Throughout, ALLCAPS indicates the name of a variable in the dataset

NSSE-AAUDE Data 2000-2012 User Guide

University of Colorado Boulder

Page 1

For collection years 2000-2012 = NSSE v.1. It has NOT been modified to accommodate version

2 2013+. Instead, use the NEW user guide. Use this only for reference when using v1 in v1 format. Info here relevant to v2 has been carried over to the new user guide, which also deals with using v1 data in v2 format.

5/2014, PBA file L:\IR\survey\NSSE\AAUDE\All\archive\UserGuide_v1.docx

Nothing below this point has been modified.

This guide is for the NSSE-AAUDE data, most especially what we call the all-years dataset , with response-level data including NSSE benchmarks and CU-Boulder scales. See







 http://www.colorado.edu/pba/surveys/nsse-aaude/index.htm

for a description of the overall data sharing arrangements and links to the pass-word protected site, public data, and the separate documents noted here.





Other files of special interest to data users

 An Excel variable list (which contains the NSSE item number, a column indicating whether the variable was computed either by NSSE or CU-Boulder, and information on variables changing over time)

C odebooks and links to instruments

An SAS dataset of basic statistics (linked to from the password-protected page ) has means and standard deviations, by year of administration (2000-12), institution, and class rank. These statistics

are computed only for the random sample (described below), only for variables for which a mean is

appropriate (e.g., those having ordinal-level scales), and items that were administered for more than one year (unless it was administered in the most recent year). Note that this file used to be in an Excel file in a very wide format, with NSSE items displayed in columns. In 2009 we changed the structure so that NSSE items are displayed in rows and the stats of N, Mean and StdDev as columns. We also flipped this file from an Excel file to a SAS dataset.

 Note that a missing value of .A for the YEAR variable in this dataset denotes all years combined.

SAS formats prepared by CU-Boulder. Click here for the SAS code.

Response rate Excel with summary and plot. The same data are in the institutional metadata dataset, which is behind the password. It’s described at the end of this document.

Institutional population and response sample dataset, which is behind the password.

NSSE now publishes a guide to multi-year analysis. http://nsse.iub.edu/pdf/2008_Institutional_Report/NSSE%20Multi-

Year%20Data%20Analysis%20Guide.pdf

Most of it is pretty rudimentary, and much is unnecessary if using the AAUDE longitudinal dataset, but there are some good tips on using effect sizes, using NSSE weights over time, etc.

Posted from http://www.colorado.edu/pba/surveys/nsse-aaude/index.htm

PBA: L:\IR\survey\NSSE\AAUDE\All\UserGuide.doc 4/16/2020



Page 2

Contents of the User Guide



Observations



Key Variables



Notes on variable names and attributes







Experimental questions

NSSE Benchmarks of Effective Educational Practice

Voluntary System of Accountability (VSA) Items (added 12/08)







Weights

Colorado Scales

File format







Institutional Metadata (added 12/08)

History – year to year changes in the longitudinal data file through 2012

University of Colorado at Boulder file refs

Throughout this document, ALLCAPS indicates the name of a variable in the dataset

Observations

 240,560 total. This is one per student participant at all participating and sharing AAUDE institutions from 2000-2012

 Except Stony Brook 2000 and 2001 participants, which have not been sent to the AAUDE rep by NSSE.

 McGill provided us with their 2008 and 2011 data.

Click here for a table that shows the distribution of responses by institution, part of a random sample or not, and year.

Key Variables

 INST = institution name. No missing. Relatively short text.







Plus variable IPEDS, the 6-digit IPEDS code.

IPEDS codes were incorrect for most institutions in the NSSE 2000 data. We have corrected.

Added in November, 2009. Target institution variable (TRGTINST) to be used for analysis.



Currently set to 0 for all rows. Set your institution to 1.

Added in November, 2010. Public institution variable (PUBLIC). 0=private, 1=public.

 YEAR = Survey year. No missing. YYYY. Contains 2000, 2001, 2002, 2003, 2004, 2005, 2006,

2007, 2008, 2009, 2010, 2011 and 2012. Numeric.









