992 Adjusted R²= .62346189
F(1,86)=145.05 p<.00000 Std.Error of estimate: 12.365
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-43.5981 5.979486
-7.29129
1.4E-10
VQ1
0.792332 0.065788 0.624455 0.051849 12.04377
3.76E-20
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 119
Table 13: Prediction Results for WPCT75: No Outliers Removed from vqhn88.sta
Predicting Values for (vqhn88.sta)
Predicting Values for (vqhn88.sta)
variable: WPCT75
variable: WPCT75
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.624455
0
0
VQ1
0.624455
0
0
Intercpt
-43.5981
Intercpt
-43.5981
Predictd
-43.5981
Predictd
-43.5981
-95.0%CL
-55.485
-95.0%PL
-70.9015
+95.0%CL
-31.7113
+95.0%PL
-16.2948
Table 14: Regression Results for WPCT90: No Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: WPCT90 (vqhn88.sta)
R= .80045652 R²= .64073064 Adjusted R²= .63655308
F(1,86)=153.37 p<.00000 Std.Error of estimate: 15.285
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-54.8014 7.391568
-7.41405
7.98E-11
VQ1
0.800457 0.064634 0.793759 0.064093 12.38445
8.14E-21
Table 15: Prediction Results for WPCT90: No Outliers Removed from vqhn88.sta
Predicting Values for (vqhn88.sta)
Predicting Values for (vqhn88.sta)
variable: WPCT90
variable: WPCT90
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.793759
0
0
VQ1
0.793759
0
0
Intercpt
-54.8014
Intercpt
-54.8014
Predictd
-54.8014
Predictd
-54.8014
-95.0%CL
-69.4954
-95.0%PL
-88.5526
+95.0%CL
-40.1075
+95.0%PL
-21.0502
Page: 120
Journal of Forensic Vocationology Vol. 7(1) Monograph
Final Results: Tables 16-33 (All Outliers Removed)
Table 16: Descriptive Statistics Results: All HMEAN Outliers Removed from vqhn88.sta
Descriptive Statistics (vqhn72.sta)
Standard
Valid N
Mean
Minimum
Maximum Std.Dev.
Error
Skewness
VQ1
72 106.7222
70
145 21.33517 2.514374 0.050029
HMEAN
72 15.77818
9.2136
25.36 5.106672 0.601827 0.331429
WPCT10
72 9.027733
5.89
15.482 2.687342 0.316706 0.592608
WPCT25
72 11.35689
6.8445
19.411 3.633217 0.428179 0.464957
HMEDIAN
72 14.81975
8.4009
25.131 5.061342 0.596485 0.405784
WPCT75
72 19.71538
10.4173
33.4 7.288025 0.858902 0.401742
WPCT90
72 25.91269
13.4773
46.8433 9.722123 1.145763 0.312846
Table 17: Regression Results for HMEAN: All HMEAN Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: HMEAN (vqhn72.sta)
R= .97269928 R²= .94614389 Adjusted R²= .94537451
F(1,70)=1229.8 p<0.0000 Std.Error of estimate: 1.1935
St. Err.
St. Err.
BETA
of BETA
B
of B
t(70)
p-level
Intercpt
-9.0689 0.722368
-12.5544
1.35E-19
VQ1
0.972699 0.027738
0.23282 0.006639 35.06793
0
Table 18: Prediction Results for HMEAN: All HMEAN Outliers Removed from vqhn88.sta
Predicting Values for (vqhn72.sta)
Predicting Values for (vqhn72.sta)
variable: HMEAN
Variable: HMEAN
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.23282 106.7222 24.84708
VQ1
0.23282 106.7222 24.84708
Intercpt
-9.0689
Intercpt
-9.0689
Predictd
15.77817
Predictd
15.77817
-95.0%CL
15.49764
-95.0%PL
13.38127
+95.0%CL
16.05871
+95.0%PL
18.17508
Table 19: Descriptive Statistics Results: All WPCT10 Outliers Removed from vqhn88.sta
Descriptive Statistics (vq10n62.sta)
Standard
Valid N
Mean
Minimum
Maximum Std.Dev.
Error
Skewness
VQ1
62 102.2581
70
145 19.52211
2.47931 0.281659
HMEAN
62 14.53629
9.2136
24.8933 4.337411 0.550852
0.52454
WPCT10
62 8.296231
5.89
13.2033 2.089095 0.265315 0.671349
WPCT25
62 10.40483
6.8445
16.81 2.920144 0.370859 0.543168
HMEDIAN
62 13.52239
8.4009
22.2767 4.141906 0.526023 0.510285
WPCT75
62 17.91649
10.4173
33.4 6.123597 0.777698 0.612809
WPCT90
62 23.70568
13.4773
46.8433 8.570035 1.088396 0.600044
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 121
Table 20: Regression Results With All WPCT10 Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: WPCT10
R= .97369152 R²= .94807517 Adjusted R²= .94720976
F(1,60)=1095.5 p<.00000 Std.Error of estimate: .47999
St. Err.
St. Err.
BETA
of BETA
B
of B
t(60)
p-level
Intercpt
-2.35869 0.327636
-7.19914
1.14E-09
VQ1
0.973692 0.029418 0.104196 0.003148 33.09859
3.04E-40
Table 21: Prediction Results With All WPCT10 Outliers Removed from vqhn88.sta
Predicting Values for (vq10n62.sta)
Predicting Values for (vq10n62.sta)
variable: WPCT10
variable: WPCT10
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.104196 102.2581 10.65493
VQ1
0.104196 102.2581 10.65493
Intercpt
-2.35869
Intercpt
-2.35869
Predictd
8.296231
Predictd
8.296231
-95.0%CL
8.174294
-95.0%PL
7.32839
+95.0%CL
8.418167
+95.0%PL
9.264072
Table 22: Descriptive Statistics Results: All WPCT25 Outliers Removed from vqhn88.sta
Descriptive Statistics (vq25n65.sta)
Standard
Valid N
Mean
Minimum
Maximum Std.Dev.
Error
Skewness
VQ1
65
103.6
70
145 20.03965 2.485612 0.194117
HMEAN
65 14.89228
9.2136
24.8933 4.542791 0.563464 0.440079
WPCT10
65 8.494946
5.89
13.2033 2.234358 0.277138 0.596825
WPCT25
65 10.66527
6.8445
16.81 3.092186 0.383538 0.474185
HMEDIAN
65 13.88674
8.4009
23.0603 4.381939 0.543513
0.45407
WPCT75
65 18.43281
10.4173
33.4 6.445447 0.799459 0.532285
WPCT90
65 24.37062
13.4773
46.8433 8.923797 1.106861 0.500962
Table 23: Regression Results With All WPCT25 Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: WPCT25
R= .97531640 R²= .95124208 Adjusted R²= .95046814
F(1,63)=1229.1 p<.00000 Std.Error of estimate: .68819
St. Err.
St. Err.
BETA
of BETA
B
of B
t(63)
p-level
Intercpt
-4.92597 0.452839
-10.878
4.24E-16
VQ1
0.975316
0.02782 0.150495 0.004293 35.05849
4.83E-43
Table 24: Prediction Results With All WPCT25 Outliers Removed from vqhn88.sta
Predicting Values for (vq25n65.sta)
Predicting Values for (vq25n65.sta)
variable: WPCT25
variable: WPCT25
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.150495
103.6 15.59124
VQ1
0.150495
103.6 15.59124
Intercpt
-4.92597
Intercpt
-4.92597
Predictd
10.66527
Predictd
10.66527
-95.0%CL
10.4947
-95.0%PL
9.279497
+95.0%CL
10.83585
+95.0%PL
12.05105
Page: 122
Journal of Forensic Vocationology Vol. 7(1) Monograph
Table 25: Descriptive Statistics Results: All HMEDIAN Outliers Removed from vqhn88.sta
Descriptive Statistics (vq50n68.sta)
Standard
Valid N
Mean
Minimum
Maximum Std.Dev.
Error
Skewness
VQ1
68 105.0882
70
145 20.81528 2.524223 0.151117
HMEAN
68 15.27591
9.2136
24.8933 4.795245 0.581509 0.394925
WPCT10
68 8.726466
5.89
14.1867 2.441013 0.296016 0.612996
WPCT25
68 10.96513
6.8445
18.315 3.337995 0.404791
0.48808
HMEDIAN
68 14.28152
8.4009
23.84 4.670555 0.566388 0.432296
WPCT75
68 18.98834
10.4173
33.4 6.824034 0.827536
0.4772
WPCT90
68 25.03844
13.4773
46.8433 9.273655 1.124596 0.410609
Table 26: Regression Results With All HMEDIAN Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: HMEDIAN
R= .97352356 R²= .94774812 Adjusted R²= .94695643
F(1,66)=1197.1 p<.00000 Std.Error of estimate: 1.0757
St. Err.
St. Err.
BETA
of BETA
B
of B
t(66)
p-level
Intercpt
-8.67399 0.676169
-12.8281
1.36E-19
VQ1
0.973524 0.028137
0.21844 0.006313 34.59931
5.04E-44
Table 27: Prediction Results With All HMEDIAN Outliers Removed from
vqhn88.sta
Predicting Values for (vq50n68.sta)
Predicting Values for (vq50n68.sta)
variable: HMEDIAN
variable: HMEDIAN
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.21844 105.0882 22.95551
VQ1
0.21844 105.0882 22.95551
Intercpt
-8.67399
Intercpt
-8.67399
Predictd
14.28152
Predictd
14.28152
-95.0%CL
14.02108
-95.0%PL
12.11811
+95.0%CL
14.54196
+95.0%PL
16.44493
Table 28: Descriptive Statistics Results: All WPCT75 Outliers Removed from vqhn88.sta
Descriptive Statistics (vq75n69.sta)
Standard
Valid N
Mean
Minimum
Maximum Std.Dev.
Error
Skewness
VQ1
69 105.4783
70
145 20.91412 2.517764 0.121374
HMEAN
69 15.39209
9.2136
24.8933 4.856707 0.584679 0.368552
WPCT10
69 8.797496
5.89
14.1867
2.4938 0.300218 0.593122
WPCT25
69 11.05597
6.8445
18.315 3.398205 0.409096 0.468029
HMEDIAN
69 14.40522
8.4009
23.84 4.748584 0.571662
0.41255
WPCT75
69 19.15482
10.4173
33.4 6.913389 0.832274 0.447047
WPCT90
69 25.22322
13.4773
46.8433 9.332305 1.123478 0.374256
Table 29: Regression Results With All WPCT75 Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: WPCT75
R= .97154723 R²= .94390402 Adjusted R²= .94306676
F(1,67)=1127.4 p<.00000 Std.Error of estimate: 1.6496
St. Err.
St. Err.
