Factors related to persistence of transfer students enrolled in engineering and technology programs at
the Montana State University College of Engineering
by Geoffrey Alexander DAtri
A thesis submitted in partial fulfillment of the requirements for the degree of DOCTOR OF
EDUCATION
Montana State University
© Copyright by Geoffrey Alexander DAtri (1982)
Abstract:
This study was designed to contribute to the Montana State University College of Engineering's effort
to increase its understanding and retention of transfer students in both the engineering and technology
programs. This study investigated demographic and cognitive variables that differentiate persister and
non-persister transfer students who initially entered the Montana State University College of
Engineering during the 1977-78 and 1978-79 academic years and Autumn Quarter 1979.
The total study populatiqn comprised 394 transfer students. The engineering subpopulation totaled 316
students and the technology sub population totaled 78 students. Six major research questions were
examined providing demographic and cognitive information as well as identifying factors relating to
predicting persistence and Montana State University grade point average.
The statistical analysis of the study included the development of twelve multiple regression models to
predict persistence/nonpersistence and MSU grade point average. Eight of these, called restricted
models, were developed by selecting only independent variables which contributed a minimum
increase of .01 in the total r^2 values computed in the stepwise regression. For the prediction of
persistence, the R^2's ranged from .151 to .468; for the prediction of MSU grade point average, the
R^2's ranged from .239 to .526, all significant at the .01 level. Four of the restricted models were
chosen for further testing using double cross-validation procedures. All four models yielded consistent
results throughout the validation process.
The study found the following independent variables to be strong predictors of persistence and grade
point average: cumulative grade point average from prior institutions, high school rank at graduation
ACT composite and quantitative aptitude test scores. The following were less powerful, less consistent
predictors: having an Associate's Degree, number of credits accepted for transfer, gender, highest level
of mathematics,'and citizenship. The study determined that persistence for a two year period was 52.5%
for engineering students and 46.2% for technology students. FACTORS RELATED TO PERSISTENCE OF TRANSFER STUDENTS
ENROLLED IN ENGINEERING AND TECHNOLOGY PROGRAMS .
AT THE MONTANA STATE UNIVERSITY COLLEGE OF ENGINEERING
BY
i
G e o f f r e y A le x an d e r D1A t r i
A t h e s i s s u b m i t t e d in p a r t i a l f u l f i l l m e n t
o f th e requirem ents f o r the degree
of
DOCTOR OF EDUCATION
i
Approved:
h a i r p e r s o n , G r a d u a te Committee
G r ad ua te Dean
MONTANA STATE UNIVERSITY '
Bozeman, Montana
March 1982
iii
ACKNOWLEDGMENTS
The w r i t e r i s g r a t e f u l and i n d e b t e d t o t h e h e l p p r o v i d e d by many
p e o p l e d u r i n g t h e co m p le ti o n o f t h i s s t u d y .
He w is h es t o pay s p e c i a l
t h a n k s t o h i s w i f e and c h i l d r e n f o r t h e i r c o n t i n u i n g s u p p o r t and u n d e r ­
s t a n d i n g t h r o u g h o u t t h e c o m p le ti o n o f t h e s t u d y .
The w r i t e r i s e s p e c i a l l y g r a t e f u l t o Dr. Stephen Hample , t h e
c h a i r p e r s o n o f t h e committee whose c o n s i s t e n t , t i m e l y , and p r o f e s s i o n a l
a s s i s t a n c e enhanced t h e v a l u e o f t h e s t u d y as well as t h e w r i t e r ' s
appreciation fo r q u ality research.
The w r i t e r a l s o e x p r e s s e s h i s s i n ­
c e r e g r a t i t u d e t o Dr. E r i c Stohmeyer and Mr. William J o h n s t o n e f o r
t h e i r e d i t o r i a l a s s i s t a n c e i n t h e c o m p le ti o n o f t h e f i n a l copy o f t h e
d issertation.
The w r i t e r acknowledges t h e c o o p e r a t i o n and a s s i s t a n c e
o f t h e o t h e r members o f t h e com mittee:
Dr. David Gibson, Dr. P a t r i c k
Donahoe, Dr. Ric ha rd H o r s w i l l , and Mr. A l f r e d S c h e e r .
A l s o , t h e w r i t e r w is h es t o e x p r e s s h i s g r a t i t u d e t o t h e f o l l o w i n g
i n d i v i d u a l s f o r t h e i r t e c h n i c a l a s s i s t a n c e and enc ouragement d u r in g
t h e c o m p le ti o n o f t h e s t u d y :
Dr. Lawrence E l l e r b r u c h , Dr. Mark
Havi I an d, Mr. Wi lli am Lannan, Mr. Joe F r a z i e r , Dr. Lyle Gohn,
Dr. A l b e r t Suvak, Dr. R o lf G r o s e t h , Dr. Ro be rt H en d ric ks o n , and
Ms. Deena W e s t f a l l .
TABLE OF CONTENTS
Page
VITA ......................................................................................................................................
ii
ACKNOWLEDGMENT ..................................................................................................................
iii
LIST OF TABLES .....................................................................................
vii
LIST OF FIGURES .............................................................................
xii
ABSTRACT ..........................................................................................................
x iii.
Chapter
1.
2.
INTRODUCTION ............................................................................................
I
S t a t e m e n t o f t h e Problem ............................................................
4
Need f o r t h e Study ..........................................................................
5
Major Resea rc h Q u e s ti o n s ............................................................
8
General P r o c e d u r e s ----- : ................................................................
10
L i m i t a t i o n s and D e l i m i t a t i o n s .................................................
14
D e f i n i t i o n o f Terms .......................
15
Summary ...................................................................................................
16
REVIEW OF RELATED LITERATURE .............
18
I n t r o d u c t i o n .......................................
18
Freshmen A t t r i t i o n S t u d i e s .......................................................
18
T r a n s f e r S t u d e n t S t u d i e s ............................................................
23
E n g i n e e r i n g A t t r i t i o n S t u d i e s .................................................
31
Summary ...................................................................................................
33
J
V
Chapter
3.
4.
Page
PROCEDURES ................................................................................
35
I n t r o d u c t i o n ..................
35
D e s c r i p t i o n o f t h e P o p u l a t i o n ..................................
36
Method o f C o l l e c t i n g Data ..........................................................
40
Coding o f t h e V a r i a b l e s f o r Data G a th e r i n g ...................
Dependent ( C r i t e r i o n L e v e l s ) ....................................
In d e p e n d e n t V a r i a b l e s ........................................................
Conve rs ion o f SAT S c o r e s t o ACT S co re s ............................
42
42
43
46
Coding o f V a r i a b l e s f o r S t a t i s t i c a l A n a l y s i s ..............
47
Major Resea rc h Q u e s ti o n s and S t a t i s t i c a l Methods . . .
48
A n a l y s i s o f Data .............
M u l t i p l e R e g r e s s io n ............................................................
Cross V a l i d a t i o n ..............................................
M iss ing Data ................
L i m i t a t i o n s o f R e g r e s s i o n A n a l y s i s ..........................
51
51
54
55
55
P r e c a u t i o n s Taken f o r Accuracy ..............................................
58
E t h i c a l S t a n d a r d s ................................................
59
Summary ...................................................................................................
59
RESULTS AND FINDINGS OF THE STUDY.................
E n g i n e e r i n g and Technology S t u d e n t s - Research
Q u e s t i o n s 1 - 3 ........
R es ear ch
Q ue s tio n One ......................
R es ear ch
Q ue s tio n Two .....................................................
R es ear ch
Q ue s tio n Three ................................
Engineering Students . Resea rc h
Q ue s tio n
. R es ear ch
Q u es tio n
R es ear ch
Q u es ti o n
61
62
62
68
76
R es ear ch Q u es ti o n s 4 - 6 .........
78
Four .......................................................... 78
Five ..................................................
84
Six .....................................................
95
56
samples from information in the independent variables.
Bhattacharyya
and Johnson (1977) caution that regression results may be misleading,
i f any of the following assumptions are violated:
1.
The underlying relation is linear.
2.
Independence of errors.
3.
Constant variance.
4.
Normal distribution.
The assumption of underlying linearity seems reasonable for the
predictor variables in this study; e.g. as levels of mathematical
background increase, success in engineering seems likely to steadily
increase.
The study's relatively large sample size for engineering
students mitigated problems caused by violations of the other assump­
tions.
However, this problem may be the cause of marginal findings
when examining the small technology subpopulation with an N of 33.
Thus, the methodology of cross validation may have uncovered errors
caused by these assumptions not being met.
The nature of the criterion variable also limits the application
of regression analysis.
Discrete, but ordered, dependent (criterion)
variables present some d iffic u ltie s.
A discrete variable, in which
outcomes are expected to be discrete values such as I, 2, or 3, does
not immediately f i t a regression equation which produces more con­
tinuous fractional values.
In this study, values predicted by regres­
sion equations for persistence/non-persistence were simply rounded
57
to values of 0 or I.
Other s ta tis tic a l procedures for treating dis­
crete cases have been suggested, but have not yet been well established
Hanushek and Jackson (1977) state:
"Unhappily, we now understand more about the d iffic u ltie s than
we do about th eir solution. The basic problem is to specify
plausible models to describe the probabilities of discrete
events. Estimation of these models using microdata often re­
quires techniques that are not well developed yet."
The separate examination and tabulation of results of this study's
cross validation procedure served as an overall safety check of the
appropriateness of the regression.
An even more serious problem exists in applying regression analy­
sis to a problem in which the dependent (criterion) variable can
assume several discrete, non-ordered outcomes.
Hanushek and Jackson
(1977) summarize:
"The estimation d ifficu lties created by linear models with
discrete dependent variables become unmanageable in situations
where outcomes are measured by categorical variables with
multiple responses or are the jo in t outcomes of several separate
events. In modeling voting in a multicandidate election such
as the 1968 presidential election or occupational choices or in
explaining a series of jo in t outcomes such as party affilia tio n
and vote or residential locations and type of housing unit, the
dependent variables cannot be ordered with each value indicat­
ing more or less of something than the previous value. These
cases, called polytomies, can be treated as dichotomies by
comparing one outcome to all others. The probability of voting
for Nixon rather than Humphrey or Wallace or a probability of
living in Boston in a three-bedroom apartment rather than all
other possib ilities are examples of this procedure. However,
these methods lose all information about the jo in t nature of
the events and do not accurately model the desired range of
behavior. . . . "
vi
C hapter
Page
Technology Students - Research Questions 4-6 ....................
Research Question Four ..................................................
Research Question Five ................................................
Research Question Six .....................................
101
101
105
Double C ross-Validation o f Four R estricted Models ____
. MSUGPA Models ...............................................................
Persistence/Non-Persistence Models ..........................
In te rn a tio n a l Students ..................................................
118
127
129
139
11.3
Summary .......................................................................
5.
SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS .......................
141
143
Statement of the Problem ........................................................
143
Summary of the Population and Procedures .......................