CLASSRAN = frosh (1), sophomore (2), junior (3) or senior group (4). No missing. Numeric (it’s

NSSE’s fault)

 There are both frosh and seniors for every institution for every year participating.

 This var is NOT 1-1 with the var CLASS, which is the student's self-reported class rank. For example, the "frosh" group will include students with sophomore standing from AP, IB, college work during HS, etc.

Throughout, the N of frosh is about the same as N of seniors.

Sophomore and juniors account for only about 1% of all respondents.

This variable is now near the front of the dataset.



NSSE-AAUDE Data 2000-2012 User Guide Page 3


 COMPARE = 0/1 (zero/one) variable where 1 = NSSE random sample from eligible first-year or senior populations (e.g., NSSE's "base random sample," "standard random oversample," or

"requested random oversample" for 2004 and later years), and 0 = not part of a NSSE random sample (may reflect institutional oversampling by college, major, demographic group, etc.) *or* part of the random sample but not in the eligible population. This variable was added January 2009, replacing RANSMP, which was added June 2008.

 Use COMPARE = 1 for across-institution comparisons unless you're absolutely sure what you're doing.



This is a function of the variables SMPL00, SMPL01, and SMPL05, and of the variables INELIG and

CLASSRAN.



For 2000 and earlier years, COMPARE is defined as SMPL00 having a value of either 1 (paper questionnaire)

 or 2 (Web questionnaire), and CLASSRAN having a value of 1 (first-year) or 4 (senior).

For 2001-2004, COMPARE is defined as SMPL01 having a value of 1 (contributes to national norm) or 2

(random oversample), and CLASSRAN having a value of 1 (first-year) or 4 (senior).



For 2005 and later years, COMPARE is defined as SMPL05 having a value of 1 (base random sample), 2

(standard random oversample), or 3 (requested random oversample), CLASSRAN having a value of 1 (firstyear) or 4 (senior), and INELIG having a value of 1 (meaning eligible; 2 = ineligible).



INELIG. Variable was added to NSSE datasets in 2005. When INELIG = 2, it excludes respondents









 who were random-sampled but proved to be ineligible, usually because they’d ended enrollment, or were not properly in the first-year or senior populations. Added by Colorado to COMPARE calculation 1/09 on the advice of NSSE after discovering mismatches between NSSE metadata and AAUDE responselevel data.

CLASSRAN 1 or 4. Added by Colorado to COMPARE calculation 1/09 on NSSE advice. This condition excludes cases mismarked by Texas 2008 but not marked by NSSE as INELIG.

In 2009 CU-Boulder was in both AAUDE and writing consortia. NSSE goofed administration and ended up with complete census. We have set SMPL05 to 1, and COMPARE to 1, for all cases for CU-Boulder for 2009.

See NSSE Responses by Institution and Year for institutional counts of respondents over time in both the random sample from eligible first-year and senior population (COMPARE = 1) and non-comparison (COMPARE= 0) group.

COMPARE replaces RANSMP Jan ’09. RANSMP replaced the former variable BASE in June ’08 . BASE was discovered in January 2008 to be incorrectly defined, in that it excluded some students who were in fact randomly selected (e.g., students in the standard and requested random oversamples for 2004 and later years). BASE is still in the all-years dataset, but it has been renamed to BASE_OLD.

Summary of changes. BASE identified too few; RANSMP identified too many; use variable COMPARE=1 for all across-institution and across-time comparisons unless you’re after particular non-standard populations.





AAUDEQ = 0/1 (zero, one) variable where 1 = respondent answered at least one AAUDE question. 0 = did not.



We calculated this by checking N of valid responses to the AAUDE questions, but it's also a logical function of

CONSORTQ (variable last seen in 2004).





In 2004, a new value of 9 was assigned to certain AAUDE questions for a response of N/A. We assigned a missing value (.N) for these responses so that the mean and other statistics would not be affected by this 9 value.

In 2009, a new missing value (.W) on all AAUDE questions to indicate Colorado students who got writing consortium questions. N is 1,382.

MODECOMP = web (2) or paper (1). No missing.