BETA
of BETA
B
of B
t(67)
p-level
Intercpt
-14.7201 1.028247
-14.3157
4.63E-22
VQ1
0.971547 0.028935 0.321156 0.009565
33.5765
1.23E-43
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 123
Table 30: Prediction Results With All WPCT75 Outliers Removed from vqhn88.sta
Predicting Values for (vq75n69.sta)
Predicting Values for (vq75n69.sta)
variable: WPCT75
variable: WPCT75
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.321156 105.4783 33.87493
VQ1
0.321156 105.4783 33.87493
Intercpt
-14.7201
Intercpt
-14.7201
Predictd
19.15482
Predictd
19.15482
-95.0%CL
18.75844
-95.0%PL
15.83847
+95.0%CL
19.5512
+95.0%PL
22.47117
Table 31: Descriptive Statistics Results: All WPCT90 Outliers Removed from vqhn88.sta
Descriptive Statistics (vq90n77.sta)
Standard
Valid N
Mean
Minimum
Maximum Std.Dev.
Error
Skewness
VQ1
77 107.1429
68
146 22.62551 2.578416 0.021595
HMEAN
77 16.06936
9.2136
27.9075 5.502208 0.627035 0.410636
WPCT10
77 9.242504
5.89
16.865 3.017558 0.343883 0.757195
WPCT25
77 11.60929
6.8445
21.265 4.003461 0.456237 0.593977
HMEDIAN
77 15.13087
8.4009
27.7175 5.486332 0.625226
0.49413
WPCT75
77 20.15281
10.4173
37.42 7.883874 0.898451 0.479637
WPCT90
77 26.36378
13.4773
46.8433 10.27203 1.170607 0.333253
Table 32: Regression Results With All WPCT90 Outliers Removed from vqhn88.sta
Regression Summary for Dependent Variable: WPCT90
R= .96636485 R²= .93386102 Adjusted R²= .93297917
F(1,75)=1059.0 p<0.0000 Std.Error of estimate: 2.6593
St. Err.
St. Err.
BETA
of BETA
B
of B
t(75)
p-level
Intercpt
-20.6432 1.475953
-13.9864
1.36E-22
VQ1
0.966365 0.029696 0.438732 0.013482 32.54191
0
Table 33: Prediction Results With All WPCT90 Outliers Removed from vqhn88.sta
Predicting Values for (vq90n77.sta)
Predicting Values for (vq90n77.sta)
variable: WPCT90
variable: WPCT90
B-Weight
B-Weight
B-Weight Value
* Value
B-Weight Value
* Value
VQ1
0.438732 107.1429 47.00699
VQ1
0.438732 107.1429 47.00699
Intercpt
-20.6432
Intercpt
-20.6432
Predictd
26.36378
Predictd
26.36378
-95.0%CL
25.76007
-95.0%PL
21.03197
+95.0%CL
26.96749
+95.0%PL
31.69559
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Hourly Mean VQ-OES Wage Estimation
The Hourly Mean distribution tends to be impacted more by extreme scores compared to
the percentile distributions. The average income is not the same as the median income.
Different values indicate the positive skew often found in wage data. The Hourly Mean
distribution started with 88 whole number VQ points. After outliers were removed, the
points were reduced to 72. Regressing the VQ points on the OES code group reported
income was initially significant. In the original distribution, Multiple R was 0.88 and
Multiple R-Squared was 0.78. The Standard Error of Estimate was $4.78. Probability was
well below 0.05. Of course the results were statistically significant after outliers were
removed. The 72 VQ points had a Multiple R of 0.97 and a Multiple R-Squared of 0.95.
The Standard Error of Estimate dropped to $1.19. The mean VQ for this distribution was
106.72, and the mean wage was $15.78 per hour.
Hourly Mean Wage Estimate = -9.068905 + (0.23282 * VQ) + error
S
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HMEAN
2
0
1
6
1
2
8
6
0
8
0
1
0
0
1
2
0
V
Q
1
1
4
0
1
6
0
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 125
10th Percentile VQ-OES Wage Estimation
The first percentile distribution studied (10th Percentile) appeared to primarily be the
distribution of people entering an occupation grouping. When people start a job, they
often are required to start at the lowest entry level, and this distribution seemed to
represent that level of employment. The distribution started with 88 whole number VQ
points. After outliers were removed, the points were reduced to 62. Regressing the VQ
points on the OES code group reported income was initially significant. In the original
distribution, Multiple R was 0.94 and Multiple R-Squared was 0.88. The Standard Error
of Estimate was $1.46. Probability was well below 0.05. Of course the results were
statistically significant after outliers were removed. The 62 VQ points had a Multiple R
of 0.97 and a Multiple R-Squared of 0.95. The Standard Error of Estimate dropped to
$0.48. This finding was consistent with previous research using Job Service Work Orders
that had a Standard Error of Estimate of around $0.50. The mean VQ for this distribution
was 102.26, and the mean wage was $8.30 per hour.
10th Percentile Wage Estimate = -2.35869 + (0.104196 * VQ) + error
S
c
a
tte
r
p
lo
t(
v
q
1
0
n
6
2
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v
*
6
2
c
)
y
=
2
.3
5
9
+
0
.1
0
4
*
x
+
e
p
s
1
4
1
3
1
2
1
1
WPCT10
1
0
9
8
7
6
5
6
0
8
0
1
0
0
1
2
0
V
Q
1
1
4
0
1
6
0
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25th Percentile VQ-OES Wage Estimation
The second percentile distribution looked like people that have worked for a job for
perhaps five years. They have some experience with the occupation, and may have
transferred from a similar occupation to the present one. Again, the distribution started
with 88 VQ data points, and the regression formula was statistically significant. After
outliers were removed, the points were reduced to 65. Regressing the VQ points on the
OES code group reported income produce an initial Multiple R of 0.82 and Multiple RSquared was 0.67. The Standard Error of Estimate was $5.20. After the outliers were
removed, the Multiple R was 0.98 and Multiple R-Squared was 0.95. The final Standard
Error of Estimate dropped to $0.69. The mean VQ for this distribution was 103.60, and
the mean wage was $10.67 per hour.
25th Percentile Wage Estimate = -4.92597 + (0.150495 * VQ) + error
S
c
a
tte
r
p
lo
t(
v
q
2
5
n
6
5
.s
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v
*
6
5
c
)
y
=
4
.9
2
6
+
0
.1
5
*
x
+
e
p
s
1
8
1
6
1
4
WPCT25
1
2
1
0
8
6
6
0
8
0
1
0
0
1
2
0
V
Q
1
1
4
0
1
6
0
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 127
50th Percentile (Median) VQ-OES Wage Estimation
This mid point of the income distribution represented those people that had considerable
work experience and had been moderately satisfactory in performing all job
requirements. The distribution started with 88 VQ data points, and the regression formula
was statistically significant. After outliers were removed, the points were reduced to 68.
Regressing the VQ points on the OES code group reported income produced an initial
Multiple R of 0.78 and a Multiple R-Squared of 0.61. The initial Standard Error of
Estimate was $9.25. After the outliers were removed, the Multiple R was 0.97 and
Multiple R-Squared was 0.95. The final Standard Error of Estimate dropped to $1.08.
The mean VQ for this distribution was 105.09, and the mean wage was $14.28 per hour.
50th Percentile Wage Estimate = -8.67399 + (0.21844 * VQ) + error
S
c
a
tte
r
p
lo
t(
v
q
5
0
n
6
8
.s
ta7
v
*
6
8
c
)
y
=
8
.6
7
4
+
0
.2
1
8
*
x
+
e
p
s
2
6
2
2
HMEDIAN
1
8
1
4
1
0
6
6
0
8
0
1
0
0
1
2
0
V
Q
1
1
4
0
1
6
0
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Journal of Forensic Vocationology Vol. 7(1) Monograph
75th Percentile VQ-OES Wage Estimation
This distribution appeared to be composed of workers that had done well in there chosen
occupations. They had marketed their talents well. Of course, the distribution started with
88 VQ data points, and the regression formula was statistically significant. After the
outliers were removed, the points were reduced to 69. Regressing the VQ points on the
OES code group reported income produced an initial Multiple R of 0.79 and a Multiple
R-Squared of 0.63. The initial Standard Error of Estimate was $12.37. After outliers were
removed, the Multiple R was 0.97 and Multiple R-Squared was 0.94. The final Standard
Error of Estimate dropped to $1.65. The mean VQ for this distribution was 105.48, and
the mean wage was $19.15 per hour.
75th Percentile Wage Estimate = -14.7201 + (0.321156 * VQ) + error
S
c
a
tte
r
p
lo
t(
v
q
7
5
n
6
9
.s
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v
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6
9
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)
y
=
1
4
.7
2
+
0
.3
2
1
*
x
+
e
p
s
3
6
3
2
2
8
WPCT75
2
4
2
0
1
6
1
2
8
6
0
8
0
1
0
0
1
2
0
V
Q
1
1
4
0
1
6
0
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Page: 129
90th Percentile VQ-OES Wage Estimation
This distribution represented the Stars in their occupations. They were top wage earners.
Of course, the variability within this distribution was also the highest. The distribution
started with 88 VQ data points, and the regression formula was statistically significant.
After outliers were removed, the points were reduced to 77. Most of the points were
retained along with their variance. Regressing the VQ points on the OES code group
reported income produced an initial Multiple R of 0.80 and a Multiple R-Squared of 0.64.
The initial Standard Error of Estimate was $15.29. After outliers were removed, the
Multiple R was 0.97 and Multiple R-Squared was 0.93. The final Standard Error of
Estimate dropped to $2.66. The mean VQ for this distribution was 107.14, and the mean
wage was $26.36 per hour.
90th Percentile Wage Estimate = -20.6432 + (0.438732 * VQ) + error
S
c
a
tte
r
p
lo
t(
v
q
9
0
n
7
7
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7
7
c
)
y
=
2
0
.6
4
3
+
0
.4
3
9
*
x
+
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p
s
5
0
4
5
4
0
3
5
WPCT90
3
0
2
5
2
0
1
5
1
0
6
0
8
0
1
0
0
1
2
0
1
4
0
1
6
0
V
Q
1
Discussion, Conclusions and Recommendations
Predictive Validity of VQ-OES Hourly Wage Estimation Formulas
All regression formulas were statistically significant at very high levels of predictive
validity with no outliers removed. The McCroskey Hahn Technique (McCroskey &
Hahn, 1998) used in removing outliers significantly improved predictive validity and
lowered standard error estimates for each of the six hourly wage estimates formulas.