144
Conclusions ...........................................................
Conclusion One .....................................
Conclusion Two .....................................
Conclusion Three ...........................................................
Conclusion F o u r ...... ..............................
Conclusion Five ....................................
Conclusion Six ...............................................
145
145
146
150
151
151
152
Recommendations...... ..............................................
Recommendation O n e ..........................................................
153
Recommendation Two ..........................................................
153
Recommendation Three .....................................
Recommendation F o u r .......................................
154.
Recommendation Five 1......................................................
154
Recommendation Six .....................................
Recommendation Seven ......................................................
155
Recommendation Eight ............................................
153
154
155
156
LITERATURE CITED ...................................................................
APPENDICES ...............................................................................
APPENDIX A .....................................................................................
APPENDIX B ......................................................................
157
170
171
175
vi i
LIST OF TABLES
Table
1.
2.
Page
A t t r it io n of Engineering Transfer Students Calculated
a t Completion of Winter Quarter 1981 ...........................................
4
Persistence vs. Non-Persistence Designation Date Based
Upon I n i t i a l Entry to Montana State U niversity College
o f Engineering ............................................................
12
College o f Engineering Transfer Student Population By
Year o f Entry and C u rric u la r Track ................................................
37
College o f Engineering Transfer Student Population By
Quarter of Entry .....................................................................................
37
5.
Description o f the Population by C u rric u la r Program .............
39
6.
C orrelations of Selected ACT Tests with SAT Tests .................
47
7.
Independent Variables Associated with Two I n i t i a l
Regression Models ...................................................................................
52
Summary of Regression Analyses Based on Dependent Variables
Independent V ariable Models andSub-Populations ......................
53
Abbreviations Used fo r Variables ............................. ......................
63
Graduation, Dropout, and Enrollment of Engineering and
Technology Populations ................
65
Persistence and Non-Persistence Rates fo r Engineering and
Technology Students ...............................................................................
66
Demographic C h a ra c te ris tic s: Numbers and Percentages
Comparing P ersisters and Non-Persisters Engineering and
Technology Programs ..............
69
Demographic and Cognitive Measures fo r Persisters and NonP ersisters Continuous Data - Engineering . . . . ........
73
Demographic and Cognitive Measures fo r P ersister and NonP e rs is te r Continuous Data - Technology .................
74
3.
4.
8.
9.
10.
11.
12.
13.
14.
v iii
T ab le
15.
Page
Stepwise M u ltip le Regression Variance Summary - Basic
R estricted Model (5 V a ria b le s )- Engineering
Dependent V ariable: Persistence/Non-Persistence ................
.
81
16.
Stepwise M u ltip le Regression Step Summary - Basic .
R estricted Model - Engineering
Dependent V ariable: Persistence/Non-Persistence .............
17.
C orrelation M atrix - Basic R estricted Model - Engineering
Dependent V ariable:
Persistence/Non-Persistence
33
Stepwise M u ltip le Regression Variance Summary - Complete
R estricted Model (6 Variables) - Engineering
Dependent V ariable:
Persistence/Non-Persistence
35
Stepwise M u ltip le Regression Step Summary - Complete
R estricted Model - Engineering
Dependent V ariable:
Persistence/Non-Persistence
35
C orrelation M atrix - Complete R estricted Model - Engineering
Dependent V ariable: Persistence/Non-Persistence ...................
37
Stepwise M u ltip le Regression Variance Summary - Basic
R estricted Model (5 V a ria b le s )- Engineering
Dependent V ariable: MSU Grade Point Average ...........................
89
Stepwise M u ltip le Regression Step Summary - Basic
R estricted Model - Engineering
Dependent V ariable: MSU Grade Point Average ...........................
90
18.
19.
20.
21.
22.
23.
C orrelation M atrix -Basic R estricted Model - Engineering
Dependent V ariable: MSU Grade Point Average ......................... .. . 91
24.
Stepwise M u ltip le Regression Variance Summary - Complete
R estricted Model (4 V a ria b le s )- Engineering
Dependent V ariable: MSU Grade Point Average ...........................
9^
Stepwise M u ltip le Regression Step Summary - Complete
R estricted Model- Engineering
Dependent V ariable: MSU Grade Point Average ...........................
93
C orrelation M atrix - Complete R estricted Model- Engineering
Dependent V ariable: MSU Grade Point Average ...........................
94
25.
26.
I
IX
Table
27.
Page
Stepwise M u ltip le Regression Variance Summary - Basic.
Model (22 V a ria b le s )_ Engineering
Dependent V ariable: Persistence/Low GPA Non-Persistence ..
96
Stepwise M u ltip le Regression Step Summary - Basic Model
Dependent V ariable: Persistence/Low GPA Non-Persistence . .
97
Stepwise M u ltip le Regression Variance Summary - Complete
Model (21 V a riab les).- Engineering
Dependent V ariable: Persistence/Low GPA Non-Persistence . .
99
30. . Stepwise M u ltip le Regression Step Summary - Complete Model
Dependent V ariable: Persistence/Low GPA Non-Persistence . .
100
28.
29.
31.
32.
33.
34.
.
35.
36.
Stepwise M u ltip le Regression Variance Summary - Basic
R estricted Model. (6 V a ria b le s )- Technology
Dependent V ariable:
Persistence/Non-Persistence
102
Stepwise M u ltip le Regression Step Summary - Basic
R estricted Model - Technology
Dependent V ariable:
Persistence/Non-Persistence
103
C orrelation M atrix - Basic R estricted Model _ Technology
Dependent V ariable:
Persistence/Non-Persistence
104
Stepwise M u ltip le Regression Variance Summary - Complete
R estricted Model (6 Variables) _ Technology
. Dependent V ariable: Persistence/Non-Persistence .....................
106
Stepwise M u ltip le Regression Step Summary - Complete
R estricted Model- Technology
Dependent V ariable:
Persistence/Non-Persistence
107
C orrelation M atrix - Complete R estricted Model _ Technology
Dependent V ariable:
Persistence/Non-Persistence
108
37.
Stepwise M u ltip le Regression Variance Summary - Basic
R estricted Model (6 Variables) - Technology
Dependent V ariab le: MSU Grade Point Average ............... .
38.
Stepwise M u ltip le Regression Step Summary - Basic
R estricted Model - Technology
Dependent V ariable: MSU Grade Point Average .................
X
T able
39.
Page
C orrelation M atrix - Basic R estricted Model - Technology
Dependent V ariable:
Persistence/Non-Persistence ...................
112
40.
Stepwise M u ltip le Regression Variance Summary - Complete
R estricted Model (7 Variables) - Technology
Dependent V ariable: MSU Grade Point Average ........................... . 114
41.
Stepwise M u ltip le Regression Step Summary - Complete
R estricted Model - Technology
Dependent V ariable: MSU Grade Point Average ...........................
115
C orrelation M atrix - Complete R estricted Model - Technology
Dependent V ariable:
MSU Grade Point Average .................. ........
116
Stepwise M u ltip le Regression Variance Summary - Basic
Model - Technology
Dependent V ariable: Persistence/Low GPA Non-Persistence . .
119
Stepwise M u ltip le Regression Step Summary - Basic Model
Dependent V ariab le: Persistence/Low GPA Non-Persistence . .
120
Stepwise M u ltip le Regression Variance Summary - Complete
Model - Technology
Dependent V ariable: Persistence/Low GPA Non-Persistence . .
121
Stepwise M u ltip le Regression Step Summary - Complete Model
Dependent V ariable: Persistence/Low GPA Non-Persistence ..
122
Summary o f M u ltip le Regression - O riginal and R estricted
Models Developed.on Engineering Students ...................................
123
Summary o f M u ltip le Regression - O riginal and R estricted
Models Developed on Technology Students ............................. .
124
Results of Double Cross-Validation
Dependent V ariable: MSU Grade Point Average ...........................
128
Accuracy of Model Predicting Persistence/Non-Persistence Basic R estricted Model Applied to Engineering Subpopula­
tio n ............................................................................................... ........
131
42.
43.
44.
45.
46.
47.
48.
49.
50.
xi
T able
51.
52.
53.
54.
55.
56.
57.
Page
Double C ross-Validation Accuracy of Model Predicting
P ersistence/Npn-Persisgence - Basic R estricted Model
Applied to Engineering Students Sample A ......................... .........
132
Double Cross-Validation Accuracy of Model Predicting
Persistence/Non-Persistence - Basic R estricted Model
Applied to Engineering Students Sample B .......... ........................
133
Accuracy of Model P redicting Persistence/Non-Persistence Basic R estricted Model Applied to Technology Sub­
population ....................................................................................
135
Double C ross-Validation Accuracy of Model Predicting
Persistence/Non-Persistence - Basic R estricted Model
Applied to Technology Students Sample A .....................................
136
Double C ross-Validation Accuracy of Model Predicting
Persistence/Non-Persistence - Basic R estricted Model
Applied to Technology Students Sample B .......................................
137
Results of Double Cross-Validation
Dependent V ariable: Persistence/Non-Persistence ........................
138
A Comparison of In tern a tio n a l Students and USA/Canadian
Students on Selected Demographic and Cognitive
Variables ...................................................................................................
140
xi i
LIST OF FIGURES
Figure
I.
Venn Diagram Describing the Relationship Among Research
in A t t r it io n of Transfer and.Engineering Students .................
Page
19
x iii
ABSTRACT.
This study was designed to contribute to the Montana State Uni­
v e rs ity College of Engineering's e f f o r t to increase it s understanding
and re te n tio n of tra n s fe r students in both the engineering and tech­
nology programs. This study investigated demographic and cognitive
variab les th a t d iffe r e n tia te p e rs is te r and non-persister tra n s fe r
students who i n i t i a l l y entered the Montana State U n iversity College
o f Engineering during the 1977-78 and 1978-79 academic years and
Autumn Quarter 1979.
The to ta l study populatiqn comprised 394 tra n s fe r students. The
engineering subpopulation to ta le d 316 students and the technology sub­
population to ta le d 78 students. Six major research questions were
examined providing demographic and cogn itive information as well as
id e n tify in g facto rs re la tin g to predicting persistence and Montana
State U niversity grade point average.
The s t a tis t ic a l analysis of the study included the development
of twelve m u ltip le regression models to predict persisterice/nonpersistence and MSU grade point average. Eight of these, called
re s tric te d models, were developed by selecting only independent
variab les which contributed a minimum increase of .01 in the to ta l
r2 values computed in the stepwise regression.
For the prediction
o f persistence, the R ^'s ranged from .151 to .468; fo r the prediction
o f MSU grade point average, the R2 's. ranged from .239 to .526, a ll
s ig n ific a n t a t the .01 le v e l. Four o f the re s tric te d models were
chosen fo r fu rth e r testin g .u sin g double cro s s-v a lid a tio n procedures.
Al I four models yield ed consistent re su lts throughout the v a lid a tio n
process.