 Percentage of responses from web: 68% in 2000, 70% in 2001, 76% in 2002, 82% in 2003,

99% in 2004-2008, 91% in 2009, 100% in 2010 and 2011, 94% in 2012.





 Other potentially important demographic variables.

 ENTER - started college at this institution (1) or another (2). Only 14% said another.

 ENRLMENT- part (1) or full (2) time. Only 5% part time.

 Major: For 2003 and later students were asked to write in their primary (MAJRPRIM) and second (MAJRSECD) major. These responses were then coded into another variable

(MAJRPCOD for primary major and MAJRSCOD for second major) that contained a list of 85 majors. The translations of majors to major codes are contained in the NSSE codebooks. For example, major codes are on p. 11 of the 2007 codebook . For 2001 and 2002 the respondents chose from a list for their primary major (MAPRIM) and second major (MASECOND). In

2000 there was a yes/no variable representing each major (e.g., MAMATH). A student could check every major and it would be unknown what his primary major was. There is no variable for institution-reported major.

 CIP#PRIM and CIP#SECD (where # is 1, 2, and 3) are six associated CIP code variables added in 11/09 that were derived from the primary and second major codes. Caution: some of the CIP codes are fake. 88.xxxx means no suitable CIP code available. An Excel file



 created by Mary Black and Linda Lorenz shows the assignments . Changes from CIP2000 to CIP2010 were made in 11/10.

DISCPRIM and DISCSECD are NSSE major field descriptions based on the NSSE primary and second major field codes. These variables use the FIELD format (e.g., code of 1 = Arts and Humanities).

Student-reported variables versus institution-reported variables.

 GENDER is the institution-reported variable and SEX is the student-reported variable. These variables are both coded 1 = Male, 2 = Female. They differ for only 0.2% of the cases.

 CLASSRAN and CLASS are mentioned above.

 Variables on race/ethnicity. Institution-reported vs. student-reported.

 ETHNICIT is the institution-reported variable. It has values 1-9 and blank; see codebook.

Multiracial and Other are options. No change over time.

 Prior to 2005, the student-reported variables are in a different format to allow for students’ reporting multiple ethnicities. For example, a student could say yes to being Asian

(REASIAN) and African-American (REAFRAM). The RE variables (amind, asian, afram,







 white, mexam, puert, othis (other Hispanic), othr1) are all coded 1=yes, blank/missing.

RELATINO (removed in 2005) is a separate question, with 1=no=90%, 2=yes=6%, and 5% not answered.

In 2005, the student-reported variable (RACE05) switched format to students’ being able to select only one ethnicity. Multiracial and Other are options.

The match between the student-reported and institution-reported vars is not great.

MULTRE is a calculated variable based on the student-reported variables to designate multiracial (1=no, 85%, 2=yes, 3%). If the student answered yes to one or more of the RE variables (or selected Multiracial in 2005 or later) then MULTRE is coded to 2 as being yes.

INTERNAT (international) is a student-reported variable, introduced in 2001.

Notes on variable names and attributes

 Variable names were not changed from what NSSE assigned them to be.

 If a question did change from one year to the next then the variable has a 2-digit year on the end of the name to denote the first year that that form of the question was used.









 For instance the variable ACADPREP was changed from 2000 to 2001 (different response values). For 2000 the variable name is ACADPR00 and for 2001 the variable name is

ACADPR01. For 2002, the variable name is still ACADPR01 since the 2002 question did not

 change from 2001.

Variables calculated by Colorado have upper/lower case names and have names no longer than 8 characters. And have -- (2 dashes) at the start of the label.

The label is a short version of the question itself.

AAU questions in earlier datasets originally had the year between AAU and the question number









(i.e. AAU0010). If the AAU question did not change over years then what was AAU0010 has become AAU10.

AAU questions 5 and 6 are about course availability. #5 is on courses for the major, #6 is on courses for general education requirements. Response alternatives have changed over time due to a

NSSE error in 2002 that made paper and web versions different. We have resolved as follows.

 Variables AAU05 and AAU06 have data from 2000, 2001, and 2002 (paper version only).



There are three response alternatives.