Six-digit ONET Occupational Unit Classification transferable skills groups did not
represent jobs; they were collections of 9-digit DOT-Coded job titles that have similar
transferable skills. Five-digit OES groups within ONET OUC groups do not represent
jobs; they are collections of 9-digit DOT-Coded job titles that have similar transferable
Page: 130
Journal of Forensic Vocationology Vol. 7(1) Monograph
skills. Variability found in the wage distributions in this study increased as the research
moved from entry-level worker 10th Percentile wage estimates to wage estimates for the
Star occupational performers (worker wage estimates at higher Percentile levels). While
the most accurate predictions were found for entry-level positions (10th Percentile Wage
Estimates), this research significantly improved wage estimates prediction and lowered
standard error estimates for workers with increasingly higher levels of transferable skills
(Mean, 25th, 50th, 75th and 90th Percentile Wage Estimates).
Generalizing Wage Estimates using Job Specific VQ1, Geographic
Location Specific Earning Capacity Link Relatives, and Average
Inflation since 1998 (AINF) to Increase Job-Specific Predictive Validity
and Decrease the Overall SEE Associated with Aggregate OES Groups
In order for these 1998 US National OES VQ1-Wage Estimation formulas to produce the
most reasonable estimates when generalized to specific 9-digit McDOT Coded Jobs and
present value dollars, in specific state and county level geographic labor markets of
interest across time, several sources of error must be controlled.
The first well-known and well-documented source of error is the job difficulty range
within aggregate OES Code Transferable Skills Groups (i.e., jobs with higher VQ1
indices tend to pay more than those with lower VQ1 indices). This error can be controlled
by replacing OES Mean VQ1s with McDOT 2000 (McCroskey, 2000), 9-digit DOTCoded Job Specific VQ1s, in each of the six prediction formulas across all 12,775 jobs
in McDOT 2000.
Two other well-known and well-documented sources of error are yearly wage inflation
and overall average wage differences between specific geographic locations of interest.
These two sources of error can be controlled by using Average Wage Inflation since
1998 (AINF; McCroskey, 1998, 2000) and Geographic-Location-Specific Earning
Capacity Link Relatives (ECLRs, McCroskey, 1998, 2000) specifically designed to
control these types of error (McCroskey, Dennis & Dennis, 1998; McCroskey & Hahn,
1997, 1998a, 1998b). These Link Relatives must be applied to the regression formulas
identified in this study in order for wage estimates to accurately generalize to present
value dollars for specific years of interest, in specific geographic locations of interest, for
each of the 12,775 specific 9-digit DOT-Coded Occupations in McDOT 2000.
Given the broader range of estimates (six points prediction, versus two points prediction)
available from these highly reliable, highly valid wage estimation formulas and very
reasonable known error rates, it is recommended that these be incorporated within the
MVQS 2001 Windows 2000TM Program Series slated for release circa Mid-Fall, 2000.
References and Bibliography
Borland International, Inc. (1995). Borland Paradox® for Windows® and Windows NT®, Version 7.0. Scotts Valley, CA: Borland
International, Inc.
Bureau of Labor Market Performance and Information, Division of Jobs and Benefits, Florida Department of Labor and Employment
Security. (1999). Florida Program Year (PY) 1998 (07/01/98-06/30/99) Job Education Partnership (JEP) 9-Digit DOT Coded Work
Order Position Openings and Wage Data by County. Tallahassee, FL.
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 131
Bureau of Labor Statistics (1999). The 1998 Occupational Employment Statistics (OES) Survey. Occupational Employment Statistics:
OESINFO@BLS.GOV.
Daubert v. Merrell Dow Pharmaceuticals, Inc. (1993). 113 S. Ct. 2786.
Dennis, K. L. & Tichauer, G. (1998). Replicating Vocational Quotient (VQ) Wage Earning Capacity Predictions in Nebraska. Journal of
Forensic Vocationology, 4(1), pp. 85-92.
Dennis, K. L., Feldbaum, C. L. & Hahn, S. J. (2000). Louisiana State McDOT 8.0R VQ-Wage Earning Capacity Predictive Validity
Generalization. Journal of Forensic Vocationology, 6(1), pp. 37-47.
Dennis, K. L., Feldbaum, C. L. & Hahn, S. J. (2000). Louisiana State McDOT 2000 VQ1-Wage Earning Capacity Predictive Validity
Generalization. Journal of Forensic Vocationology, 6(1), pp. 49-59.
Dennis, M. L. & Dennis, K. L. (1998). Replicating Vocational Quotient (VQ) Earning Capacity Predictions In Wisconsin. Journal of
Forensic Vocationology, 4(1), pp. 61-68.
E. I. du Pont de Nemours & Co. v. Robinson, (1995). 923 S. W. 2d. At 559 (Texas).
Feldbaum, C. L. & McCroskey, B. J. (1995). Expert Testimony: Evolving Vocational and Rehabilitation Economic Technologies, Federal
Rules of Evidence and the Daubert Decision. Journal of Vocationology, 1(1), pp. 1-5.
Hahn, S. J. & Wells-Moran, J. (1998). Washington State VQ-Wage Earning Capacity Predictive Validity Generalization. Journal of
Forensic Vocationology, 4(1), pp. 79-84.
Hahn, S. J. (1997). An Independent Replication of the McCroskey Vocational Quotient (VQ) as a Predictor of Earning Capacity. Journal
of Forensic Vocationology, 3(1), pp. 29-33.
Hahn, S. J., Larkin, S. & Dennis, K. L. (2000). Florida State McDOT 2000 VQ1-Wage Earning Capacity Predictive Validity
Generalization Follow-up Study. Journal of Forensic Vocationology, 6(1), pp. 29-36.
Hahn, S. J., Larkin, S. & Williams, J. M. (2000). Florida State McDOT 1998 VQ-Wage Earning Capacity Predictive Validity
Generalization. Journal of Forensic Vocationology, 6(1), pp. 17-27.
McCroskey, B. J. & Dennis, K. L. (2002). The MVQS Rehabilitation Economist. Journal of Forensic Vocationology, Vol 7(1), Fall
Update, pp. 178-188.
McCroskey, B. J. & Feldbaum, C. L. (1995). Statistical Basics Revisited for Vocational Evaluation and Earning Capacity Analysis under
Daubert: A Need for Forensic Standards. Journal of Vocationology, 1(1), pp. 6-8.
McCroskey, B. J. & Hahn, S. J. (1997). The Vocational Quotient (VQ) as a Predictor of Earning Capacity. Journal of Vocationology, 3(1),
pp. 1-27.
McCroskey, B. J. & Hahn, S. J. (1998). The Vocational Quotient (VQ) as a Predictor of Earning Capacity: 1996-97 Criterion-Referenced
Validity Follow-up Studies. Journal of Forensic Vocationology, 4(1) 11-52.
McCroskey, B. J. & Hahn, S. J. (1998). The Vocational Quotient (VQ) as a Predictor of Earning Capacity: 1996-97 Criterion-Referenced
Follow-up Validity Studies. The Earnings Analyst, Journal of the American Rehabilitation Economics Association, 1(1), pp. 39-80.
McCroskey, B. J. (1984-2003). The MVQS Rehabilitation Economist. (A Computerized Diminished Earning Capacity Calculations
Program). Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B. J. (1998). The 1998 Encyclopedia of Job Requirements (EOJR-98). [Electronic Version within the 1998 McDOT 8.0R
Program]. Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B. J. (1998). The McCroskey Dictionary of Occupational Titles Program (McDOT). Version 8.0R: Mini-Manual and Quick
Start Guide. Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B. J. (1998). The McCroskey Dictionary of Occupational Titles Program (McDOT). Version 8.0R. Brooklyn Park, MN:
Vocationology, Inc.
McCroskey, B. J. (2000). The 2000 Encyclopedia of Job Requirements (EOJR-2000). [Electronic Version within the McDOT 2000
Program]. Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B. J. (2000). The McCroskey Dictionary of Occupational Titles Program (McDOT) Extended 5th Edition DOT Dataset,
Version 2000. Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B. J. Hahn, S. J., Dennis, K. L. (2000). MTSP VQ1-OES Aggregate & VQ1-McDOT Specific Wage Estimation. Journal
of Forensic Vocationology, 6(1), pp. 107-134.
McCroskey, B. J. Hahn, S. J., Dennis, K. L. (2002). MTSP VQ1-OES Aggregate & VQ1-McDOT Specific Wage Estimation.
Reprinted for continuity: Journal of Forensic Vocationology, 7(1), Fall Update, pp. 128-156.
McCroskey, B. J. Hahn, S. J., Dennis, K. L. & Wattenbarger, W. E. (2002). MTSP VQ1-OES Aggregate & VQ1-McDOT Specific
Wage Estimation Follow-Up Study. Journal of Forensic Vocationology, 7(1), Fall Update, pp. 157-177.
McCroskey, B. J., Dennis, M. L. & Dennis, K. L. (1998). Geographic Location and the Vocational Quotient (VQ) Prediction Of Earning
Capacity. Journal of Forensic Vocationology, 4(1), pp. 69-78.
StatSoft, Inc. (1998). STATISTICA, Kernel Release 5.1. Tulsa, OK: StatSoft, Inc.
US Department of Labor, Bureau of Employment Security. (1991a). Dictionary Of Occupational Titles (Vols. I, 4th ed. Rev.). Washington,
DC: US Government Printing Office.
US Department of Labor, Bureau of Employment Security. (1991b). Dictionary Of Occupational Titles (Vols. II, 4th ed. Rev.).
Washington, DC: US Government Printing Office.
US Department of Labor, Employment & Training Administration (US DOLETA). (1998). The Occupational Information Network-98
(O*NET-98, CD-ROM Vers. 1.0). Washington, DC: US Govt. Printing Office.
US Dept. of Commerce, Bureau of Economic Analysis and Bureau of the Census. (1972). Standard Industrial Classification Codes.
Washington, DC, US Govt. Printing Office. Revised, 1977. Revised, 1987.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
MTSP VQ1-OES Aggregate & VQ1-McDOT Specific Wage Estimation
Follow-Up Study
By
Billy J. McCroskey, Ph.D., CRE, CRC, CRV-Diplomate, ABVE-Diplomate
Steven J. Hahn, MS, CRC, CVE, QRC, CRC-Diplomate
Kenneth L. Dennis, Ph.D., LP, LMFT, CRC, CRV-Diplomate
William E. Wattenbarger, CRC, CRV-Diplomate42
Abstract
Original Study: Previous research has assessed the validity of the Vocational Quotient
(VQ) as a predictor of income. This study evaluated the ability of the VQ1 to predict
income reported by Occupational Employment Statistics (OES) groups. Linear regression
was used to predict reported income at the Mean, 10th, 25th, 50th, 75th and 90th percentile
of the 1998 OES-Wage distribution. VQ1 was used to predict to the middle of these six
distributions, and was found to be a very accurate predictor of the OES-Wage distribution
as reported by the US Department of Labor. Relative Predictive Validity (Rxy)
Coefficients were determined to be 0.970, 0.973, 0.975, 0.974, 0.972 and 0.966,
respectively. Relative Coefficients of Determination were 0.950, 0.948, 0.951, 0.948,
0.944 and 0.934, respectively. Relative Standard Errors of Estimate (SEE) were $1.19,
$0.45, $0.69, $1.08, $1.65 and $2.66 per hour, respectively. It was recommended that
specific McDOT-VQ143 Wage Prediction be used to increase overall reliability and
reduce aggregate SEEs.