The study found the follow ing independent variables to be strong
predictors of persistence and grade point average: cumulative grade
point average from p rio r in s titu tio n s , high school rank a t graduation,
ACT composite and q u a n tita tiv e aptitude te s t scores. The follow ing
were less powerful, less co n sisten t.p red icto rs: having an Assoc­
ia te 's Degree, number of cred its accepted fo r tra n s fe r, gender,
highest level of mathematics,' and c itiz e n s h ip . The study determined
th a t persistence fo r a two year period was 52.5% fo r engineering
students and 46.2% fo r technology students.
CHAPTER I
INTRODUCTION
During the past century higher education has been marked by
continuous expansion.
The 1950's and 1960's produced a period of
strong optimism in higher education.
Enrollments grew a t an unpre­
cedented r a te , bringing dramatic increases in revenue, fa c u lty ,
f a c i l i t i e s , programs, and in s titu tio n s .
Higher.education is now entering it s f i r s t period o f declining
enrollments in more than a century.
In 1980, the number o f youth
reaching 18 years of age w ill peak a t 4 .3 m illio n .
w ill be 26% fewer 18-year olds (M agarrel, 1980).
By 1992, there
Since th is age
group represents the major pool from which in s titu tio n s obtain stu­
dents, th is decline w ill cause serious consequences fo r colleges and
u n iv e rs itie s (Carnegie, 1980).
A fte r interview ing members o f the
Carnegie Council, Scully (1980) states:
"The demographic depression of the next two decades w ill lead
to declines in the undergraduate enrollment . . . and w ill
bring fundamental changes to many American colleges and univer­
s itie s . . . . .
A new academic revolution is upon us . . . .
In the past excellence was the theme. Now i t is s u rv iv a l."
- In the current lit e r a t u r e , numerous scenarios have been discussed
regarding the fu tu re o f higher education.
Most notably, Carnegie
(1 9 80 ), Cross (1980), Harcleroad (1979), and Cooke (1979) have pre­
sented both the o p tim is tic and pessim istic outlooks on the future
health of higher education.
Rather than review the s tra te g ies and
assumptions associated with each, i t is important to understand a
2
central theme which p re v a ils .
Bowen (1979) best summarizes:
"The
events th a t l i e ahead fo r higher education w ill have q u ite d iffe re n t
impacts upon d iffe r e n t types of in s titu tio n s , programs and regions.
These differences must be recognized to e ffe c tiv e ly manage academic
programs."
While many in s titu tio n s have recently experienced declining*
le v e l, or moderate increases in enrollm ent, engineering programs
throughout the nation have experienced substantial growth.
Accord­
ing to Barnes (1980), to ta l engineering enrollments in 1973 were
187,000 and increased to 311,000 in 1978.
crease to two major fa c to rs .
He contributes th is in ­
F ir s t , the natio n 's job market is
requiring an increasing percentage of te c h n ic a lly trained people.
Second, with the change in societal values, engineering schools
are a ttra c tin g a reasonable fra c tio n of female student population.
These two trends are expected to continue, thus providing impetus
to the steady growth of engineering programs during th is decade.
The Montana State U niversity College of Engineering, the la rg ­
est college w ithin the U n iv e rs ity , is experiencing the same growth
as the n a tio n 's other engineering programs.
Since 1973, the College
o f Engineering (COE) enrollment has more than doubled, a 115% in ­
crease.
During the same period, the other Montana State U niversity
(MSU) schools and colleges grew a t a 21% ra te .
This tremendous
growth in engineering enrollment has placed a d e fin ite s tra in on the
3
College o f Engineering's fa c u lty , students, and adm inistratio n.
In order to c o n tin u a lly improve the academic programs and to
more e ffe c tiv e ly cope with the overcrowded conditions, the College
of Engineering has in it ia t e d a number o f research studies to exam­
ine the c h a ra c te ris tic s and performance o f students.
Since the
m a jo rity o f incoming students enter the engineering program as fresh­
men, much o f the research and support programs have been directed
to the entering freshmen.
Research on the engineering tra n s fe r student population has
been overshadowed by studies o f the freshmen group,
ably due to two primary reasons.
This is prob­
F ir s t , the freshmen group repre­
sents the la rg e s t pool o f f i r s t entry students a t the u n iv e rs ity .
Second, due.to th e ir p rio r experience o f adapting to a new in s t i­
tu tio n , tra n s fe r students are often perceived as more sophisticated
and capable o f adapting more re a d ily to the academic environment.
A fte r conducting prelim inary research a t the completion of Win­
te r Quarter 1981, the author found the follow ing.
F ir s t , the tran s fe r
students are a s ig n ific a n t group representing 25.6% of the College
o f Engineering.
Of the 2,301 students who completed Winter Quarter
1981, 590 students completed some portion o f th e ir undergraduate pro­
gram a t another in s titu tio n .
Second, a review Of the tra n s fe r stu­
dents entering engineering during 1977, 1978 and 1979 reveals a t t r i ­
tio n rates o f 47.7%, 44.2% and 35.7% resp ectively (see Table I ) .
The
4
a t t r i t io n ra te fo r freshmen engineers averaged 11% during th e ir f i r s t
year o f study a t MSU.
Table
I
A t t r it io n o f Engineering Transfer Students
Calculated a t Completion o f Winter Quarter 1981
Year Entered
Number
Enrolled
Number
No Longer
Enrolled
Percent of
A t t r it io n
1977-78
153
73
47.7
1978-79
165
73
44.2
1979-80
210
75
35.7
A t t r it io n is a major problem in engineering since i t represents
a loss o f tim e, e f f o r t , and finances to the student, c o lle g e , and
in s titu tio n .
Although every in s titu tio n w ill experience some loss
of students, a ll colleges and u n iv e rs itie s must s triv e to keep stu­
dent a t t r i t io n a t an in s t it u t io n a lly defined acceptable ra te .
Statement of the Problem
This study was designed to contribute to the Montana State
U n iversity College o f Engineering's e f f o r t to increase it s under­
standing and re te n tio n o f tra n s fe r students in both the engineering
5
and technology programs.
The problem o f th is study was twofold:
F ir s t , the study examined demographic and cognitive variables
th a t were presumed to d iffe r e n tia te p e rs is te r and non-persister trans­
fe r students.
Second, the study examined demographic and cognitive variables
th a t were presumed to p re d ic t Montana State U niversity cumulative
grade point average.
The study population was delim ited to tra n s fe r students from
other in s titu tio n s who i n i t i a l l y entered the Montana State U niversity
College of Engineering's engineering and technology programs during
the follow ing quarters:
Autumn, 1977; W inter, Spring, and Autumn,
1978; W inter, Spring, and Autumn, 1979.
Need fo r the Study
The completion of th is study w ill contribute to the base of
knowledge regarding the a t t r i t io n o f tra n s fe r students in engineer­
ing c u rric u la . . A review o f lit e r a t u r e reveals a wealth o f information
regarding the a t t r i t io n and reten tio n o f students.
However, research
s p e c ific a lly re la tin g to tra n s fe r students in engineering c u rric u la
is sparse.
L i t t l e is known about the c h a ra c te ris tic s o f non-persis-
ters or the actual a t t r i t io n ra te of general tra n s fe r students.
This in ve s tig atio n also has p ra c tic a l sign ificance fo r Montana
State U n iversity and the College o f Engineering.
The U niversity
6
cannot apply w ith confidence the general findings from freshmen and
general tra n s fe r student research conducted a t other u n iv e rs itie s or
conducted on other c u rric u la .
The re su lts o f th is study may aid the
U niversity in lim itin g enrollment to tra n s fe r students who have a
strong p ro b a b ility o f completing a Bachelor's Degree.
By having a
b e tte r understanding o f the performance of tra n s fe r students, fa c u lty
w ill be b e tte r prepared to advise new students.
A review of the lit e r a t u r e reveals th a t a t t r it io n is a s ig n i- .
fic a n t problem.
Summerskill (1962), Eckland (1964), Astin (1971,
1 9 72 ,.1 97 5 ), Tinto and Terenzini (1978) have provided the most
extensive and noteworthy contributions to the theory o f student
a ttr itio n .
students.
However, th e ir research has been directed to freshmen
Cross (1968), Knoell and Medsker (1965), Sahdeen and
Goodale (1 9 76 ), Peng and B aily (1977), among others have studied
tra n s fe r students, but the m ajo rity o f tra n s fe r research is lim ited
to the examination o f tra n s fe r shock and to the general comparison
o f tra n s fe r students w ith students who i n i t i a l l y enro ll in a four
year in s titu tio n .
Lea, Sedlacek, and Stewart (1979) point out th a t numerous top­
ics associated with a t t r i t io n and reten tio n need to be researched.
They stress the importance o f local studies which allow fo r d ire c t
in s titu tio n a l a p p lic a tio n of fin d in g s.
t iv e ly l i t t l e
Richman (1979) states " re la ­
research has been done on tra n s fe r students and corre­
7
spondingly we know l i t t l e about th e ir needs and problems."
Sandeen
and Goodale (1 9 76 ), a f t e r conducting a four year in ve s tig atio n of
tra n s fe r students fo r the National Association fo r Student Personnel
Adm inistrators (NASPA), s ta te :
"Despite some s ig n ific a n t e ffo rts in recent years to learn
more about tra n s fe r students, most in s titu tio n s have not
studied th e ir own tra n s fe r students . . . . In s titu tio n s
could b e n e fit s ig n ific a n tly by conducting research bn trans­
fe r students."
An extensive review of lit e r a t u r e suggests th a t no research
has been conducted in the State of Montana and l i t t l e research has
been conducted on the national level addressing the a t t r i t io n and
reten tio n o f tra n s fe r students in engineering c u rric u la .
Dr. Paul
L. Dressel (1981), Professor of U niversity Research a t Michigan
State U niversity states:
" I am convinced th a t our e ffo rts to encourage students to re­
main in college in pursuit of a degree or meaningful c e r t if ic a t e
program require th a t we look more c a re fu lly a t the nature of
the educational experiences provided and p a rtic u la rly a t the
in te ra c tio n o f students with the various majors and degree
programs provided . . . .
We need to look a t reten tio n and
degree completion program by program ra th e r than on a compos­
i t e in s titu tio n a l basis . . . .
The data I have convinces me
th a t there is a great deal o f v a ria tio n in student persistence
from one program to another. Some o f th is has to do with the
nature o f the program, but I suspect th a t careful analysis in
any in s titu tio n w ill in dicate th a t there are numerous other
fa c to rs ."
The National Center fo r Higher Educational Management Systems
(NCHEMS) (1979) has recognized the importance of in s titu tio n a l e ffo rts
to understand the a t t r i t io n process, and they suggest th a t in s t it u ­
8
tions examine th e ir own a t t r i t io n problems and formulate e ffe c tiv e
re te n tio n programs.
The NCHEMS report summarizes the b enefits of
a t t r i t io n studies as:
1.
2.
3.
"Provide information about dropouts th a t may lead the
in s titu tio n to c o rrec tiv e actio n .