 1 = Hardly ever

 2 = Some of the time

 3 = Most of the time

Variables AAU0205 and AAU0206 have data from 2002 (web version only), 2003 and each year thereafter. (These were named something different by NSSE in the 2002 datasets; we have dropped the NSSE-named 2002 variables.) There are four response alternatives.





1 = Hardly ever

2 = Some of the time

 3 = Most of the time

 4 = Always or nearly always

 No cases have data on both sets of variables.

All variables from NSSE are numeric except obvious comments/write-ins, institution name, student

ID, date of birth, and zip code, even though many are not even monotonic.

Variables that are Colorado recodes of NSSE variables all end in “r” and say RECODED in the label. A recode variable switches around the order of responses of the original variable. For instance, MEMORIZR is a recode of MEMORIZE. A value of ‘1’ for MEMORIZE would have a value of ‘4’ in MEMORIZR. A value of ‘2’ would be recoded as ‘3’, and so on. Recodes are used in scales so that the higher numbers are better.

Some variables were renamed from 2002 due to changes in the 2003 survey or to lower variable name lengths down to 8 characters. Below is a list of these variables with their old name and new name.

 GRADES = GRADES02 (for 02), GRADES03 (for 03)







INDSTUDYR = INDSTDYR

INTRDISCR = INTRDSCR

MEMORIZER = MEMORIZR

 RESEARCHR = RESERCHR







SCAVAIL = SCAVAIL0 (for 00, 01 and 02 paper) and SCAVAIL2 (for 02 - 04)

SCGENRLED = SCGNRLED

SCPRACACA = SCPRACAC





SOCIAL00R = SOCIAL0R

SOCIALR = SOCIAL1R





 STUDYABRR = STDYABR

 See the information on SAT variables under the ‘New in November 2010’ section.

Experimental questions

 Note that all of these questions were moved to the NSSE_ONEYEAR dataset . This dataset was created for all items administered only one year. We have added a variable, CU row ID



(CUROWID), that has a unique value for each row. This variable is on both the NSSEALLYRS and NSSE_ONEYEAR datasets so that the two datasets can be merged by the user using only the

CUROWID

In 2002 NSSE added some Beta questions. These all have variable names that begin with B_, like

B_ARTEX.

 BETAMARK values (valid only in 2002) denote which group of BETA questions were

 answered.

 A missing value or value of 5 on BETAMARK denotes that the respondent did not answer

 any BETA questions (there appear to be 20 or so respondents who have a missing

BETAMARK yet still answered some of the BETA questions).

Each institution only has one BETAMARK value.

In 2003 NSSE added some questions on technology. These all have variable names that begin with

EXP03, like EXP0302A.

 EXPMARK values (valid only in 2003) denote whether or not an institution answered the technology questions.

 A value of 1 on EXPMARK denotes that the institution did not answer the technology







 questions.

Each institution only has one EXPMARK value. All institutions received the same set of questions to answer.

In 2005 NSSE added some questions specifically for first-year students and seniors.

 First-year students were asked questions on their first-year experiences (these all have variable



 names that begin with EX05f).

Seniors were asked questions on multiple institution attendance (these all have variable names that begin with EX05s).

 These students were also asked questions on their motivation to learn. However, NSSE intentionally left out the individual responses to these questions in the 2005 AAU dataset.

Frequency distributions for all of the 2005 experimental questions can be viewed here -

 http://nsse.iub.edu/pdf/2005_inst_report/EXP_Items_2005.pdf

There were no experimental questions in 2000, 2001, 2004, 2006, 2007, 2008 and 2009.

NSSE Benchmarks of Effective Educational Practice

 “The benchmarks are based on 42 key questions from the NSSE survey that capture many of the most important aspects of the student experience. These student behaviors and institutional features are some of the more powerful contributors to learning and personal development.”

(description from NSSE PDF on the variables - http://nsse.iub.edu/pdf/nsse_benchmarks.pdf

)

The five NSSE Benchmark variables begin with NB.

Introduced by NSSE in 2004 and added to the NSSE all years dataset in 2005. Benchmarks were calculated by Colorado back to 2000 although some individual variables used to calculate the benchmarks were not on the 2000 and 2001 surveys.