Follow-up Study: This Follow-up Study extended the research in its predecessor study to
allow better predictions in the 1st three VQ-Wage Bands and allows predictions to be
extended into the 4th VQ-Wage Band, where most outliers were found and removed in the
previous study.
Literature Review
From 1996 through 1999, there were a series of predictive validity studies that used the
Vocational Quotient (VQ) to predict income. These were regression studies that used the
VQ as the independent variable and Job Service work orders or actual income of
Vocational Rehabilitation clients as the dependent variable. When the Vocational
Quotient was used as a predictor to the middle of the income distribution, it was found to
predict 80 to over 90 percent of the variance in actual income data. In 1999, the
Vocational Quotient (VQ) was grouped by O*NET code groups. Again, the VQ was
found to be an accurate predictor of Job Service work order income. This study expanded
42
Dr. Wattenbarger was added to the follow-up study authors as a result of his significant contributions in programming the
curvilinear wage prediction algorithms (from the follow-up study prediction formulas) into the MVQS 2003 MTSP 6.0
Program.
43
McCroskey Dictionary of Occupational Titles (McDOT; McCroskey, 2003) 6th Edition DOT VQ replacement data.
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 133
earlier research by grouping linking the new McDOT VQ1 (McCroskey, 2000, 2003)
mean for each Occupational Employment Statistics (OES) code groupings with the six
mean OES Wage distribution data points. The McDOT VQ1 means were then used to
predict Mean Hourly OES wages (1998 US DOL) at the Mean, 10th, 25th, 50th, 75th and
90th percentiles. Originally, only straight-line Linear Regression was used and outliers
were removed. In this Follow-Up Study, a new regression technique using 4 VQ-Wage
bands (with no outliers removed), each incorporating six-point Linear Regression
formulas, was used to mimic the overall Curvilinear Regression Prediction Plot findings.
Methodology
As indicated above, in this study, a new curvilinear regression technique using 4 VQWage bands (with no outliers removed), each incorporating six-point Linear Regression
formulas, was used to mimic the overall Curvilinear Regression Prediction Plot findings
from the first Study. Since this Follow-Up study was based on the original study and on
the same data, the references and bibliography remains the same. To keep it simple,
tabled raw data inputs and selected output results will be presented with selected scatter
plot graphs in the next section and commented on at the end of the follow-up study.
Raw Data Inputs and Prediction Results
MVQS MTSP 6th Edition Curvilinear Earning Capacity Prediction Inputs
© 2003 by Billy J. McCroskey, Ph.D.
The 1998 OES Raw Hourly Wage Means Data Across OES Group Integer VQs
OBS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
VQ1
68
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
HMEAN
$ 9.73
$ 9.21
$ 10.18
$ 9.62
$ 9.73
$ 9.70
$ 9.73
$ 10.10
$ 9.87
$ 10.03
$ 10.12
$ 10.31
$ 10.41
$ 10.62
$ 10.63
$ 10.70
$ 10.65
$ 10.62
$ 10.73
$ 10.83
$ 10.71
$ 10.83
$ 10.89
$ 11.34
$ 11.09
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
WPCT10
6.10
5.89
6.19
6.01
5.97
6.02
6.02
6.18
6.13
6.16
6.23
6.31
6.34
6.44
6.44
6.44
6.43
6.43
6.47
6.55
6.53
6.55
6.57
6.72
6.67
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
WPCT25
7.23
6.84
7.41
7.05
6.98
7.07
7.10
7.37
7.24
7.33
7.43
7.55
7.62
7.78
7.78
7.80
7.79
7.79
7.83
7.92
7.90
7.95
7.97
8.27
8.11
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
HMEDIAN
9.10
8.40
9.36
8.77
8.79
8.84
8.87
9.25
9.06
9.20
9.32
9.49
9.59
9.78
9.78
9.86
9.82
9.81
9.87
9.99
9.92
10.03
10.06
10.48
10.24
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
WPCT75
11.25
10.42
11.89
11.02
11.24
11.20
11.21
11.75
11.48
11.70
11.81
12.06
12.22
12.49
12.52
12.64
12.54
12.51
12.65
12.75
12.60
12.78
12.88
13.50
13.16
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
WPCT90
14.25
13.48
15.46
14.36
14.99
14.68
14.64
15.26
14.83
15.16
15.14
15.58
15.78
16.05
16.15
16.29
16.12
16.02
16.31
16.37
16.13
16.34
16.49
17.27
16.83
Page: 134
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
Journal of Forensic Vocationology Vol. 7(1) Monograph
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
$ 10.91
$ 11.54
$ 11.84
$ 11.99
$ 11.83
$ 11.93
$ 12.13
$ 12.46
$ 12.80
$ 13.50
$ 13.85
$ 15.12
$ 14.92
$ 15.24
$ 16.50
$ 17.19
$ 17.81
$ 17.06
$ 17.80
$ 17.35
$ 18.19
$ 18.15
$ 17.93
$ 17.96
$ 17.58
$ 18.52
$ 19.59
$ 19.00
$ 19.00
$ 19.55
$ 18.35
$ 20.40
$ 20.16
$ 21.22
$ 21.33
$ 21.47
$ 21.80
$ 22.18
$ 23.29
$ 24.27
$ 25.86
$ 24.34
$ 23.62
$ 22.03
$ 25.36
$ 24.12
$ 26.74
$ 26.76
$ 24.46
$ 22.27
$ 22.77
$ 24.89
$ 27.91
$ 32.68
$ 29.21
$ 39.77
$ 47.50
$ 6.74
$ 6.95
$ 7.05
$ 7.11
$ 7.04
$ 7.05
$ 7.21
$ 7.33
$ 7.54
$ 7.88
$ 7.91
$ 8.32
$ 8.36
$ 8.51
$ 9.03
$ 9.20
$ 9.46
$ 9.28
$ 9.71
$ 9.47
$ 9.96
$ 9.73
$ 9.74
$ 9.85
$ 9.72
$ 10.17
$ 10.49
$ 10.47
$ 10.41
$ 10.73
$ 10.40
$ 11.08
$ 11.13
$ 11.18
$ 12.40
$ 11.27
$ 12.40
$ 13.05
$ 13.63
$ 13.91
$ 15.24
$ 13.58
$ 13.00
$ 12.74
$ 15.48
$ 13.99
$ 16.50
$ 16.03
$ 14.19
$ 12.41
$ 13.02
$ 13.20
$ 16.87
$ 15.25
$ 17.87
$ 18.90
$ 19.94
$ 8.10
$ 8.50
$ 8.74
$ 8.77
$ 8.66
$ 8.70
$ 8.91
$ 9.05
$ 9.36
$ 9.86
$ 9.96
$ 10.64
$ 10.69
$ 10.83
$ 11.58
$ 11.91
$ 12.30
$ 11.98
$ 12.52
$ 12.22
$ 12.88
$ 12.64
$ 12.56
$ 12.68
$ 12.50
$ 13.15
$ 13.62
$ 13.53
$ 13.41
$ 13.90
$ 13.25
$ 14.48
$ 14.24
$ 14.44
$ 15.67
$ 14.55
$ 15.77
$ 16.32
$ 17.23
$ 17.92
$ 19.79
$ 17.51
$ 16.71
$ 16.04
$ 19.41
$ 17.75
$ 20.31
$ 20.00
$ 18.32
$ 15.89
$ 16.74
$ 16.81
$ 21.27
$ 26.82
$ 21.85
$ 33.69
$ 41.73
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
10.14
10.73
11.03
11.14
10.98
11.04
11.30
11.57
11.89
12.57
12.86
13.90
13.96
14.15
15.22
15.84
16.44
15.84
16.59
16.11
17.03
16.90
16.66
16.80
16.49
17.39
18.37
17.81
17.79
18.56
17.36
19.37
18.81
20.03
20.47
20.34
20.72
21.46
22.82
24.05
26.26
23.88
23.06
20.87
25.13
23.20
26.04
25.84
23.84
20.97
22.05
22.28
27.72
39.19
27.76
50.89
66.13
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
12.90
13.72
14.14
14.33
14.06
14.26
14.45
15.00
15.33
16.22
16.84
18.46
18.31
18.73
20.34
21.51
22.28
21.22
22.33
21.44
22.87
22.96
22.35
22.54
21.97
23.16
25.19
24.08
23.95
24.91
22.91
26.20
25.41
28.25
27.11
28.09
28.19
29.02
30.48
31.96
35.18
32.67
32.01
28.46
33.20
31.54
35.98
35.73
32.52
29.07
30.08
33.40
37.42
55.28
38.91
68.62
88.79
$ 16.31
$ 17.32
$ 17.95
$ 18.23
$ 17.94
$ 18.21
$ 18.31
$ 19.41
$ 19.86
$ 20.96
$ 22.19
$ 25.02
$ 23.98
$ 24.82
$ 27.84
$ 29.53
$ 30.86
$ 28.62
$ 30.49
$ 29.55
$ 30.97
$ 31.34
$ 30.84
$ 30.28
$ 29.61
$ 31.57
$ 34.25
$ 32.26
$ 32.15
$ 33.28
$ 30.29
$ 34.27
$ 34.16
$ 38.21
$ 35.39
$ 38.09
$ 37.09
$ 37.16
$ 37.79
$ 41.16
$ 43.94
$ 42.73
$ 41.86
$ 36.89
$ 41.42
$ 40.90
$ 44.10
$ 44.90
$ 40.46
$ 38.97
$ 37.82
$ 46.84
$ 45.60
$ 71.96
$ 51.14
$ 86.14
$ 111.88
Journal of Forensic Vocationology Vol. 7(1) Monograph
83
84
85
86
87
88
151
152
153
154
155
156
VQ Band