Determine how many students leave fo r reasons not amenable
to correctio n .
Lead to the development of a p re d ic tiv e model fo r dropouts."
The proposed study has considerable p ra c tic al sign ificance to
students, the College, and the U n iv e rsity.
G enerally, the reten tion
o f students in college is highly desirable fo r a ll p a rtie s concerned.
A ttr itio n represents a serious loss o f personal and in s titu tio n a l
resources.
An acceptable reten tio n ra te helps provide a basis fo r
sound management.
Also, increased student reten tio n is lik e ly to be
an in d ic a to r of b e tte r education (Noel, 1978).
Major Research Questions
The six questions posed in th is study were designed to assist
the College of Engineering in id e n tify in g p o ten tial p e rs is tin g trans­
fe r students and to contribute to the general body o f knowledge on
tra n s fe r students.
The follow ing questions served as the basis of
th is study and were applied in d iv id u a lly to engineering as well as
technology tra n s fe r students who entered the College o f Engineering
from Autumn Quarter 1977 through Autumn Quarter 1979.
I.
What percent o f tra n s fe r students p e rs is t in the engineer-
9
ing and technology programs fo r two years a f t e r i n i t i a l
entry?
2.
How do p e rs is te r and non-persister tra n s fe r students in
engineering and technology programs compare on various
demographic c h a ra c te ris tic s ( i . e . , quarter o f e n try , year
o f e n try , country o f c itiz e n s h ip , sex, number o f in s t it u ­
tions previously attended, previous in s titu tio n (s ) type,
highest level of math a tta in e d , number o f quarters enrolled
a t MSU, Associate Degree, age, time between p rio r in s t it u ­
tio n and Montana State U niversity)?
3.
How do p e rs is te r and non-persister tra n s fe r students in
engineering and technology programs compare on various
achievement oriented or "cognitive" variables ( i . e . , number
o f c red its accepted fo r tra n s fe r; cumulative GPA from p rio r
in s t it u t io n ( s ) ; ACT Q u a n tita tiv e , Verbal, and Composite
Aptitude Scores; and high school rank)?
4.
To what extent do various combinations of demographic
c h a ra c te ris tic s and cognitive variables contribute to
the prediction o f persistence of engineering and technology
tra n s fe r students?
5.
To what extent do various combinations o f demographic
c h a ra c te ris tic s and cognitive variables contribute to
the prediction o f the Montana State U niversity grade point
10
average fo r the engineering and technology students?
6.
To what extent do various combinations o f demographic
c h a ra c te ris tic s and cognitive variables contribute to
the prediction o f "low GPA non-persistence" ( i . e . , trans­
f e r students who leave the engineering program with a
cumulative GPA below 2.50)?
General Procedures
The problem o f th is study was approached in the follow ing man­
ner.
F ir s t , a ll tra n s fe r students who i n i t i a l l y enrolled in the
Montana S tate U n iversity College o f Engineering engineering and
technology programs during the Autumn, W inter, and Spring Quarters of
academic years 1977-78 and 1978-79 and Autumn Quarter 1979 were iden­
t i f i e d in th is study.
This was a to ta l o f 394 students; 316 in engi­
neering programs and 78 in technology programs.
The s p e c ific break­
down o f the population by year and quarter are presented in Tables 3
and 4.
Since th is population was o f a workable s iz e , no sampling was
required and sampling e rro r was avoided.
Both the technology and engineering populations were divided
in to two groups:
p e rs is te r and no n-persister.
A p e rs is te r was de­
fined as a tra n s fe r student who was enrolled in the same track (engi­
neering or technology) twenty-one months from in it a l entry or who was
graduated in the same track (engineering or technology) w ithin
11
twenty-one months from i n i t i a l e n try.
A non-persister was a student
who was not enrolled in the same track twenty-one months from i n i t i a l
entry and who did not graduate.
For example, a tra n s fe r student
who entered the college in the C iv il Engineering program and who trans­
fe rre d to the Construction Engineering Technology program during the
twenty-one month period was c la s s ifie d as a non-persister.
This d iffe r e n tia tio n between engineering and technology programs
was necessary to form homogeneous groups in order to obtain the most
powerful p re d ic tiv e model.
The d iffe re n c e between the two c u rric u la r
tracks was made on the basis o f rig o r.
Engineering programs require
more p roficien cy in mathematics and theory, while the technology
track stresses a p p lic a tio n .
This d iffe r e n tia tio n also enhanced the
u t i l i t a r i a n value o f the fin d in g s , allowing a more accurate comparison
with other engineering colleges which do not have technology programs.
The twenty-one month period e s s e n tia lly measures persistence
fo r two years.
The twenty-one month period is a precise d e fin itio n
of the point in time a t which the researcher designated each student
as a p e rs is te r or a no n-persister.
Table 2 displays th is point in
time fo r each entering group o f tra n s fe r students.
For b re v ity , the
study re fe rs to persistence a fte r "two years", but persistence was
a c tu a lly measured as twenty-one months from i n i t i a l entry in the
u n iv e rs ity .
12
Table 2
Persistence vs. Non-Persistence. Designation
Date Based Upon I n i t i a l Entry to Montana State
U niversity College o f Engineering
Quarter of I n i t i a l Entry
Autumn
Winter
Spring
Autumn
Winter
Spring
Autumn
1977
1978
1978
1978
1979
1979
1979
Quarter of Designation
Spring
Autumn
Winter
Spring
Autumn
Winter
Spring
1979
1979
1980
1980
1980
1981
1981
From the College of Engineering Dean's O ffic e , the follow ing
information was co llected on each tra n s fe r student by examining
the in divid ual student f il e s :
1.
In itia l
Quarter of Entry in Engineering
2.
In itia l
Year o f Entry in Engineering
3.
Curriculum Entered
4.
Country o f C itizenship
5.
Sex
6.
Year of B irth
7.
Number of In s titu tio n s Previously Attended
8.
Previous In s titu tio n Type (two y e ar, co lleg e, u n iv e rs ity )
9.
Date o f Last Attendance a t P rio r In s titu tio n
13
10.
Number o f Credits Accepted fo r Transfer by Montana State
U niversity
11.
Highest Level of Mathematics Attained a t P rio r In s titu tio n (s )
(None, Algebra, Trigonometry, Calculus)
12.
Montana State U niversity Cumulative Grade Point Average
(MSUGPA) a t Last Quarter o f Attendance or Two Years from
I n i t i a l Entry
13.
Number of Quarters Enrolled a t Montana State U niversity
From the Montana S tate U niversity R eg is trar's O ffice the
follow ing information was collected on every tra n s fe r student by
examining each student f i l e :
14.
Cumulative Grade Point Average (CGPA) from P rio r In s t it u tio n (s )
15.
Associate Degree or No Degree from P rio r In s t itu tio n (s )
From the Registrars O ffic e , American College Testing (ACT) and
Scholastic Aptitude Test (SAT) te s t scores were obtained.
scores were obtained on 143 students.
on 39 students.
the students.
ACT te s t
SAT te s t scores were obtained
Thus, aptitude te s t scores were gathered on 182 of
The follow ing were obtained:
16.
ACT and/or SAT Verbal Aptitude Score
17.
ACT and/or SAT Q u an titative Aptitude Score
18.
ACT and/or SAT Composite Aptitude Score
In addition to the above data, high school rank a t graduation
14
was obtained on 223 tra n s fe r students from examining the R eg istrar's
student records.
19.
Thus, the fin a l v a ria b le collected was:
High School Rank a t Graduation
Since the inform ation in items 16 through 19 is not required oh
a ll tra n s fe r students p rio r to admission, 39.6% (156/394) of the popu
la tio n had a complete set o f these four v a riab le s .
The high school
record and ACT or SAT aptitude te s t scores are required only on stu­
dents tra n s fe rrin g in to Montana State U niversity with less than one
f u l l academic year (45 quarter c red its or 30 semester c r e d its ).
A fte r compiling the information from each student f i l e and
coding the information on a general data sheet, the data were placed
on computer cards fo r s ta tis t ic a l analysis.
The s p e c ific s t a tis t ic a l
operations and the procedures to properly account fo r missing data
are discussed in Chapter 3.
Lim itations and D elim itations
1.
The study population was delim ited to tra n s fe r students
i n i t i a l l y e n ro llin g in the Montana State U niversity College
o f Engineering during the Autumn, W inter, and Spring Quar­
ters o f academic years 1977-78 and 1978-79, and Autumn
Quarter 1979; second degree students were omitted from
th is study.
2.
The tra n s fe r students were examined only fo r the two year
15
period a f t e r i n i t i a l l y e n ro llin g in the Montana State
U n iversity College o f Engineering.
3.
The study, considered only the selected variables which
have been s ig n ific a n t in past research or which appear to
have sig n ifican ce based upon prelim inary experience.
4.
Transfer students in the Computer Science curriculum were
excluded from th is study.
D e fin itio n o f Terms
Low GPA N on-P ersister:
A non-persister with a cumulative GPA
less than 2.50.
Native Student:
A student e n ro llin g in a four year in s titu tio n
as a freshman and who never attends another college or u n iv ers ity
( H ill s , 1965).
N on-P ersister:
A student who was not enrolled in the same cur­
r ic u la r track (engineering or technology) in the College o f Engineer­
ing twenty-one months a f t e r i n i t i a l enrollment in the College of
Engineering and who did not graduate.
P ersis te r :
A student who was enrolled in the same c u rric u la r
track (engineering or technology) in the College o f Engineering
twenty-one months a fte r i n i t i a l enrollment in the College of Engineer­
ing or who had graduated with a Bachelor o f Science in Engineering
or technology during twenty-one months a f t e r i n i t i a l enrollm ent.
16
Transfer Shock:
A drop in grade point average during the f i r s t
semester or quarter a t a four-year in s titu tio n from the cumulative
grade point average a t the p rio r in s titu tio n ( H ills , 1965).
Transfer Student:
A student who has withdrawn from one in s t it u ­
tio n and is admitted to another (Commission fo r Higher Education,
1968).
Voluntary Non-Persis t e r :
A student who has withdrawn from
college fo r any reason other than academic suspension.
Summary
With the projected decrease in the number o f students entering
higher education during th is decade, many in s titu tio n s are concerned
about th e ir enrollment le v e ls .
The lit e r a t u r e reveals wide variations
in conditions and trends among s p e c ific programs, in s titu tio n s , and
regions.
Engineering programs throughout the nation have grown a t an
unprecedented ra te .
The Montana State U niversity College of Engineer­
ing follows the same pattern with an increase of 115% in eigh t years.
This growth has placed considerable s tra in on the College and the
U n iv e rsity.
In an e f f o r t to e ffe c tiv e ly manage the academic programs,
the College of Engineering a c tiv e ly supported th is research.
Because
the tra n s fe r student population represents a sizeable percentage of
the to ta l college enrollment and since the a t t r i t io n ra te is considered
unacceptably high, th is study investigated the demographic character­
i
17
is tic s and the cognitive variables which d iffe r e n tia te p e rs is te r
tra n s fe r students from non-persister tra n s fe r students.