 Colorado calculated the unadjusted benchmarks scores only. All scores go from 0 to 100 and are missing if the student answered fewer than about 2/3 of the items.

 SPSS syntax on calculating the benchmarks can be found here -

 http://nsse.iub.edu/html/syntax_library.cfm

See also the NSSE multiyear user guide http://nsse.iub.edu/pdf/2008_Institutional_Report/NSSE%20Multi-

Year%20Data%20Analysis%20Guide.pdf

Voluntary System of Accountability (VSA) Items (new calcs 2008)

 Twenty-three items from NSSE are used for VSA reporting. Each “VSA item” is computed by collapsing across particular response categories of a NSSE item. For example, the responses of



 seniors reporting that they tutored or taught other students "very often," "often," or "sometimes" are combined to compute a VSA item, reported as a percentage of total responses. Only “never” is not included in the percentage.

The all-years dataset contains the 23 VSA items (VSA01-VSA23) for 2005 and later years. They are computed for seniors (CLASSRAN = 4) only, using the random sample (COMPARE = 1) for eligible students. Note that each VSA item is a 0/1 variable and that a mean computed on any of these items represents the percentage of seniors who selected particular response categories for that item.

The table below shows each VSA item, its label in the all-years dataset, the NSSE item from which the VSA item is derived, and the NSSE item response values that were used to create the VSA item. Grouped with the VSA group title.

VSA item name VSA item label

Derived from

NSSE item

NSSE item response values used

Group Learning Experiences

VSA01 occgrp

VSA02 tutor

VSA03

VSA: Spent at least 6 hours per week participating in co-curricular activities such as student organizations and intramural sports cocurr01

Active Learning Experiences

VSA04 acadpr01

VSA05 resrch04

VSA06 intern04

VSA07 volntr04

VSA08 stdabr04

VSA09 clpresen

Institutional Commitment to Student Learning and Success

VSA10 envsuprt

VSA11

VSA: Rated the quality of academic advising at this institution as good or excellent

VSA: Reported that this institution provided help in coping with work, family and other non-academic responsibilities VSA12

VSA13

VSA: Reported working harder than they thought they could to meet an instructor's standards or expectations

Student Satisfaction advise envnacad workhard

3-8

4

3, 4

2, 3, 4

2, 3, 4





Page 8

VSA14 samecoll

VSA15 entirexp

VSA16 envstu

Student Interaction with Campus Faculty and Staff

VSA17 envadm

VSA18 envfac

VSA19

VSA: Reported that faculty members provided prompt feedback on their academic performance facfeed

VSA20 facideas

2, 3, 4

Experiences with Diverse Groups of People and Ideas

VSA21

VSA22

VSA: Reported that they often tried to understand someone else's point of view

VSA: Reported their experience at this institution contributed to their understanding people of other racial and ethnic backgrounds othrview 3, 4 gndivers 2, 3, 4

VSA23

VSA: Often had serious conversations with students of a different race or ethnicity divrstud 3, 4





For additional information on VSA, see http://www.voluntarysystem.org/index.cfm?page=homePage http://nsse.iub.edu/html/vsa.cfm

The NSSE VSA item codebook is located at http://nsse.iub.edu/pdf/vsa/VSA%20NSSE%20Codebook.pdf





The Excel file of basic statistics has means for all items – so use the filter to pick seniors only and see all the VSA item percentages.

Our calculating the VSA items does NOT mean that all schools participate in VSA!!!

Weights

 In 2008 we calculated, in the metadata dataset (described at bottom of this doc) but mergeable with response-level data, three weights useful for analysis of AAUDE data: WtToPop,





WtToSchoolsEqual, and WtToNRespondents.

 These are available for 2005 and later only.





See the metadata section of this doc for explanation.

These are not the same as the weights NSSE calculates!!

NSSE computes weights to adjust for different response rates by gender and enrollment status (fulltime, part-time), and for different institutional enrollment sizes. NSSE, in a February 2008 e-mail to Colorado, suggested using WEIGHT1 (in the all-years dataset) to adjust for different response rates by gender and enrollment status, noting that this weight "will keep your analysis to the actual number of respondents in your dataset." NSSE suggested using WEIGHT2 to adjust for institutional enrollment size. In 2006, these two weights were combined into WEIGHT3, which is used for the AAUDE and other peer comparisons in NSSE-generated reports in that year.