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
VQ1
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
$ 46.26
$ 39.16
$ 43.90
$ 49.05
$ 47.89
$ 46.15
$ 20.65
$ 18.06
$ 19.27
$ 19.05
$ 19.72
$ 18.67
$ 38.40
$ 32.07
$ 37.25
$ 45.88
$ 42.77
$ 42.02
$
$
$
$
$
$
60.86
49.28
58.09
72.71
67.77
65.81
Page: 135
$
$
$
$
$
$
80.18
66.48
78.50
99.54
91.48
90.12
$ 100.29
$ 83.54
$ 99.35
$ 126.37
$ 115.51
$ 114.75
Predictions from 4 VQ-Wage Band Curvilinear Mean Hourly Wage Formulas
HMEAN
$ 9.37
$ 9.44
$ 9.52
$ 9.60
$ 9.67
$ 9.75
$ 9.83
$ 9.90
$ 9.98
$ 10.06
$ 10.14
$ 10.21
$ 10.29
$ 10.37
$ 10.44
$ 10.52
$ 10.60
$ 10.67
$ 10.75
$ 10.83
$ 10.90
$ 10.98
$ 11.06
$ 11.13
$ 11.21
$ 11.29
$ 11.37
$ 11.44
$ 11.52
$ 11.60
$ 11.67
$ 11.75
$ 11.95
$ 12.48
$ 13.00
$ 13.53
$ 14.06
$ 14.58
$ 15.11
$ 15.64
$ 16.17
$ 16.35
$ 16.62
$ 16.89
$ 17.16
$ 17.43
$ 17.70
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
WPCT10
5.86
5.90
5.93
5.97
6.01
6.04
6.08
6.12
6.15
6.19
6.22
6.26
6.30
6.33
6.37
6.41
6.44
6.48
6.52
6.55
6.59
6.63
6.66
6.70
6.74
6.77
6.81
6.85
6.88
6.92
6.95
6.99
7.15
7.37
7.58
7.80
8.01
8.23
8.44
8.66
8.87
8.60
8.79
8.97
9.16
9.35
9.53
WPCT25
$ 6.85
$ 6.91
$ 6.97
$ 7.02
$ 7.08
$ 7.14
$ 7.19
$ 7.25
$ 7.31
$ 7.37
$ 7.42
$ 7.48
$ 7.54
$ 7.59
$ 7.65
$ 7.71
$ 7.76
$ 7.82
$ 7.88
$ 7.93
$ 7.99
$ 8.05
$ 8.10
$ 8.16
$ 8.22
$ 8.27
$ 8.33
$ 8.39
$ 8.44
$ 8.50
$ 8.56
$ 8.61
$ 8.80
$ 9.13
$ 9.45
$ 9.78
$ 10.10
$ 10.42
$ 10.75
$ 11.07
$ 11.39
$ 11.21
$ 11.44
$ 11.67
$ 11.89
$ 12.12
$ 12.35
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
HMEDIAN
8.55
8.63
8.70
8.78
8.86
8.93
9.01
9.08
9.16
9.24
9.31
9.39
9.46
9.54
9.62
9.69
9.77
9.84
9.92
10.00
10.07
10.15
10.22
10.30
10.38
10.45
10.53
10.60
10.68
10.76
10.83
10.91
11.12
11.60
12.08
12.57
13.05
13.53
14.01
14.49
14.97
15.01
15.30
15.60
15.89
16.18
16.47
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
WPCT75
10.74
10.85
10.96
11.06
11.17
11.28
11.38
11.49
11.59
11.70
11.81
11.91
12.02
12.13
12.23
12.34
12.44
12.55
12.66
12.76
12.87
12.98
13.08
13.19
13.29
13.40
13.51
13.61
13.72
13.82
13.93
14.04
14.21
14.93
15.64
16.36
17.07
17.79
18.50
19.22
19.94
20.07
20.48
20.89
21.30
21.71
22.12
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
WPCT90
14.03
14.16
14.28
14.40
14.53
14.65
14.77
14.90
15.02
15.14
15.26
15.39
15.51
15.63
15.76
15.88
16.00
16.13
16.25
16.37
16.49
16.62
16.74
16.86
16.99
17.11
17.23
17.35
17.48
17.60
17.72
17.85
18.05
19.16
20.27
21.38
22.49
23.60
24.71
25.82
26.93
28.05
28.50
28.94
29.38
29.83
30.27
Page: 136
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
Journal of Forensic Vocationology Vol. 7(1) Monograph
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
$ 17.97
$ 18.24
$ 18.51
$ 18.78
$ 19.05
$ 19.32
$ 19.59
$ 19.86
$ 20.13
$ 20.39
$ 20.66
$ 20.93
$ 21.20
$ 21.47
$ 21.74
$ 22.01
$ 22.28
$ 22.55
$ 22.82
$ 23.09
$ 23.36
$ 23.63
$ 23.90
$ 24.17
$ 24.44
$ 24.71
$ 24.98
$ 25.25
$ 25.52
$ 25.06
$ 27.26
$ 29.45
$ 31.65
$ 33.85
$ 36.04
$ 38.24
$ 40.44
$ 42.64
$ 44.83
$ 47.03
$ 49.23
$ 51.43
$ 53.62
Filename: C:Documents\VQWageNew.xls
$ 9.72
$ 9.91
$ 10.09
$ 10.28
$ 10.47
$ 10.65
$ 10.84
$ 11.03
$ 11.21
$ 11.40
$ 11.59
$ 11.77
$ 11.96
$ 12.14
$ 12.33
$ 12.52
$ 12.70
$ 12.89
$ 13.08
$ 13.26
$ 13.45
$ 13.64
$ 13.82
$ 14.01
$ 14.20
$ 14.38
$ 14.57
$ 14.76
$ 14.94
$ 14.78
$ 15.27
$ 15.76
$ 16.25
$ 16.74
$ 17.24
$ 17.73
$ 18.22
$ 18.71
$ 19.20
$ 19.69
$ 20.18
$ 20.68
$ 21.17
$ 12.57
$ 12.80
$ 13.03
$ 13.25
$ 13.48
$ 13.71
$ 13.94
$ 14.16
$ 14.39
$ 14.62
$ 14.84
$ 15.07
$ 15.30
$ 15.52
$ 15.75
$ 15.98
$ 16.20
$ 16.43
$ 16.66
$ 16.88
$ 17.11
$ 17.34
$ 17.56
$ 17.79
$ 18.02
$ 18.24
$ 18.47
$ 18.70
$ 18.92
$ 17.71
$ 20.11
$ 22.51
$ 24.91
$ 27.30
$ 29.70
$ 32.10
$ 34.50
$ 36.90
$ 39.29
$ 41.69
$ 44.09
$ 46.49
$ 48.88
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
16.76
17.06
17.35
17.64
17.93
18.22
18.52
18.81
19.10
19.39
19.68
19.98
20.27
20.56
20.85
21.14
21.44
21.73
22.02
22.31
22.60
22.89
23.19
23.48
23.77
24.06
24.35
24.65
24.94
22.80
27.08
31.37
35.65
39.93
44.22
48.50
52.79
57.07
61.36
65.64
69.92
74.21
78.49
$ 22.53
$ 22.95
$ 23.36
$ 23.77
$ 24.18
$ 24.59
$ 25.00
$ 25.41
$ 25.82
$ 26.23
$ 26.64
$ 27.05
$ 27.46
$ 27.87
$ 28.28
$ 28.69
$ 29.10
$ 29.51
$ 29.92
$ 30.33
$ 30.74
$ 31.15
$ 31.56
$ 31.97
$ 32.38
$ 32.80
$ 33.21
$ 33.62
$ 34.03
$ 31.92
$ 37.61
$ 43.30
$ 48.99
$ 54.68
$ 60.37
$ 66.06
$ 71.75
$ 77.44
$ 83.13
$ 88.82
$ 94.51
$ 100.20
$ 105.89
$ 30.71
$ 31.15
$ 31.60
$ 32.04
$ 32.48
$ 32.92
$ 33.37
$ 33.81
$ 34.25
$ 34.70
$ 35.14
$ 35.58
$ 36.02
$ 36.47
$ 36.91
$ 37.35
$ 37.80
$ 38.24
$ 38.68
$ 39.12
$ 39.57
$ 40.01
$ 40.45
$ 40.90
$ 41.34
$ 41.78
$ 42.22
$ 42.67
$ 43.11
$ 41.44
$ 48.52
$ 55.61
$ 62.69
$ 69.77
$ 76.86
$ 83.94
$ 91.02
$ 98.10
$ 105.19
$ 112.27
$ 119.35
$ 126.44
$ 133.52
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 137
Scatterplot 1: Mean Hourly VQ-Wage Bands Linear Regression Plot
Scatterplot (vqhn88.sta 8v*88c)
y=-19.812+0.346*x+eps
55
45
HMEAN
35
25
15
5
60
80
100
120
140
160
180
VQ1
Scatterplot 2: Mean Hourly VQ-Wage Bands Curvilinear Regression Plot
Scatterpl ot (vqhn88.sta 7v*88c)
y=-1247.383+65.486*x-1.339*x^2+0.013*x^3-6.59e-5*x^4+1.271e-7*x^5+eps
55
45
HMEAN
35
25
15
5
60
80
100
120
VQ1
140
160
180
Page: 138
Journal of Forensic Vocationology Vol. 7(1) Monograph
Scatterplot 3: Six-Point Mean Hourly VQ-Wage Bands Curvilinear Prediction Plots
Scatterplot 4: 10th %ile Mean Hourly VQ-Wage Band Curvilinear Prediction Plot
Journal of Forensic Vocationology Vol. 7(1) Monograph
Scatterplot 5: 25th %ile Mean Hourly VQ-Wage Band Curvilinear Plot
Scatterplot 6: 50th %ile (Median) Mean Hourly VQ-Wage Band Curvilinear Plot
Page: 139
Page: 140
Journal of Forensic Vocationology Vol. 7(1) Monograph
Scatterplot 7: Hourly Mean VQ-Wage Band Hourly Means Curvilinear Plot
Scatterplot 8: 75th %ile Mean Hourly VQ-Wage Band Curvilinear Plot
Journal of Forensic Vocationology Vol. 7(1) Monograph
Scatterplot 9: 90th %ile Mean Hourly VQ-Wage Band Curvilinear Plot
NOTES
* Wage Estimation Algorithms developed specifically for the McCroskey Transferable Skills Program 2003 (MTSP 2003; McCroskey, 2003).
Predictive Validity Estimate=0.68; Std Error of Est. = $1.01/hr. (Dennis & McCroskey, 1999, p. 71; McCroskey & Dennis, 1999, p. 32).
SINF = Simple Inflation Rates [McCroskey, 1998, p. 70; McCroskey & Hahn, 1998a, pp. 34-38; McCroskey & Hahn, 1998b, pp. 67-71].
ECLRs = Earning Capacity Link Relatives (McCroskey, 1998; McCroskey, Dennis & Dennis, 1998).
REFERENCES & BIBLIOGRAPHY
Dennis, K.L. & McCroskey, B.J. (1999). O*NET Validity Under Daubert. Journal of Forensic Vocationology (JOFV), 5(1), 49-74
McCroskey, B.J. (2000), Manual for the McCroskey Transferable Skills Program (MTSP) 2000. Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B.J. & Dennis, K.L. (1999). O*NET Issues for Rehabilitation Economists. The Earnings Analyst Journal of the
American Rehabilitation Economics Association, 2(1), 23-34.
McCroskey, B.J. & Dennis, M.L. & Dennis, K.L. (1998). Geographic Location and VQ Prediction of Earning Capacity. JOFV, 4(1), pp. 69-78.