The results
have im plications regarding policy fo r admission o f tra n s fe r students
and fo r id e n tify in g those who need supportive services in order to
complete a Bachelor o f Science in Engineering.
CHAPTER 2
REVIEW OF RELATED LITERATURE
Introduction
The lit e r a t u r e pertaining to student a t t r it io n and tra n s fe r
students is extensive and sometimes contradictory.
In order to provide a c le a r presentation of lit e r a t u r e p ertin en t
to the study, th is chapter is organized under three main topics:
(a ) freshmen a t t r i t io n studies, (b) tra n s fe r student studies,
and (c) engineering a t t r i t io n studies.
Figure I g rap h ically repre­
sents the three areas re la tin g to the research associated with th is
study.
The discussion of these topics provides the reader with a com­
prehensive understanding o f the major research conducted in each topic
and the in te r r e la tio n of variables re la tin g to the prediction of per­
sistence of engineering tra n s fe r students.
In Chapter 5, comparisons
are made regarding the lit e r a t u r e and s p e c ific resu lts o f th is study.
Freshmen A t t r it io n Studies
To date, the m ajority o f student a t t r i t io n research has been
directed to the college freshmen.
I f f e r t , Summerskill, and Eckland
19
Figure I
Venn Diagram Describing the Relationship Among Research
in A t t r it io n of Transfer and Engineering Students
FRESHMEN
STUDENT
ATTRITION
TRANSFER
STUDENT
ATTRITION
ENGINEERING
STUDENT
ATTRITION
ATTRITION OF TRANSFER STUDENTS IN ENGINEERING
have provided the most s ig n ific a n t findings relevan t to the th e o re tic a l
aspects of a t t r i t i o n , es p e cia lly the involuntary non-persisters.
T in to ,
P ascarella, and Terenzini have conducted the most recent research add­
ing to the th e o re tic a l base fo r understanding voluntary non-persisters.
A description of th e ir research follows in th is section.
I f f e r t (1957, 1965), under the sponsorship of the Department
of H ealth, Education, and W elfare, conducted extensive research on
freshmen dropouts.
In 1950 he surveyed a sample of 12,667 freshmen
who m atriculated a t 149 colleges and u n iv e rs itie s .
He found th a t 39.5%
20
of the students graduated in four years and th a t approximately one
h a lf o f the dropouts occurred during the f i r s t year of c o lleg e.
In
a follow -up study o f twenty of the o rig in a l 147.in s titu tio n s , he
surveyed over 10,000 students entering college in the f a l l o f 1956
and 1957 to determine the reasons fo r dropping out.
reasons were c ite d :
Three major
(a ) academic d i f f i c u l t y , (b) fam ily and health
problems, and (c ) fin a n c ia l concerns.
Summerskill (1962) reviewed t h i r t y - f iv e a t t r i t io n studies conduc­
ted p rio r to 1962.
He found th a t re te n tio n rates maintained a
r e la tiv e ly stable le v e l.
He concluded th a t approximately 40% of the
students graduate w ith in four years from the o rig in a l in s titu tio n ,
20% graduate from the o rig in a l or another in s titu tio n a f t e r four years,
and the remaining 40% never complete the baccalaureate.
In 1962, Eckland conducted a ten year follow -up study o f 1,332
students who dropped out o f the U niversity of I l l i n o i s .
T h irty -s ix
percent of th is group completed a baccalaureate degree in four years,
while an addition al fourteen percent graduated a fte r more than four
years a t the U niversity of I l l i n o i s or another in s titu tio n .
Eckland
concluded th a t most researchers overestimate student a t t r i t io n rates.
The American Council on Education has sponsored the most extensive
student a t t r i t io n research o f record.
Alexander W. Astin (1971, 1972, 1975).
The research was directed by
During the f a l l 1966
o rie n ta tio n and re g is tra tio n period, f ir s t - t im e entering freshmen a t
21
each o f 217 in s titu tio n s , representing a to ta l sample o f 51,721 stu­
dents, were surveyed.
The major fin ding p e rtin e n t to th is study was th a t persistence
rates vary g re a tly among colleges.
However, the average persistence
ra te was 40% which supports the findings o f I f f e r t .
Of the in s t it u ­
tio n a l types studied, Astin noted th a t two year college students had
the highest dropout ra te .
persistence are:
He found th a t the p rin c ip le predictors of
( I ) good high school grades and high academic
a b il it y te s t scores, (2) being a male and a non-smoker and (3) having
a high degree of as p ira tio n and not being employed.
He also suggested
th a t accepting students' responses a t face value was often question­
able.
The s e lf-p e rce ive d expectations are often quite d iffe r e n t from
r e a lit y .
In the e a rly 1970's, the emphasis on a t t r it io n research moved from
d e s c rip tiv e analysis o f p rim a rily the involuntary dropout (academic
and personal problems) to a th e o re tic a l approach accounting fo r
voluntary dropouts.
Tinto provided the f i r s t research o f the soci­
ological factors of non-persistence.
His work was fu rth e r supported
by the research of Pascarella and Terenzini (1977); H. C. Rose (1981);
G reenfield , Holloway, and Remus (1981); and Elton and H. A. Rose (1967)
Tinto (1975), a f t e r examining many e a r lie r a t t r i t io n studies,
formulated an a t t r i t io n model based on the concepts drawn by Durkheim's
theory o f suicide.
B a s ic a lly , Durkheim's theory states th a t suicide
22
happens when two in teg ra tin g processes, value and a f f i l i a t i o n , are
lacking.
T in to 's model provides a conceptual framework fo r under­
standing a t t r i t io n as a lo n g itu d in a l, p re d ic tiv e process involving a
complex m atrix o f in te rre la te d v a riab le s .
Tinto described the process o f a t t r i t io n as follow s:
Students
bring c e rta in personal c h a ra c te ris tic s to college, ( i . e . , fam ily
background, personality a ttrib u te s , academic a p titu d e , and goal .
commitments).
These personal c h a ra c te ris tic s in te ra c t with the
p a rtic u la r college environment leading to a c e rta in level o f in te ­
gration into the academic and social systems of the in s titu tio n .
The
higher the level o f in te g ra tio n , the less lik e ly the student is to
v o lu n ta rily leave the in s titu tio n .
Although fam ily background, in ­
dividual a b i l i t y , and goal commitment are important facto rs re la tin g
to persistence, Tinto contends th a t the central fa c to r to persistence
is the students' non-classroom in te ra c tio n with fa c u lty .
This in te r ­
action in teg rates a student's values with the in s titu tio n and creates
an accepting atmosphere.
In order to te s t T in to 's model, Pascarella and Terenzini (1980,
1977) conducted research on the in te ra c tio n of students with fa c u lty .
They surveyed over 2,400 freshmen entering Syracuse U niversity in
1975.
The research design, which encompassed a pre-enrollm ent
questionnaire, a twelve dimension personality inventory, and two
follow -up questionnaires six and twelve months a fte r i n i t i a l e n try.
23
provided data to support T in to 's model.
In addition to fin ding th at
s tu d e n t-fa c u lty contact is a good p red icto r of persistence, th e ir
re su lts in d icate th a t the type o f in te ra c tio n is important.
The
primary fa c to r which discrim inates students v o lu n ta rily withdrawing
from college is the amount o f discussion time with fa c u lty regarding
(a) in te lle c tu a l or course re la ted m aterial and (b) the student's
career concerns.
This fin ding has also been supported by Rose (1981)
To summarize the freshmen student a t t r i t io n research, I f f e r t ,
SummerskilI , Eckland, and Astin have provided the most s ig n ific a n t
data.
Their research helps one understand the c h a ra c te ris tic s o f
dropouts and the factors which p red ict involuntary non-persistence.
More re c e n tly , T in to 's model o f a t t r i t io n provides an explanation fo r
the factors associated with voluntary non-persisters.
Pascarella and
Terenzini have provided supportive research in dicating th a t s p e c ific
types o f in te ra c tio n with fa c u lty enhance reten tion of freshmen
students.
Transfer Student Studies
Most o f the research on tra n s fe r students has been conducted
since the advent of the community college movement spanning the past
twenty years.
Following World War I I , a few s ig n ific a n t studies were
conducted on the c h a ra c te ris tic s and success of veterans (Holms, 1961
Rodes, 1949; Sammartino arid Byrke, 1947).
P rio r to World War I I , no
24
research was found re la te d to the tra n s fe r student, since there were
few two year colleges and since the m o b ility of students between
in s titu tio n s was minimal.
As the number of tra n s fe r students grew to a s ig n ific a n t popula­
tio n , researchers began to examine the c h a ra c te ris tic s , a b i l i t y , per­
formance, and problems o f the tra n s fe r student.
Since 1960, the
tra n s fe r student research has focused on three areas:
(a) academic
success o f tran sfers versus native students, (b) descrip tive analysis
o f tra n s fe r student types, and (c ) a rtic u la tio n problems created by
the tra n s fe r process.
L i t t l e research has focused on the prediction
of tra n s fe r student a t t r i t io n .
In order to provide a comprehensive analysis o f the p e rtin e n t
lit e r a t u r e , a general description o u tlin in g the major tra n s fe r student
research by K ills , Knoell and Medsker, Peng, and others w ill be
provided in th is section.
Their findings have been supported by
others wbo have conducted research with a more lim ite d scope.
Through
out th is section, demographic and cognitive variables which tend to
p red ict persistence w ill be discussed.
In 1965, H ills reviewed more than twenty studies involving
community college tra n s fe r students and th e ir academic performance a t
four year in s titu tio n s .
He discovered a drop in grade point average
during the f i r s t term a f t e r entering a four year in s titu tio n from a
two year college.
This i n i t i a l decline in academic performance, when
25
compared with the grades attain ed a t the two year in s titu tio n , was
termed "tra n s fe r shock".
He also noticed a general trend of gradual
improvement of the grade point average a t the four year in s titu tio n
over subsequent quarters. . This was labeled "grade recovery".
His
findings were also supported by Burke (1973), Willingham (1972),
Western (1970), and Falkenberg (1969).
The most extensive research on the academic performance and
a t t r i t io n of ju n io r college tran sfers a t senior in s titu tio n s was conducted by Knoell and Medsker (1965, 1964a, 1964b).
Their national
study, comparing ju n io r college tran sfers with native students, in ­
volved 7,243 ju n io r college tra n s fe r students from 345 two year in s t i­
tutions who attended 43 four year colleges or u n iv e rs itie s .
The
research methodology included examination of tra n s c rip ts , adm inistra­
tio n o f questionnaires to students and to in s titu tio n s , and personal
interviews with students.
A summary of th e ir fiv e major findings are
presented together with supporting resu lts from less extensive studies.
1.
Junior college students often earn lower grades than native
students in upper d ivis io n coursework ( KnoelI and Medsker, 1965).