WEIGHT3 adjusts for different response rates by gender and enrollment status within institutions and then sets the contribution of each institution to be equal, irrespective of enrollment size. NSSE has not made WEIGHT3 available for 2007 or 2008.

As described in NSSE literature (e.g., the 2008 Codebook), using NSSE weights will enable an institution to reproduce its NSSE-generated statistics (e.g., Benchmark scores, means, and percentage distributions). However, NSSE recommends that institutions interested in further





Page 9 examination of their own data consider using different weighting schemes that account for

 response rate differences among relevant subgroups (e.g., majors and colleges).

Additional information on NSSE weights can be found at http://nsse.iub.edu/html/weighting.cfm

Colorado Scales

 All variables beginning with SC are Colorado scales. These are the means of student responses

 over a set of related questions.

 For instance, the variable SCCLSZ is the mean of questions AAU01 and AAU02.

See http://www.colorado.edu/pba/surveys/NSSE/06/items.htm

for a listing of items in each scale.



Note that some items were reversed (recoded). Note also that some of these scales differ slightly for years prior to 2003 (for example, see http://www.colorado.edu/pba/surveys/NSSE/02/items.htm

).

Scales do not all have the same minimum and maximum.

File format

 The longitudinal NSSEALLYRS dataset is a SAS version 9 dataset, produced using SAS 9.2 on a

PC, then zipped. If an earlier SAS format is needed then let us know at IR@colorado.edu

.

 If you need the data in a different format, try unzipping the dataset and using a translation program such as DBMScopy to translate to SPSS, CSV, etc. You may lose item labels in doing so; they are





 all shown in the Excel file MatchVars.xls.

All of the variables have names with 8 or less characters.

SAS formats that define response alternatives are not placed on the dataset. However, we have created the formats that could be attached to these variables. The name of the format is in the

Excel variable list and the SAS code for the formats can be accessed by clicking here .

If you can’t get a translation let us know at IR@colorado.edu

and we’ll create a translation you can use.

Institutional Metadata (added 12/2008) Data revised 1/09

 The institutional metadata dataset, provided by NSSE, contains the number of freshmen and seniors, by year of administration and participating institution, in each institution's population and

NSSE-selected random sample from the eligible population (COMPARE=1). It also contains the number of responding students, the response rate (computed as the number of responses divided by the number of students in the sample), and NSSE-computed sampling error (stored in the dataset as a percentage).

 NSSE attempts an explanation of its sampling error formula at http://nsse.iub.edu/html/error_calculator.cfm

. An example of sampling error usage that NSSE provides is: “assume that 60% of your students reply ‘very often’ to a particular item. If the sampling error is +/-5%, then the true population value is most likely between 55% and 65%.”

NSSE has not provided enough information on its site (that we can find, anyway) to fully determine how the sampling error formula was developed, whether it changes from year to year, why it reflects response rate as well as sample size, etc. We therefore don't have much in the way of recommendations for use. There are alternatives to NSSE sampling error, such as computing the standard error of proportions and means for sample data from individual schools.





 We asked NSSE for eligible population and random-sample/elgible population sample N’s for all years (start year is 2000). NSSE was unable to provide such. They could be proxied by 1) assuming similar response rates to later years and calculating sample as respondents/respRate

(and using NSSE sampling policies too), and 2) using the 2005+ NSSE population data vs.

IPEDS fall enrollment to model pct of undergrad fall enrollment that’s freshmen and seniors, then applying the model pcts to IPEDS enrollment for earlier years, by school.

 Colorado has not done either. If any AAUDE user does, please let Lou know and we’ll include in the metadata for all to use.

 CU-Boulder has computed several variables and added them to the metadata dataset. These include a 0/1 indicator variable for AAU publics ("public"); the sampling fraction ("Sample_Fraction," ranges from under 10% to 100%), computed as the number in the sample divided by the number in the population; and several different types of weights ("WtToPop," WtToSchoolsEqual," and

"WtToNRespondents"). We did not add these weights to the response-level dataset, but the user easily can.