McCroskey, B.J. (1998). The 1998-2000 ECLRBOOK. Brooklyn Park, MN: Vocationology Inc.
McCroskey, B.J. & Hahn, S.J. (1998a). The Vocational Quotient (VQ) as a Predictor of Earning Capacity: 1996-97 Criterion-Referenced
Validity Follow-up Studies. Journal of Forensic Vocationology, 4(1), 11-52.
McCroskey, B.J. & Hahn, S.J. (1998b). The Vocational Quotient (VQ) as a Predictor of Earning Capacity: 1996-97 Criterion-Referenced
Validity Follow-up Studies. The Earnings Analyst Journal of the American Rehabilitation Economics Association, 1(1), 39-80.
US Dept. of Commerce, Economics & Statistics Admin., Bureau of the Census (1987, 1990, 1992, 1993, 1994, 1995, 1996, 1997, 1998).
Selected Average Annual Pay Tables: 1987-2001 and Appendix III: Limitations of Data. In: Statistical Abstracts of the United States.
Washington, DC: Government Printing Office.
US Bureau of Labor, Occupational Employment Statistics (OES; 12/13/1999). 1998 National OES Employment and Wage Estimates.
World Wide Web Internet Website: http://stats.bls.gov./oes/national/oes#####.htm.
Filename: C:Documents\OESLPred.doc
Page: 141
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Journal of Forensic Vocationology Vol. 7(1) Monograph
The Rxy Predictive Validity Coefficients and Standard Errors of Estimate (SEE)
Across each of the VQ-Wage Bands and Across Each of the Six Predictions Points in
each VQ Band: The Mean, 10th, 25th, Median (50th), 75th, and 90th Percentiles
Six-Point Prediction Using the Overall VQ-Hourly Wage Distribution
Regression Summary for Dependent Variable: HMEAN (vqhn88.sta)
R= .88104822 R²= .77624597 Adjusted R²= .77364418
F(1,86)=298.35 p<.00000 Std.Error of estimate: 4.7814
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-19.8118
2.312267
-8.56811 3.73E-13
VQ1
0.881048
0.051008
0.346319
0.02005
17.27283 1.06E-29
Regression Summary for Dependent Variable: WPCT10 (vqhn88.sta)
R= .93842408 R²= .88063975 Adjusted R²= .87925184
F(1,86)=634.51 p<.00000 Std.Error of estimate: 1.4588
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-6.94415
0.705483
-9.84311 9.48E-16
VQ1
0.938424
0.037255
0.154092
0.006117
25.18944 1.84E-41
Regression Summary for Dependent Variable: WPCT25 (vqhn88.sta)
R= .82128465 R²= .67450848 Adjusted R²= .67072369
F(1,86)=178.22 p<.00000 Std.Error of estimate: 5.1999
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-18.2418
2.514633
-7.25425 1.66E-10
VQ1
0.821285
0.061521
0.291087
0.021805
13.34975 1.14E-22
Regression Summary for Dependent Variable: HMEDIAN
R= .78018270 R²= .60868505 Adjusted R²= .60413488
F(1,86)=133.77 p<.00000 Std.Error of estimate: 9.2486
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-30.5729
4.472578
-6.83564 1.12E-09
VQ1
0.780183
0.067455
0.448555
0.038782
11.56598 3.29E-19
Journal of Forensic Vocationology Vol. 7(1) Monograph
Regression Summary for Dependent Variable: WPCT75 (vqhn88.sta)
R= .79233195 R²= .62778992 Adjusted R²= .62346189
F(1,86)=145.05 p<.00000 Std.Error of estimate: 12.365
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-43.5981
5.979486 -7.29129
VQ1
0.792332
0.065788
0.624455
0.051849 12.04377
Regression Summary for Dependent Variable: WPCT90 (vqhn88.sta)
R= .80045652 R²= .64073064 Adjusted R²= .63655308
F(1,86)=153.37 p<.00000 Std.Error of estimate: 15.285
St. Err.
St. Err.
BETA
of BETA
B
of B
t(86)
p-level
Intercpt
-54.8014
7.391568 -7.41405
VQ1
0.800457
0.064634
0.793759
0.064093 12.38445
Six-Point Prediction Using the VQ Band1-Mean Hourly Wage Distribution
Regression Summary for Dependent Variable: HMEAN (vqhn881.sta)
R= .95169735 R²= .90572784 Adjusted R²= .90247708
F(1,29)=278.62 p<.00000 Std.Error of estimate: .23075
St. Err.
St. Err.
BETA
of BETA
B
of B
t(29)
p-level
Intercpt
4.139882
0.388883 10.64556
VQ1
0.951697
0.057015
0.076866
0.004605 16.69191
Regression Summary for Dependent Variable: WPCT10
R= .95789162 R²= .91755636 Adjusted R²= .91471348
F(1,29)=322.76 p<.00000 Std.Error of estimate: .10180
St. Err.
St. Err.
BETA
of BETA
B
of B
t(29)
p-level
Intercpt
3.378015
0.171565 19.68936
VQ1
0.957892
0.053319
0.036498
0.002032 17.9654
Regression Summary for Dependent Variable: WPCT25
R= .95400454 R²= .91012466 Adjusted R²= .90702551
F(1,29)=293.67 p<.00000 Std.Error of estimate: .16607
St. Err.
St. Err.
BETA
of BETA
B
of B
t(29)
p-level
Intercpt
2.992104
0.27988 10.69069
VQ1
0.954005
0.05567
0.056795
0.003314 17.13678
Page: 143
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Regression Summary for Dependent Variable: HMEDIAN
R= .94777740 R²= .89828201 Adjusted R²= .89477449
F(1,29)=256.10 p<.00000 Std.Error of estimate: .23800
St. Err.
St. Err.
BETA
of BETA
B
of B
t(29)
p-level
Intercpt
3.383364
0.401098 8.435253 2.7E-09
VQ1
0.947777
0.059224
0.076009
0.00475 16.00319 6.25E-16
Regression Summary for Dependent Variable: WPCT75
R= .95170406 R²= .90574061 Adjusted R²= .90249029
F(1,29)=278.66 p<.00000 Std.Error of estimate: .31888
St. Err.
St. Err.
BETA
of BETA
B
of B
t(29)
p-level
Intercpt
3.520565
0.537409 6.551003 3.56E-07
VQ1
0.951704
0.057012
0.106231
0.006364 16.69316 2.06E-16
Regression Summary for Dependent Variable: WPCT90
R= .94083033 R²= .88516172 Adjusted R²= .88120178
F(1,29)=223.53 p<.00000 Std.Error of estimate: .41214
St. Err.
St. Err.
BETA
of BETA
B
of B
t(29)
p-level
Intercpt
5.673032
0.694567 8.167726 5.25E-09
VQ1
0.94083
0.062928
0.122967
0.008225 14.95089 3.66E-15
Six-Point Prediction Using the VQ Band2-Mean Hourly Wage Distribution
Regression Summary for Dependent Variable: HMEAN (vqhn882.sta)
R= .97948677 R²= .95939434 Adjusted R²= .95359353
F(1,7)=165.39 p<.00000 Std.Error of estimate: .31762
St. Err.
St. Err.
BETA
of BETA
B
of B
t(7)
p-level
Intercpt
-40.7861
4.265817 -9.56115 2.87E-05
VQ1
0.979487
0.076163
0.527338
0.041005 12.86039 3.99E-06
Regression Summary for Dependent Variable: WPCT10
R= .98439509 R²= .96903370 Adjusted R²= .96460995
F(1,7)=219.05 p<.00000 Std.Error of estimate: .11250
St. Err.
St. Err.
BETA
of BETA
B
of B
t(7)
p-level
Intercpt
-14.3451
1.510918 -9.49426 3.01E-05
VQ1
0.984395
0.066511
0.214955
0.014524 14.80041 1.54E-06
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 145
Regression Summary for Dependent Variable: WPCT25
R= .98502886 R²= .97028185 Adjusted R²= .96603640
F(1,7)=228.55 p<.00000 Std.Error of estimate: .16594
St. Err.
St. Err.
BETA
of BETA
B
of B
t(7)
p-level
Intercpt
-23.5826
2.228627 -10.5817 1.47E-05
VQ1
0.985029
0.065157
0.32386
0.021423 15.11775 1.33E-06
Regression Summary for Dependent Variable: HMEDIAN
R= .98507331 R²= .97036942 Adjusted R²= .96613648
F(1,7)=229.24 p<.00000 Std.Error of estimate: .24635
St. Err.
St. Err.
BETA
of BETA
B
of B
t(7)
p-level
Intercpt
-37.0326
3.308625 -11.1927 1.01E-05
VQ1
0.985073
0.065061
0.481535
0.031804 15.14075 1.32E-06
Regression Summary for Dependent Variable: WPCT75
R= .98217707 R²= .96467179 Adjusted R²= .95962490
F(1,7)=191.14 p<.00000 Std.Error of estimate: .40071
St. Err.
St. Err.
BETA
of BETA
B
of B
t(7)
p-level
Intercpt
-57.3069
5.38169 -10.6485 1.41E-05
VQ1
0.982177
0.071041
0.715203
0.051731 13.82541 2.45E-06
Regression Summary for Dependent Variable: WPCT90
R= .96822823 R²= .93746590 Adjusted R²= .92853245
F(1,7)=104.94 p<.00002 Std.Error of estimate: .83974
St. Err.
St. Err.
BETA
of BETA
B
of B
t(7)
p-level
Intercpt
-93.0083
11.27809 -8.24681 7.5E-05
VQ1
0.968228
0.094517
1.110547
0.10841 10.24397 1.82E-05
Six-Point Prediction Using the VQ Band3-Mean Hourly Wage Distribution
Regression Summary for Dependent Variable: HMEAN (vqhn883.sta)
R= .92141818 R²= .84901147 Adjusted R²= .84443606
F(1,33)=185.56 p<.00000 Std.Error of estimate: 1.1831
St. Err.
St. Err.
BETA
of BETA
B
of B
t(33)
p-level
Intercpt
-13.051
2.502885
-5.2144 9.8E-06
VQ1
0.921418
0.067642
0.269725
0.019801 13.62203 4.22E-15
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Regression Summary for Dependent Variable: WPCT10
R= .90194312 R²= .81350139 Adjusted R²= .80784991
F(1,33)=143.95 p<.00000 Std.Error of estimate: .92944
St. Err.
St. Err.
BETA
of BETA
B
of B
t(33)
p-level
Intercpt
-11.7441
1.966303 -5.97266 1.05E-06
VQ1
0.901943
0.075176
0.186633
0.015556 11.99771
1.4E-13
Regression Summary for Dependent Variable: WPCT25
R= .90417437 R²= .81753129 Adjusted R²= .81200194
F(1,33)=147.85 p<.00000 Std.Error of estimate: 1.1143
St. Err.