This
fin ding is supported by others (Dragon, 1981; Holahan and K elley, 1976;
Kennedy, 1975; Hodgson and Dickinson, 1975; Sloan and F a r r e lly , 1972;
K ille n , 1968).
2.
Junior College students usually experience some drop in
grade point average in th e ir f i r s t term a fte r tra n s fe r, receiving a
26
GPA below the cumulative average they earned. in ju n io r college.
The
grades o f tra n s fe r students who p e rs is t in the four year colleges
generally improve in successive terms ( Knoell and Medsker, 1965).
This fin d in g is supported by other studies (Gold, 1979; Il lin o is
Community College Board, 1977, 1976; Kennedy, 1975; Nolan and H a ll,
1978; Johnson, 1974; Cromwell, 1974; Schade, 1974; Snyder and Blocker,
1970; Mince, 1968).
3.
Grades a t a community college are the best predictor of
academic success a t a four year in s titu tio n ( Knoell and Medsker,
1964).
Agreement is again found in the lit e r a t u r e (Nolan and H a ll,
1978; Holahan and K elle y , 1978; N ickels, 1972; Wray and Leischuck,
1971; Snyder and Blocker, 1970; Mince, 1968; Lee and Suslow, 1966;
Sims, 1966).
4.
Students who complete the Associate Degree progress as well
as native students.
For students who did not complete the Associate
Degree, the more hours earned a t the two year school, the b e tte r
th e ir grade point average a t the four year in s titu tio n ( Knoell and
Medsker, 1964b).
The completion of the Associate Degree is a strong
predictor of academic success (Thomas, 1972; Wray and Leischuck, 1971;
Snyder and Blocker, 1970; Zimmerman, 1968; Spangler, 1966; Lea and
Suslow, 1966; McKenzie, 1965; K litz k e , 1961).
5.
Two year college tra n s fe r students with a grade point aver­
age of 2 .5 or less have the most s ig n ific a n t drop in grade point
27
average and have a low graduation ra te ( Knoell and Medsker, 1965).
Cesa (.1980), Howell (1 980), and Holahan and K elly (1976) also found
.
th a t a low grade point average a t the two year in s titu tio n is a strong
p re d ic to r o f non-persistence.Many studies have been conducted u t iliz in g the data base of the
National Longitudinal Study of the High School Class o f 1972.
This
survey was in it ia t e d by and is kept current by the National Center
fo r Educational S ta tis tic s .
This data base consists of 20,000 seniors
from 1,800 high schools which have been selected by a s t r a t if ie d
random sample process.,
Using th is data base, Peng (1977; Peng and
B aile y , 1977) examined tra n s fe r students in four categories:
( I ) v e rtic a l tran sfers (2 year to 4 year c o lle g e s ), (2) horizontal
tran sfers (4 year to 4 year c o lle g e s ), (3) reverse tran sfers (4 year
to 2 year c o lle g e s ), and (4 ) open-door tran sfers (2 year to 2 year
c o lle g e s ).
Since the major group in the study was the v e rtic a l trans­
fe rs , the findings emphasized the performance and c h a ra c te ris tic s of
the two year tran sfers to the four year in s titu tio n .
He found th at
these v e rtic a l tra n s fe r students, when compared with n ative students,
had a lower socioeconomic statu s, lower high school grades and aptitude
te s t scores, and lower educational a s p ira tio n s.
He also found a
general tendency o f ju n io r college students to receive lower grades
in upper d iv is io n than tra n s fe r students from four year colleges.
Often, tra n s fe r students from other four year in s titu tio n s receive
28
b e tte r upper d iv is io n grades than ju n io r college students.
This
fin d in g is supported by others (DeWolf, 1980, 1979; M artinko1 1978;
Holahan and K elle y , 1976; Kennedy, 1975; K itzn er, 1974; Thomas, 1972;
Snyder and Blocker, 1970; Mann, 1971; Hartman, 1968; Spangler, 1966;
McKenzie, 1965).
The above three studies conducted by H ill s , Knoell and Medsker,
and Peng represent the major research on tra n s fe r students.
A review
o f the lit e r a t u r e reveals an abundance o f less extensive research,
which taken together supports the findings o f the three other studies
and which in d iv id u a lly add to the understanding o f the c h a rac te ris tic s
and performance of tra n s fe r students.
The most noteworthy statewide research studies of tra n s fe r stu­
dents are:
U niversity o f Washington Educational Assessment Center
(DeWolf, 1980, 1979; Lunneborg and Lunneborg, 1975; Hodgson and
Dickinson, 1975), State U niversity o f New York (SUNY, 1980, 1979,
1975), I l l i n o i s Community Board (1977, 1976; Moughamian, 1978),
C a lifo rn ia Community College System (1979, Sheldon and Hunter, 1980),
Pennsylvania (M artinko, 1978), New Jersey (M ille r , 1976), and
Massachusetts (Beals, 1975).
Many in s titu tio n s have conducted local studies to examine.the
performance and c h a ra c te ris tic s of the tra n s fe r students.
comprehensive are:
The most
Los Angeles Community College (Gold 1980a, 1980b,
1979, 1968), Lorain County Community College (Tomsick, 1979), Old
29
Dominion (Howell, 1980), W illiam Rainey Harper College (1974), Univer­
s ity o f Texas a t Austin (Holahan and K elley, 1974), Southwest Missouri
State U niversity ( Schade, 1974), U niversity of Missouri (1975; Smalley,
1975), Florida A & M (Abraham, 1975), Manoa Community College (Hawaii
U n iv e rs ity , 1975), Marshall U niversity (Nolan and H a ll, 1974),
Appalachian State Teachers College (1968).
The s ta te and in s titu tio n a l studies, coupled with other individual
studies, provide a wealth of information about the c h a rac te ris tic s
and performance of tra n s fe r students.
The resu lts o f these studies
are sometimes c o n tra d ic to ry , though two noteworthy generalizations
emerge.
;
F ir s t , aptitude te s t scores, high school rank, and other cogni­
tiv e variab les provide strong indications of fu tu re academic perfo r­
mance in upper level college studies.
Second, non-cognitive and
demographic variables are less powerful predictors o f academic persis­
tence.
These statements apply to other a t t r i t io n studies as supported
by the follow ing:
LeBold and Shell (1980), Lamb and Durio (1980),
Haviland (1979), Holahan and Kelley (1978), Comptroller General of
the United States (1977), Pedrini and Pedrini (1974), Snyder and
Blocker, (1970), Van Erdewyk (1968), and Sims (1966).
Many researchers have examined the academic performance o f stu­
dents in various c u rric u la .
There is a great d iffe re n c e in the
performance of tra n s fe r students, depending upon the c u rric u la and
30
in s titu tio n .
Newlon and Gaither (1980) found th a t engineering trans­
fe r students had b e tte r persistence rates than students in other
c u rric u la .
However, Johnson (1974) and Hartman (1968) found that
engineering tra n s fe r students received the poorest grades, resu lting
in academic suspension.
Gender has also provided inconsistent resu lts in the prediction
of persistence.
Women receive higher grade point averages before and
a f t e r tra n s fe r (C a lifo rn ia State Postsecondary Education Commission,
1979; Sloan and P a rre lI y , 1972; Lambe, 1965; McKenzie, 1965).
However,
Foster (1976) and O tt (1978b) found no d ifferen ce in the performance of
male and female engineering students.
In many tra n s fe r student studies, other non-cognitive variables
have been u t iliz e d to a s s is t in the prediction o f persistence and
academic performance.
Although they have been examined, age, quarter
of e n try , number o f cred its accepted by the senior in s titu tio n , and
number o f in s titu tio n s attended, have not provided consistent re s u lts .
Other variables such as the highest level of mathematics attained
p rio r to tra n s fe r, the frequency o f curriculum changes, and the period
of time between the lower and upper d iv is io n work, have not been
reported in previously published research.
To summarize, considerable research has been conducted on the
c h a ra c te ris tic s and performance of tra n s fe r students.
The m ajority
o f lit e r a t u r e examines the tra n s fe r shock phenomenon, the comparison
31
o f tra n s fe r students with native students, the comparison o f types of
tra n s fe r students, and the factors re la te d to persistence and academic
success.
Engineering A t t r it io n Studies
The review of the lit e r a t u r e reveals th a t published a t t r it io n
studies of engineering students are confined to the entering freshman
student.
Tp date, the a t t r i t io n o f engineering tra n s fe r students has
apparently not been c a re fu lly researched.
A fte r conducting a national
a t t r i t io n study o f m inority students, the Committee on M in o ritie s in
Engineering (1977) noted the "lack o f d e ta ile d information th a t most
engineering colleges have regarding the reten tion of stud en ts.11
Perhaps the overcrowding of engineering schools is the primary reason
fo r lack o f in te re s t in the a t t r it io n o f engineering students.
The
follow ing discussion w ill summarize the engineering a t t r i t io n studies
p e rtin e n t to th is study.
Ahmann (1955) investigated the performance of 804 male engineer­
ing students who were enrolled in 1946.
The primary purpose o f this
followup study was to examine the performance of World War T I veterans
and ju n io r college students.
Referring only to engineering students,
he concludes th a t "Junior college students, both veterans and non­
veterans, have a lower graduation ra te than native students."
In 1968, Van Erdewyk surveyed 430 freshmen males enrolled in the
32
College o f Engineering a t the U niversity of North Dakota.
Findings,
which were la t e r supported by Elkins and Luetkemeyer (.1974), revealed
th a t p ersisters had s ig n ific a n tly higher mean ACT composite scores,
higher high school grades, higher f i r s t semester college grades, and
higher grades in the f i r s t college chemistry course than non-persiste rs .
Also, engineering students had a high dropout ra te i f they had
less than a standard ACT mathematics score of 26, a 2.80 grade point
average in high school, or a 2.05 f i r s t semester college grade point
average.
The most extensive a t t r i t io n study of engineering students was
conducted by Foster (1976) a t the U niversity of Pennsylvania.
He
surveyed 2,563 freshmen a t 39 eastern in s titu tio n s in the Spring of
1973.
He discovered a gross a t t r i t io n ra te of 23% fo r engineering
freshmen during the f i r s t year of college.
He found th a t m otivation,
strong high school records, and commitment to engineering were the
c h ie f c h a ra c te ris tic s d iffe r e n tia tin g persisters from non-persisters.
This is also supported by G reenfield (1964) and Durio and Kildow
(1980).
He also noted a large discrepancy between students' avowed
expectations and a c tu a lity .
Foster's re su lts were supported by O tt (1978a) who examined a
sample o f students taken from a population o f 17,739 engineering .
students and 42 schools in the northeastern United States.
In addi­
tio n to supporting the factors o f m otivation and academic a p titu d e,
33
she found th a t grades o f men and women were the same a t the end of the
f i r s t quarter o f c o lle g e , although women received b e tte r grades in
high school.
LeBold and Shell (1980) and Lamb and Durio (1980) conducted the
most recent research.