 Rationale for the three weights is the same for any AAUDE survey with individual-respondent









 data; this is described in a separate document .

Note that these are not the same weights that NSSE uses to compute benchmarks and other institutional statistics (those weights are described in the "Weights" section above).

A row in the metadata dataset is a combination of institution, IPEDS no., year of administration, and CLASSRAN (1 = first year, 4 = senior). This dataset can therefore be merged easily with the all-years dataset, which has a similar structure.

Both the metadata dataset and the all-years response-level dataset are sorted by year, IPEDS,

CLASSRAN, and SURVEYID (response-level dataset only).

 Jan ’09: The variable SURVEYID previously contained duplicate values within a given year due to the length of the variable being too short. This has been corrected and there are now no duplicate values within an individual year. Note that this variable is missing for 2000, 2001 and 2003.

The Excel of response rates is based on the same metadata.

Jan ’09: The metadata now match the response-level counts with COMPARE=1 exactly.

 The match was accomplished by

 Replacing response level counts for RANSMP=1 with those from COMPARE=1,



 incorporating all of INELIG=1, CLASSRAN in 1, 4, and SMPLxx

Fixing the metadata counts for Indiana 2005 first-year to meet the specifications for

COMPARE=1. NSSE provided the revised sample count.

Earlier statement, 12/08: The metadata from NSSE contain errors, counts that do not reconcile to the response-level data. The Excel shows the errors. We did quick fixes on them in the dataset. The dataset will be reposted with a date after 12/12/08 if and when the errors are fixed for good, and I’ll send notice.

History – year to year changes in the longitudinal data file through 2012

New in November 2012

 2012 data added to the dataset. No variables added or changed





Page 11

New in December 2011

 McGill University previously not included. Data were added for the 2008 and 2011 administrations.

 Georgia Tech 2011 data added April, 2012.


 This is the first that NSSE routinely did a census rather than a sample. The number of respondents is far bigger.

 Variables added to the NSSEALLYRS dataset.

 We have added a 0/1variable (PUBLIC) that marks public institutions. 0 = private institution,

1 = public institution.

 We also added a variable (WINDOW1) denoting a 3-year time window on when the survey was administered. The values are ‘2001-03,’ ‘2004-06,’ ‘2007-09,’ and ‘2010-12.’ This window works to give CU-Boulder a group of 16 public AAU’s with the same schools in every

 window.

Two variables were added on military status (VETERAN) and whether or not they received combat pay for military service (VETPAY).

 Changes made to the variables reporting SAT scores.





From 2002 – 2006 the only SAT variables were SATT (total), SATM (math), SATV (verbal).

NSSE kept the above variables from 2007 – 2009 but also added variables on the new version of the SAT. These variables were SATRT (revised total), SATRM (math), SATRR (verbal),

SATRW (writing).





In 2010, based on College Board findings, NSSE did not see a need to distinguish between the old and new SAT test and replaced the above SAT variables with SAT_M (math), SAT_V

(verbal) and SAT_W (writing). They removed the total variable due to some institutions’ including the writing score in the summation.

CU-Boulder actions for the above…

 Added an SAT_T variable for 2002 – 2011 that is the sum of SAT_M and SAT_V





(provided both of those values are NOT missing). This means some values of the total variable from 2002-2009 were replaced with the result of this new calculation (most significant changes to institutions who included the writing score into the total).

From 2007-2009, if the old variables of SATM or SATV were missing then we would use

 the values from the new version variables (SATRM or SATRR).

SATRW was renamed to match the 2010 variable name (SAT_W). The other new version variables were then discarded.

 The old version variables (SATM, SATV) were renamed to match the 2010 variable name

(SAT_M, SAT_V).

 We end up with SAT_T, SAT_M, SAT_V for 2002 – present.

CIP code variables were added in 2009 (see below). With the change to CIP 2010 we have updated a few of the CIP codes that were affected by the change from CIP 2000. See the cells highlighted in blue of the Excel file that shows the mapping for changes/updates.