St. Err.
BETA
of BETA
B
of B
t(33)
p-level
Intercpt
-13.5033
2.357321 -5.72824 2.15E-06
VQ1
0.904174
0.07436
0.226763
0.018649 12.15948 9.77E-14
Regression Summary for Dependent Variable: HMEDIAN
R= .90607768 R²= .82097677 Adjusted R²= .81555182
F(1,33)=151.33 p<.00000 Std.Error of estimate: 1.4182
St. Err.
St. Err.
BETA
of BETA
B
of B
t(33)
p-level
Intercpt
-16.8148
3.000214 -5.60453
3.1E-06
VQ1
0.906078
0.073654
0.291984
0.023735 12.30177 7.12E-14
Regression Summary for Dependent Variable: WPCT75
R= .91349165 R²= .83446699 Adjusted R²= .82945084
F(1,33)=166.36 p<.00000 Std.Error of estimate: 1.9013
St. Err.
St. Err.
BETA
of BETA
B
of B
t(33)
p-level
Intercpt
-24.6636
4.022271 -6.13177 6.55E-07
VQ1
0.913492
0.070825
0.410421
0.031821 12.89791 1.94E-14
Regression Summary for Dependent Variable: WPCT90
R= .90960397 R²= .82737938 Adjusted R²= .82214845
F(1,33)=158.17 p<.00000 Std.Error of estimate: 2.1037
St. Err.
St. Err.
BETA
of BETA
B
of B
t(33)
p-level
Intercpt
-20.2114
4.450472
-4.5414 7.08E-05
VQ1
0.909604
0.072325
0.4428
0.035208 12.57659 3.89E-14
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 147
Six-Point Prediction Using the VQ Band4-Mean Hourly Wage Distribution
Regression Summary for Dependent Variable: HMEAN (vqhn884.sta)
R= .89600170 R²= .80281904 Adjusted R²= .78489350
F(1,11)=44.786 p<.00003 Std.Error of estimate: 4.4295
St. Err.
St. Err.
BETA
of BETA
B
of B
t(11)
p-level
Intercpt
-291.354
49.26559
-5.91394 0.000101
VQ1
0.896002
0.133886
2.197303
0.328335
6.692258 3.41E-05
Regression Summary for Dependent Variable: WPCT10
R= .77373982 R²= .59867331 Adjusted R²= .56218906
F(1,11)=16.409 p<.00191 Std.Error of estimate: 1.6368
St. Err.
St. Err.
BETA
of BETA
B
of B
t(11)
p-level
Intercpt
-55.9965
18.20528
-3.07584 0.010549
VQ1
0.77374
0.191008
0.491488
0.121331
4.050814 0.001913
Regression Summary for Dependent Variable: WPCT25
R= .90281619 R²= .81507707 Adjusted R²= .79826589
F(1,11)=48.484 p<.00002 Std.Error of estimate: 4.6454
St. Err.
St. Err.
BETA
of BETA
B
of B
t(11)
p-level
Intercpt
-327.548
51.66698
-6.33961 5.52E-05
VQ1
0.902816
0.129658
2.397657
0.344339
6.963063 2.38E-05
Regression Summary for Dependent Variable: HMEDIAN
R= .89664712 R²= .80397607 Adjusted R²= .78615571
F(1,11)=45.116 p<.00003 Std.Error of estimate: 8.6048
St. Err.
St. Err.
BETA
of BETA
B
of B
t(11)
p-level
Intercpt
-594.124
95.7041
-6.20793 6.64E-05
VQ1
0.896647
0.133493
4.284179
0.637829
6.716814 3.3E-05
Regression Summary for Dependent Variable: WPCT75
R= .90093160 R²= .81167774 Adjusted R²= .79455754
F(1,11)=47.411 p<.00003 Std.Error of estimate: 11.147
St. Err.
St. Err.
BETA
of BETA
B
of B
t(11)
p-level
Intercpt
-787.367
123.9838
-6.35057 5.44E-05
VQ1
0.900932
0.130844
5.689523
0.826301
6.885529 2.64E-05
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Journal of Forensic Vocationology Vol. 7(1) Monograph
Regression Summary for Dependent Variable: WPCT90
R= .90173462 R²= .81312532 Adjusted R²= .79613671
F(1,11)=47.863 p<.00003 Std.Error of estimate: 13.812
St. Err.
St. Err.
BETA
of BETA
B
of B
t(11)
p-level
Intercpt
-978.531
153.6213 -6.36976
5.3E-05
VQ1
0.901735
0.13034
7.083128
1.023824 6.918307 2.53E-05
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 149
Table 4: Quick Reference Summary of MVQS 6.0 Predictive Validity (R xy) & Known Error Rates (SEE)
Table 4: Quick Reference Summary of MVQS 6.0 Predictive Validity (Rxy) & Known Error Rates (SEE)
Across All VQ Bands
Mean Estimates:
10th %ile Estimates:
25th %ile Estimates:
Median Estimates:
75th %ile Estimates:
90th %ile Estimates:
VQ Band 1
Mean Estimates:
10th %ile Estimates:
25th %ile Estimates:
Median Estimates:
75th %ile Estimates:
90th %ile Estimates:
VQ Band 2
Mean Estimates:
10th %ile Estimates:
25th %ile Estimates:
Median Estimates:
75th %ile Estimates:
90th %ile Estimates:
VQ Band 3
Mean Estimates:
10th %ile Estimates:
25th %ile Estimates:
Median Estimates:
75th %ile Estimates:
90th %ile Estimates:
VQ Band 4
Mean Estimates:
10th %ile Estimates:
25th %ile Estimates:
Median Estimates:
75th %ile Estimates:
90th %ile Estimates:
VQ Range
VQ Band
68 -158
68 -158
68 -158
68 -158
68 -158
68 -158
Across All 4 VQ Bands
Across All 4 VQ Bands
Across All 4 VQ Bands
Across All 4 VQ Bands
Across All 4 VQ Bands
Across All 4 VQ Bands
VQ Range
VQ Band
68 -100
68 -100
68 -100
68 -100
68 -100
68 -100
Below Avg - Mid-Avg VQs
Below Avg - Mid-Avg VQs
Below Avg - Mid-Avg VQs
Below Avg - Mid-Avg VQs
Below Avg - Mid-Avg VQs
Below Avg - Mid-Avg VQs
VQ Range
VQ Band
100 - 109
100 - 109
100 - 109
100 - 109
100 - 109
100 - 109
Mid-Avg - High-Avg VQs
Mid-Avg - High-Avg VQs
Mid-Avg - High-Avg VQs
Mid-Avg - High-Avg VQs
Mid-Avg - High-Avg VQs
Mid-Avg - High-Avg VQs
VQ Range
VQ Band
109 - 144
109 - 144
109 - 144
109 - 144
109 - 144
109 - 144
High-Avg - Very-High VQs
High-Avg - Very-High VQs
High-Avg - Very-High VQs
High-Avg - Very-High VQs
High-Avg - Very-High VQs
High-Avg - Very-High VQs
VQ Range
VQ Band
144 - 158
144 - 158
144 - 158
144 - 158
144 - 158
144 - 158
Extremely High VQs
Extremely High VQs
Extremely High VQs
Extremely High VQs
Extremely High VQs
Extremely High VQs
© 2002 by Billy J. McCroskey, Ph.D.
%ile
Band
1st - 100th
1st - 100th
1st - 100th
1st - 100th
1st - 100th
1st - 100th
%ile
Band
1st - 50th
1st - 50th
1st - 50th
1st - 50th
1st - 50th
1st - 50th
%ile
Band
50th - 67th
50th - 67th
50th - 67th
50th - 67th
50th - 67th
50th - 67th
%ile
Band
67th - 99th
67th - 99th
67th - 99th
67th - 99th
67th - 99th
67th - 99th
%ile
Band
99th - 100th
99th - 100th
99th - 100th
99th - 100th
99th - 100th
99th - 100th
% Jobs
in Range
Rxy
SEE
100
100
100
100
100
100
0.8810
0.9384
0.8213
0.7802
0.7923
0.8005
$ 4.78
$ 1.46
$ 5.20
$ 9.25
$ 12.37
$ 15.29
% Jobs
in Range
Rxy
50
50
50
50
50
50
0.9517
0.9579
0.9540
0.8948
0.9517
0.9408
% Jobs
in Range
Rxy
17
17
17
17
17
17
0.9795
0.9844
0.9850
0.9851
0.9823
0.9682
% Jobs
in Range
Rxy
32
32
32
32
32
32
0.9214
0.9019
0.9042
0.9061
0.9134
0.9096
SEE
$
$
$
$
$
$
0.23
0.10
0.17
0.24
0.32
0.41
SEE
$
$
$
$
$
$
0.32
0.11
0.17
0.25
0.40
0.84
SEE
$
$
$
$
$
$
1.18
0.93
1.11
1.42
1.90
2.10
% Jobs
in Range
Rxy
SEE
1
1
1
1
1
1
0.8960
0.7737
0.9028
0.8966
0.9009
0.9017
$ 4.43
$ 1.64
$ 4.65
$ 8.60
$ 11.15
$ 13.81
Page: 150
Journal of Forensic Vocationology Vol. 7(1) Monograph
Discussion, Conclusions and Recommendations
Predictive Validity of VQ-OES Hourly Wage 4 VQ-Wage Band Six-Point Hourly
Wage Estimation Formulas
As in the original study, all regression formulas were statistically significant at very high
levels of predictive validity, with no outliers removed. The McCroskey Hahn Technique
(McCroskey & Hahn, 1998) was used in the original study for removing outliers
significantly improved predictive validity and lowered standard error estimates for each
of the six wage estimates formulas. In this follow-up study, outliers were retained for
better, more complete, hourly wage estimate predictions based on the 4 VQ-Band Mean
Hourly Wage six-point curvilinear hourly wage prediction estimate formulas.
Six-digit Occupational Information Network (O*NET) Occupational Unit Classification
(OUC) Occupational Employment Statistics (OES) transferable skills subgroups did not
represent jobs; they were groupings of 9-digit DOT-Coded job titles that have similar
transferable skills. They have now been replaced by even fewer 8-digit ONET OES
Standard Occupational Code (SOC) subgroups, which continue to have the similar
aggregate data problem.
The variability found in the wage distributions, in both the original and follow-up studies,
increased as the research moved from entry-level worker 10th Percentile wage estimates
to wage estimates for the Star occupational performers (worker wage estimates at higher
Percentile levels). The most accurate predictions were originally found for entry-level
positions (10th Percentile Wage Estimates) in both studies. Results from the follow-up
study research, using 4 VQ-Wage bands (with no outliers removed), each incorporating
six-point Linear Regression formulas, to mimic the VQ-Hourly Wage Curvilinear
Regression Prediction Plot findings, significantly improved wage estimate predictions
relative to the original study, and lowered standard error estimates of VQ-Hourly Wage
predictions for entry-level workers at the 10th Percentile, as well as, increasingly higher
levels of transferable skills (Mean, 25th, 50th, 75th and 90th Percentile Wage Estimates).