They concluded th a t cognitive variables ( i . e . ,
high school grades, high school rank, and aptitude te s t scores) are
the strongest predictors o f re te n tio n .
P re-co lleg e, non-cognitive
variables are less r e lia b le predictors o f persistence and grade point
average.
Lamb and Durio add, "The score on mathematic aptitude tests
is the single most important predictor o f completion (R
= .4 5 )."
Elkins and Luetkemeyer (1974) found th a t the SAT mathematics score
did d iffe r e n tia te persisters from non-persisters.
Summary
This chapter has reviewed the major research o f the past t h ir t y
years pertaining to three topics:
freshman a t t r it io n studies, tra n s fe r
student studies, and engineering a t t r i t io n studies.
A review of
selected lit e r a t u r e reveals a wealth o f information regarding a t t r it io n
and tra n s fe r students.
Yet factors which c o rrec tly p red ict success a t
one in s titu tio n may be u n re lia b le predictors a t another.
Because of
the v a r ia b ilit y o f human behavior and the differences among in s t it u ­
tio n s , u n iv ers a lly r e lia b le predictors evidently do not e x is t.
Pub­
lished research directed a t the a t t r i t io n of engineering students was
34
lacking p rio r to the completion of th is study.
CHAPTER 3
PROCEDURES
Introduction
This study was designed to contribute to the Montana State Uni­
v e rs ity College o f Engineering's e ffo r ts to increase it s understanding
of and re te n tio n of tra n s fe r students.
The problem o f th is study was
to in ve s tig ate variab les th a t d iffe r e n tia te p e rs is te r and non-persiste r tra n s fe r students who i n i t i a l l y entered the Montana State Univer­
s ity College o f Engineering during the 1977-78 and 1978-79 academic
years and Autumn Quarter 1979.
The re su lts of th is study have added
to the sparse inform ation regarding the a t t r i t io n of tra n s fe r
engineering students.
In a d d itio n , the College of Engineering may
gain basic inform ation to aid in the proper advisement o f tran s fe r
students.
The regression models developed in th is research w ill
provide a s ig n ific a n t step towards the form ulation of a model to
p red ict persistence and the cumulative grade point average of incoming
tra n s fe r students.
This chapter ou tlines the s p e c ific procedures which were used in
the study, and provides the reader with a thorough understanding of
the scope, methods, s t a t is t ic a l procedures, and analysis associated
with the solution of the research problem.
te r are:
The topics o f th is chap­
description o f the population, method of c o lle c tin g data,
coding o f the variab les fo r data gathering, conversion o f SAT to ACT
36
scores, coding o f the variab les fo r s ta tis t ic a l analysis, major
research questions and s t a tis t ic a l methods, analysis of data, pre­
cautions taken, fo r accuracy, e th ic a l standards, and summary.
Description of the Population
The M.S.U. Admissions O ffic e defines a tra n s fe r student as any
student who has withdrawn from another college or u n iv e rs ity and is
admitted to Montana S tate U n iv e rs ity .
This study examines a ll trans­
fe r students pursuing th e ir f i r s t bachelor degree who i n i t i a l l y
entered Montana S tate U n iversity in the College of Engineering during
the follow ing quarters:
Autumn, 1977; W inter, 1978; Spring, 1978;
Autumn, 1978; W inter, 1979; Spring, 1979; and Autumn, 1979.
The to ta l population o f th is study was 394 students.
Approxi­
mately 80% o f the population were enrolled in engineering programs
and almost 20% were enrolled in technology programs.
Twenty-seven
of the tra n s fe r students were female and a ll were enrolled in engi­
neering c u rric u la .
Due to the gradual increase in tra n s fe r student
enrollm ent, the percent of students who i n i t i a l l y enrolled during
1979 (41.2%) was greater than the 25.6% level of 1977 (see Table 3 ).
A review o f Table 4 indicates th a t 84% of the population entered
during the Autumn quarters.
A small percent (3.6%) entered during
the Spring quarters.
With respect to the c u rric u la r track entered, 80.2% enrolled in
37
Table 3
College o f Engineering Transfer Student Population
By Year o f Entry and C u rric u la r Track
(N = 394)
YEAR OF ENTRY
1977
N
%
Sub-Population
1978
N
%
1979 .
N
%
Total
N •
%
Engineering
77
19.5
105
26.6
134
34.1
316
80.2
Technology
24
6.1
26
6.6
28
7.1
78
19.8
101
25.6
131
33.2
162
41.2
Total
394 100.0
Table 4
College o f Engineering Transfer Student Population
By Quarter of Entry
(N = 394)
QUARTER OF ENTRY
Sub-Population
N
Autumn
% of T
N
Winter
% of T
I
N
Spring
% of T
Total
% of T
N
Engineering
265
67.3
40
10.1
11
2.8
316
80.2
Technology
66
16.7
9
2.3
3
.8
78
19.8
331
84.0
49
12.4
14
3.6
394
100.0
Total
38
engineering c u rric u la and 19.8% enrolled in technology programs.
5 summarizes the enrollment by s p e c ific programs.
Table
C iv il Engineering
had the la rg e s t percent o f enrollment with 22.8% of the to ta l popula­
tio n .
In d u s tria l Management Engineering represented the sm allest en­
rollm ent with 2.8% o f the population.
I t should be noted th a t two small groups were excluded from th is
study.
F ir s t , tra n s fe r students entering engineering during the
Summer quarter o f 1978 and 1979 were excluded from the study.
To in ­
clude these 13 addition al students would have required separate con­
sideration o f the defined two year period fo r defining persistence,
and hence separate s t a tis t ic a l an alysis.
Second, 20 tra n s fe r students
entering Computer Science were excluded from th is study.
A fte r
consulting with members of the Engineering fa c u lty , agreement could
not be reached as to how Computer Science students should be c la s s i­
fie d .
Since Computer Science had c u rric u la r c h a ra c te ris tic s of both,
the researcher chose to elim inate the 20 students in order to avoid
contamination o f e ith e r of the two sub-populations.
No sampling techniques were used in th is study because the study
population o f 394 tra n s fe r students in engineering and technology
c u rric u la was o f workable s ize .
sampling e rro r was avoided.
.
By using the e n tire population,
Of course, the group o f 394 students may
i t s e l f be considered a sample of a ll p o ten tial tra n s fe r students.
39
Table 5
Description o f the Population by C u rricu lar Program
N
Percent of
C urricular
Track
Percent o f
Total
Population .
Engineering Program
A g ric u ltu ra l Engineering
16
Chemical Engineering
42
13.3
10.6
C iv il Engineering
90
28.5
22.8
E le c tric a l Engineering
73
23.1
18.5
Engineering Science
29
9.1
7.4
In d u s tria l Management
Engineering
11
3.5
2.8
Mechanical Engineering
55
17.4
14.0
316
100.0
SUBTOTAL
5.1 T '
4.1
80.2
■ *■
Technology Program
Construction Engineering
Technology
35
46.2
9.1
E le c tric a l and Electronics
Engineering Technology
20
25.6
5.1
Mechanical Engineering
Technology
22
28.2
5.6
78
100.0
19.8
SUBTOTAL
TOTAL
394
100.0
40
The question of sample r e l i a b i l i t y is then equivalent to questioning
the homogeniety o f the two groups and the accuracy o f the prediction
model, fo r which s t a tis t ic a l analysis is described in Chapter 4.
Method o f C ollecting Data
A ll o f the data associated with th is study was co llected person­
a lly by the researcher who examined in divid ual students' f il e s in the
College o f Engineering and in the R e g is tra r's O ffic e .
This allowed
the researcher to probe fo r data which may be overlooked by using com­
puter search methods.
The study population was determined by examination o f the perma­
nent records in the College of Engineering.
A fte r a l i s t of appro­
p ria te tra n s fe r students and th e ir respective social s e cu rity numbers
was obtained, the information was cross-referenced with the
R e g is tra r's O ffice A ll Campus L is t to check fo r errors and omissions.
A fte r defining the population of 394 tra n s fe r students, data were
co llected by hand from student f il e s in the College o f Engineering
and the R e g is tra r's O ffic e .
A fte r a ll data were collected and coded,
as described in the next section, each student was assigned an anony­
mous id e n tific a tio n number.
The D irecto r of In s titu tio n a l Research
a t M.S.U. kept the master l i s t of student names, social security
numbers, and id e n tific a tio n numbers u n til a ll research was completed.
He then destroyed the master l i s t to preserve anonymity.
The research
41
er was aware o f, and observed, federal privacy laws re levan t to student
records throughout th is research.
From the College of Engineering Dean's O ffic e , the follow ing
information was co llected on each tra n s fe r student.
1.
In itia l
Quarter of Entry in Engineering
2.
In itia l
Year o f Entry in Engineering
3.
Curriculum Entered
4.
Country o f C itizenship
5.
Sex
6.
Date of B irth
7.
Number o f In s titu tio n s Previously Attended
8.
Previous In s titu tio n Type (two year, c o lleg e, u n iv e rs ity )
9.
Date of Last Attendance a t P rio r In s titu tio n
10.
Number o f Credits Accepted fo r Transfer by Montana State
U niversity .
11.
Highest Level of Mathematics Attained a t P rio r In s titu tio n (s )
(none, algebra, trigonom etry, calculus)
12.
Montana State U niversity Cumulative Grade Point Average
(MSUGPA) a t Last Quarter o f Attendance or Two Years from
I n i t i a l Entry
13.
Number o f Quarters Enrolled a t Montana State U niversity
From the Montana State U niversity R eg istrar's O ffice the fo llo w ­
ing information was co llected on every tra n s fe r student.
42
14.
Cumulative Grade Point Average (CGPA) from P rio r In s t it u ­
tio n (s)
15.
Associate Degree or No Degree from P rio r I n s t i t u t i on(s)
. Also from the R e g is tra r's O ffic e , American College Testing (ACT)
and/or Scholastic Aptitude Test (SAT) te s t scores were obtained on 182
tra n s fe r students, 145 engineering students arid 37 technology students.
The follow ing were obtained:
16.
ACT and/or SAT Verbal Aptitude Score
17.
ACT and/or SAT Q u an titative Aptitude Score
18.
ACT and/or SAT Composite Aptitude Score
In addition to the above data, high school rank a t graduation was
obtained on 223 tra n s fe r students by examining the R eg is trar's stu­
dent records.
19.
The fin a l v a riab le collected was:
High School Rank
Coding o f the Variables fo r Data Gathering
Dependent (C rite rio n ) Variables
Two dependent or c r ite rio n v a ria b le s , used in d iv id u a lly , were
u tiliz e d in the stepwise m u ltip le regression analysis o f th is re ­
search.
They were:
(a ) pers1stence/non-pers1$tence and (b) Montana
State U niversity cumulative grade point average (MSUGPA).
coded as follow s:
They were
43
A.
coded as:
Persistence/Non-persistence.