Page 12

There are now three SAS datasets in the ZIP file (linked to from the password-protected page).

 BASESTATS. The basic statistics dataset discussed above.

 NSSEALLYRS is the all-years dataset. Starting in 2009, we are excluding the items that were

 administered for only one year. The item is included if it is a new variable and was added to the most recent survey.

NSSE_ONEYEAR. This dataset contains 170 items that were administered for only one year.

 We have added a variable, CU row ID (CUROWID), that has a unique value for each row.





This variable is on both the NSSEALLYRS and NSSE_ONEYEAR datasets so that the two datasets can be merged by the user using CUROWID only.

Only one item changed from 2008 to 2009. LIVENOW, which asks where students live, added a

“None of the above” response. Only about 1% of respondents chose this option so we decided the change did not warrant a creation of a new variable for this item.

Variables added to the NSSEALLYRS dataset.

 CIP code variables derived from the NSSE primary and second major codes. CIPs were assigned by Mary Black (Michigan State) and Linda Lorenz (Minnesota). A separate Excel file also shows the mapping . There can be up to three CIP codes associated with each major code.

 NSSE has posted the NSSE-code to CIP match as Excel and SPSS code at its SPSS syntax





 page, http://nsse.iub.edu/html/syntax_library.cfm

NSSE major field descriptions based on the NSSE primary and second major field codes.

These variables use the FIELD format (e.g., code of 1 = Arts and Humanities).





Target institution variable (TRGTINST) to be used for analysis. Currently set to 0 for all rows.

Note that the institution class rank variable (CLASSRAN) was moved to the front of the dataset since it is a KEY variable.

Minor changes made in variable labels.

Introduction of a new missing value (.W) on all AAUDE questions to indicate Colorado students who got writing consortium questions.

New since 12/2008



Institutional metadata data set which includes population, sample, and respondent counts, response

rates, and calculated weights by institution, year, and class rank, and can be easily merged with





 response-level data. See end of this document. Metadata revised 1/09 after we and NSSE got on the same wave length. Only Indiana ’05 first years changed.

Item changes

 Two of the AAUDE optional questions have been changed slightly for 2008.

 A seventh response alternative, “poor academic performance,” has been added to the question about what's the biggest obstacle to academic progress (A17).

 The wording for the first response alternative in the question about primary reason for working for pay (A18) has been modified slightly.

 For additional information on these changes, see http://www.colorado.edu/pba/surveys/nsse-aaude/codebook/AAUDEOpts0710.doc

Voluntary System of Accountability (VSA) items . Twenty-three items from NSSE are used for

VSA reporting. A new section in the user guide explains how these items are computed.

Key variable change Jan ’09. The variable COMPARE replaces RANSMP for defining the population useable for across-institution (and across-time) comparisons. RANSMP failed to use





Page 13 the variable INELIG that excludes some respondents not eligible for inclusion in the standard firstyear and senior populations. We removed the variable RANSMP entirely.

 Key variable change (July 08): The variable RANSMP replaces the former variable BASE for

 use in across-institution comparisons. BASE was discovered in January 2008 to be incorrectly defined, in that it excluded some students who were in fact randomly selected. What was

BASE has been renamed to BASE_OLD.

SAS variable labels now start with -- (2 dashes) for all CU-Boulder calculated variables. The



NSSE item number is in the Excel of variables .

Minnesota 2008 data added 01/09. The data were inadvertently omitted by NSSE.

University of Colorado at Boulder file refs

 This file: L:\ir\survey\nsse\aaude\UserGuide.doc

 Datasets: L:\ir\survey\nsse\aaude\datasets\NSSEALLYRS, NSSE_ONEYEAR and BASESTATS.





Excel variable list: L:\ir\survey\nsse\aaude\MatchVars.xls

Codebooks in L:\ir\survey\nsse\aaude\codebooks



Throughout, ALLCAPS indicates the name of a variable in the dataset

History – year to year changes in the longitudinal data file through 2012

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Throughout, ALLCAPS indicates the name of a variable in the dataset

History – year to year changes in the longitudinal data file through 2012

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