Generalizing Wage Estimates using Job Specific VQ1, Geographic Location Specific
Earning Capacity Link Relatives, and Average Inflation since 1998 (AINF) to
Increase Job-Specific Predictive Validity and Decrease the Overall SEE Associated
with Aggregate OES Groups
For the 1998 US National O*NET-98 OES VQ-Hourly Wage Estimation formulas to
produce the most reasonable estimates when generalized to specific 9-digit McDOTCoded Jobs and present value dollars, in specific state and county level geographic labor
markets of interest across time, several sources of known error must be controlled.
The first known and well-documented source of error is the job difficulty range within
aggregate O*NET-98 OES Code Transferable Skills Groups (i.e., jobs with higher VQ
indices tend to pay more than those with lower VQ indices, and tend to have more known
error). This error can best be controlled by replacing OES Mean VQs with 9-digit
McDOT-Coded Job Specific VQs using the new 4 VQ Band, six-point curvilinear hourly
Journal of Forensic Vocationology Vol. 7(1) Monograph
Page: 151
wage prediction formulas across all 12,974 jobs in McCroskey Transferable Skills
Program (MVQS MTSP 6.0; McCroskey, 2003).
Two other well-known and well-documented sources of error are yearly wage inflation
and average wage differences between specific geographic locations of interest. These
two sources of error can be controlled by using Average Wage Inflation since 1998
(AINF; McCroskey, 1998, 2000, 2002) and Geographic-Location-Specific Earning
Capacity Link Relatives (ECLRs, McCroskey, 1998, 2000, 2002) specifically designed
to control these types of error (McCroskey, Dennis & Dennis, 1998; McCroskey & Hahn,
1997, 1998a, 1998b, and McCroskey, Hahn, Dennis & Wattenbarger, 2002). These Link
Relatives must be applied to the 24 new curvilinear regression formulas (4 VQ Bands
each with 6 formulas) identified in this follow-up study, in order for wage estimates to
accurately generalize to present value dollars for specific years of interest, in specific
geographic locations of interest, for each of the 12,974 specific 9-digit DOT-Coded
Occupations in McDOT 2003.
The 4 VQ-Wage Bands with six hourly wage prediction formulas model the curvilinear
wage distribution of the entire VQ-Wage Range. Each of the 6 VQ-Wage prediction
distributions was highly reliable. The development of these 24 new, reliable prediction
distributions produce wage estimation formulas with very reasonable known error rates.
This research documents that these formulas should be incorporated in the MVQS 2003,
Windows 2000TM Program Series. These programs are slated for release circa Mid-Fall,
2002, after completion of the recommended Curvilinear Model Earning Capacity
Prediction programming updates in the MVQS 2003 MTSP 6.0 Program.
References and Bibliography
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International, Inc.
Bureau of Labor Market Performance and Information, Division of Jobs and Benefits, Florida Department of Labor and Employment
Security. (1999). Florida Program Year (PY) 1998 (07/01/98-06/30/99) Job Education Partnership (JEP) 9-Digit DOT Coded Work
Order Position Openings and Wage Data by County. Tallahassee, FL.
Bureau of Labor Statistics (1999). The 1998 Occupational Employment Statistics (OES) Survey. Occupational Employment Statistics:
OESINFO@BLS.GOV.
Daubert v. Merrell Dow Pharmaceuticals, Inc. (1993). 113 S. Ct. 2786.
Dennis, K. L. & Tichauer, G. (1998). Replicating Vocational Quotient (VQ) Wage Earning Capacity Predictions in Nebraska. Journal of
Forensic Vocationology, 4(1), pp. 85-92.
Dennis, K. L., Feldbaum, C. L. & Hahn, S. J. (2000). Louisiana State McDOT 8.0R VQ-Wage Earning Capacity Predictive Validity
Generalization. Journal of Forensic Vocationology, 6(1), pp. 37-47.
Dennis, K. L., Feldbaum, C. L. & Hahn, S. J. (2000). Louisiana State McDOT 2000 VQ1-Wage Earning Capacity Predictive Validity
Generalization. Journal of Forensic Vocationology, 6(1), pp. 49-59.
Dennis, M. L. & Dennis, K. L. (1998). Replicating Vocational Quotient (VQ) Earning Capacity Predictions In Wisconsin. Journal of
Forensic Vocationology, 4(1), pp. 61-68.
E. I. du Pont de Nemours & Co. v. Robinson, (1995). 923 S. W. 2d. At 559 (Texas).
Feldbaum, C. L. & McCroskey, B. J. (1995). Expert Testimony: Evolving Vocational and Rehabilitation Economic Technologies, Federal
Rules of Evidence and the Daubert Decision. Journal of Vocationology, 1(1), pp. 1-5.
Hahn, S. J. & Wells-Moran, J. (1998). Washington State VQ-Wage Earning Capacity Predictive Validity Generalization. Journal of
Forensic Vocationology, 4(1), pp. 79-84.
Hahn, S. J. (1997). An Independent Replication of the McCroskey Vocational Quotient (VQ) as a Predictor of Earning Capacity. Journal
of Forensic Vocationology, 3(1), pp. 29-33.
Hahn, S. J., Larkin, S. & Dennis, K. L. (2000). Florida State McDOT 2000 VQ1-Wage Earning Capacity Predictive Validity
Generalization Follow-up Study. Journal of Forensic Vocationology, 6(1), pp. 29-36.
Hahn, S. J., Larkin, S. & Williams, J. M. (2000). Florida State McDOT 1998 VQ-Wage Earning Capacity Predictive Validity
Generalization. Journal of Forensic Vocationology, 6(1), pp. 17-27.
McCroskey, B. J. & Dennis, K. L. (1998). O*Net Issues for Rehabilitation Economists. The Earnings Analyst (TEA), Journal of the
American Rehabilitation Economics Association (AREA) Vol 2, pp. 23-34.
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Journal of Forensic Vocationology Vol. 7(1) Monograph
McCroskey, B. J. & Dennis, K. L. (2002). The MVQS Rehabilitation Economist. Journal of Forensic Vocationology, Vol 7(1), Fall
Update, pp. 178-188.
McCroskey, B. J. & Feldbaum, C. L. (1995). Statistical Basics Revisited for Vocational Evaluation and Earning Capacity Analysis under
Daubert: A Need for Forensic Standards. Journal of Vocationology, 1(1), pp. 6-8.
McCroskey, B. J. & Hahn, S. J. (1997). The Vocational Quotient (VQ) as a Predictor of Earning Capacity. Journal of Vocationology, 3(1),
pp. 1-27.
McCroskey, B. J. & Hahn, S. J. (1998). The Vocational Quotient (VQ) as a Predictor of Earning Capacity: 1996-97 Criterion-Referenced
Validity Follow-up Studies. Journal of Forensic Vocationology, 4(1) 11-52.
McCroskey, B. J. & Hahn, S. J. (1998). The Vocational Quotient (VQ) as a Predictor of Earning Capacity: 1996-97 Criterion-Referenced
Follow-up Validity Studies. The Earnings Analyst, Journal of the American Rehabilitation Economics Association, 1(1), pp. 39-80.
McCroskey, B. J. (1984-2003). The MVQS Rehabilitation Economist. (A Diminished Earning Capacity Calculations Program).
Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B. J. (1998). The 1998 Encyclopedia of Job Requirements (EOJR-98). [Electronic Version within the 1998 McDOT 8.0R
Program]. Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B. J. (1998). The McCroskey Dictionary of Occupational Titles Program (McDOT). Version 8.0R: Mini-Manual and Quick
Start Guide. Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B. J. (1998). The McCroskey Dictionary of Occupational Titles Program (McDOT). Version 8.0R. Brooklyn Park, MN:
Vocationology, Inc.
McCroskey, B. J. (2000). The 2000 Encyclopedia of Job Requirements (EOJR-2000). [Electronic Version within the McDOT 2000
Program]. Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B. J. (2000). The McCroskey Dictionary of Occupational Titles Program (McDOT) Extended 5th Edition DOT Dataset,
Version 2000. Brooklyn Park, MN: Vocationology, Inc.
McCroskey, B. J. Hahn, S. J., Dennis, K. L. (2000). MTSP VQ1-OES Aggregate & VQ1-McDOT Specific Wage Estimation. Journal
of Forensic Vocationology, 6(1), pp. 107-134.
McCroskey, B. J. Hahn, S. J., Dennis, K. L. (2002). MTSP VQ1-OES Aggregate & VQ1-McDOT Specific Wage Estimation.
Reprinted for continuity: Journal of Forensic Vocationology, 7(1), Fall Update, pp. 128-156.
McCroskey, B. J. Hahn, S. J., Dennis, K. L. & Wattenbarger, W. E. (2002). MTSP VQ1-OES Aggregate & VQ1-McDOT Specific
Wage Estimation Follow-Up Study. Journal of Forensic Vocationology, 7(1), Fall Update, pp. 157-177.
McCroskey, B. J., Dennis, M. L. & Dennis, K. L. (1998). Geographic Location and the Vocational Quotient (VQ) Prediction Of Earning
Capacity. Journal of Forensic Vocationology, 4(1), pp. 69-78.
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US Department of Labor, Bureau of Employment Security. (1991a). Dictionary Of Occupational Titles (Vols. I, 4th ed. Rev.). Washington,
DC: US Government Printing Office.
US Department of Labor, Bureau of Employment Security. (1991b). Dictionary Of Occupational Titles (Vols. II, 4th ed. Rev.).
Washington, DC: US Government Printing Office.
US Department of Labor, Employment & Training Administration (US DOLETA). (1998). The Occupational Information Network-98
(O*NET-98, CD-ROM Vers. 1.0). Washington, DC: US Govt. Printing Office.
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Additional Resources
1)
Journals of Forensic Vocationology, Vol 1(1), (1995) through Vol 7(1), (2002) for numerous Scientific
Research Studies by various authors on Inter-Rater Reliability, Validity, Known Error Rates and Theoretical
Foundations underpinning MVQS Program inputs and outputs.
2)
McCroskey & Dennis (2000). Course Disks 1 - 4 for information on the development and underpinning of
VDARE MVQS Programs Theory, the ONET, the Updating the 5th ed. DOT, MVQS Vocational Analysis,
Vocational Evaluation, the impact of Daubert, Kumho & Kinneman on Expert Testimony, VDARE, MVQS,
Inter-Reliability, Validity, & Known Error Rates. Published by Vocationology, Inc., 1809 Halifax Court
North, Brooklyn Park, MN 55443, phone: 763-569-0680.