Each o f the subjects were
JD id e n tify in g non-persisters as those who were not en­
ro lle d in the same c u rric u la r track (engineering or technology)
twenty-one months a f t e r i n i t i a l enrollment and who were not graduates
from the College of Engineering; and
id e n tify in g persisters as
those who were enrolled in th e ir o rig in a l c u rric u la r track and those
who graduated during the twenty-one month period a f t e r i n i t i a l e n r o ll­
ment in the College of Engineering.
B.
Montana State U niversity Cumulative Grade Point Average.
This v a ria b le was expressed as the actual cumulative MSUGPA a t the
completion o f the twenty-one month period or a t the end o f the fin a l
quarter of attendance p rio r to departing from engineering.
Independent Variables
1.
I n i t i a l quarter entered M .S.U.
the month of entry:
2.
This v a riab le was coded as
9^ - Autumn, 1_ - W inter, and _3 - Spring.
Country of C itiz e n s h ip .
Binary coded v a ria b le , 0 id e n ti­
fying USA and Canadian c itiz e n s and 1_ id e n tify in g students o f other
countries or c itiz e n s h ip .
3.
Sex.
Binary coded, (I - id e n tifie d the males and 1_ - id e n ti­
fie d the females.
4.
Age.
entry a t M.S.U.
This v a riab le is the student's approximate age a t
This was computed by programming the computer to
44
subtract the year of. b irth from the.year o f entry re s u ltin g in the
approximate age in years.
5a.
Engineering c u rric u la entered.
The standard MSU two d ig it
c u rric u la code was recorded as follow s:
05
20
30
50
65
75
80
5b.
-
A g ric u ltu ra l Engineering
Chemical Engineering
C iv il Engineering
E le c tric a l Engineering
Engineering Science
In d u s tria l and Management Engineering
Mechanical Engineering
Technology c u rric u la entered.
The standard MSU two d ig it
c u rric u la code was recorded as follow s:
45 - Construction Engineering Technology
60 - E le c tric a l and Electronics Engineering Technology
90 - Mechanical Engineering Technology
6.
Number of. in s titu tio n s previously attended.
This v ariab le
is the actual number o f c o lle g ia te in s titu tio n s previously attended.
7.
Previous in s titu tio n type.
This was coded by:
- two year
c o lleg e, 2 - four year c o lleg e, and 3^ ~ four year u n iv e rs ity .
In
instances where a subject attended more than one in s t it u t io n , the
coding was based upon the type o f in s titu tio n a t which the m ajority
o f c red its were completed.
8.
Time between p rio r in s titu tio n and entry a t M.S.U.
The
number o f months between the time of departure a t the p rio r in s t it u ­
tio n and entrance a t M.S.U. was calculated by programming the computer
to subtract the month, year of la s t attendance from the month, year of
45
entrance a t M.S.U.
9.
Number o f c red its accepted by M.S.U.
This v a ria b le is the
actual to ta l number of c red its e lig ib le fo r tra n s fe r in to the Univer­
s ity as evaluated by the Admissions O ffic e .
10.
Highest level of mathematics a ttain ed a t in s titu tio n (s )
p rio r to attending M.S.U.
This v a ria b le was coded:
1_ - No math, ;2 -
Algebra, _3 - Trigonometry, and 4 - Calculus.
11.
M.S.U. cumulative grade point average as of the student's
la s t quarter o f attendance during the period of study.
This v ariab le
is expressed as the actual cumulative GPA p rio r to departure or end o f
the period o f study.
12.
Cumulative grade point average from p rio r in s t it u t io n ( s ) .
This was coded as the actual grade point average earned p rio r to
entering M.S.U.
In cases where subjects attended more than one in s t i­
tu tio n , the grades from each in s titu tio n were.combined to y ie ld one
value.
A ll grade point averages are expressed on the 4 .0 scale.
When necessary, conversions were done to express the value on the 4.0
scale.
13.
Graduate or non-graduate o f the p rio r in s t it u t io n .
je c t who obtained an Associate Degree was coded as 1_.
coded as 0.
population.
A sub­
A ll others were
Bachelor Degree holders are excluded from the study
46
14-16.
ACT/SAT V erbal, Q u a n tita tiv e , and Composite Aptitude Test
Scores.
This v a riab le was coded according to the ACT standard score
(Range:
1 -3 6 ).
Students with only SAT scores (N=39) were converted
to ACT using the method described in the follow ing section.
17.
High school rank.
This is the student's rank a t graduation
expressed as a p e rc e n tile (I=Iow to 99=high).
Conversion o f SAT Scores to Act Scores
In examining the R eg is trar's records to obtain aptitude te s t
scores, data was obtained on 143 students having ACT te s t scores.
An addition al 39 students who did not have ACT scores were found to
have SAT te s t scores.
Rather than elim inate these 39 students from
the study, the researcher examined various methods of converting
SAT standard scores to ACT standard scores and made the conversion,
by hand, on these 39 students.
The f u l l method is found in Appendix A.
B a s ic a lly , the ju s t if ic a tio n o f th is decision is based upon the
follow ing inform ation.
Langston and Watfcins (1980) formulated SAT-ACT equivalents by
c o rre la tin g scores earned by 12,014 students who took both the ACT
and SAT between January I , 1978, and December 31, 1979.
This popula­
tio n was drawn from students who applied fo r admission a t the UrbanaChampaign or the Chicago C irc le campus o f the U niversity o f I l l i n o i s .
They made th is assumption:
"Since both tests were developed to predict
47
performance in c o lleg e, i t was assumed th a t a high c o rre la tio n be­
tween them would tend to in d icate th a t the two tests were e s s e n tia lly
measuring the same th in g ."
Their co rrelatio n s which were s ig n if i-
cant a t the .01 level are as follow s:
Table 6
C orrelations o f Selected ACT Tests with SAT Tests
SAT TEST
ACT TEST
Q u an titative
Mathematics
,8 4 9 **
Verbal
English
.7 33**
Total Q u an titative
and Verbal
Composite
.833**
PEARSON CORRELATION
**p < .01
With respect to th is research, 39 SAT scores were transformed
in to ACT equivalents by u t iliz in g the values found in Appendix A.
Thus, 21.4% of the 182 te s t scores are transformed SAT te s t scores.
Coding o f Variables fo r S ta tis tic a l Analysis
The aforementioned coding procedure describes the method which
was used to code data fo r c o lle c tin g information from student f i l e s .
Binary and continuous variab les were already appropriate fo r regres­
sion an alysis.
However, non-continuous, non-ordered variab les such
48
as q u arter, previous in s titu tio n types, highest level o f math obtained,
and curriculum entered, could not be used as a single v a riab le in the
present coding format without seriously damaging the regression
an alysis.
Thus, such variab les were transformed to binary variables
through an ad d itio n al recoding procedure.
For example, quarter of
entry (a single v a ria b le ) was expanded to three variables (Autumn,
Winter and S p rin g ), so th a t students entering Winter Quarter were
0, I , and 0 re sp ectively on these three new variables (see Table 9
on page 6 3 ).
Major Research Questions and S ta tis tic a l Methods
The follow ing six questions were applied independently to the two
subpopulations:
engineering and technology.
The research questions
and respective methods o f solution are:
Research Question I .
What percent of tra n s fe r students are en­
ro lle d in or graduated from the engineering and technology programs
two years a f t e r i n i t i a l entry?
Method:
Percentages fo r the engineering and technology groups
were determined.
Research Question 2 .
How do p e rs is te r and non-persister engineer­
ing and technology students compare on various demographic character­
is tic s ( i . e . , quarter o f e n try , year o f e n try , country o f c itiz e n s h ip ,
sex, number o f in s titu tio n s attended, previous in s titu tio n type,
49
highest level o f mathematics a ttain ed a t p rio r in s t it u t io n ( s ) , number
o f quarters e n ro lle d , graduate with an Associate's Degree, age, and
time between p rio r in s titu tio n and Montana State U n iv e rsity.
Method:
P e rsis te r and non-persister were compared on the above
eleven c h a ra c te ris tic s .
The nominal c h a ra c te ris tic s were reported in
percentages o f the to ta l group w hile continuous variab les were com­
pared by using group means and standard deviations.
Research Question 3 .
How do p e rs is te r and non-persister engineer
ing and technology students compare on various cognitive variables
( i . e . , number of c red its accepted fo r tra n s fe r, cumulative GPA from
p rio r in s t it u t io n ( s ) , MSU cumulative GPA, ACT Q u a n tita tiv e , ACT Ver­
b a l, ACT Composite, and high school rank a t graduation)?
Method:
The means and standard deviations were used to compare
the p e rs is te r with the non-persister in each o f the two groups.
Research Question 4 .
To what extent do various combinations of
demographic c h a ra c te ris tic s and cognitive variables contribute to the
prediction o f persistence o f engineering and technology students?
Method:
Stepwise m u ltip le regression was used to determine the
unique contrib ution of various sets o f independent pred icto r variables
to the prediction of the dependent v a ria b le , persistence versus non­
persistence.
The a b i l i t y o f a set o f variab les to p re d ic t persis2
tence is measured by the R s t a t i s t ic , often known as the c o e ffic ie n t
of determ ination.
Three tables on each regression model report the
50
findings o f the study r e la tiv e to th is question.
Research Question 5 .
To what extent do various combinations of
demographic c h a ra c te ris tic s and cognitive variables contribute to
the prediction o f the Montana State U niversity cumulative grade point
average a t the end o f two years or a t the la s t quarter o f attendance?
Method:
Stepwise m u ltip le regression was used to determine the
unique contrib ution of various sets o f independent p re d ic to r variables
to the prediction of MSUGPA fo r both technology and engineering groups.
Results are reported in n a rra tiv e and ta b u lar form fo r each o f the
models developed.
Research Question 6 .
To what extent do various combinations of
demographic c h a ra c te ris tic s and cognitive variables contribute to the
prediction o f "low GRA non-persistence" ( i . e . , the case o f tran s fe r
students who leave the engineering or technology program with an MSU
cumulative GPA below 2.50)?
Method:
A stepwise m u ltip le regression model developed in
Research Question 4 was applied to a subgroup of the technology and
engineering population in order to determine i f the re s u ltin g Rc would
increase.
51
Analysis o f Data
M u ltip le Regression
Stepwise m u ltip le lin e a r regression analysis was u t iliz e d in
th is study.
Williams (1979) summarizes the four main objectives
o f th is s t a tis t ic a l t o q l:
1.
Developing a formula fo r the p ra c tic al prediction o f the
c r ite r io n v a ria b le from more than one v a ria b le .
2.
Determining i f the predictions are b e tte r than chance.
3.
Estimating the accuracy of the p red ictio n .
4.
Describing the r e la tiv e contrib ution of the independent
variables in the prediction o f the c r ite rio n v a ria b le .
M u ltip le lin e a r regression was used to determine the contribu­
tio n of independent variab les in estab lishing a prediction equation
to p re d ic t the dependent.variables.
Table 7 summarizes the two sets
o f independent variables associated w ith each o f the two models.
These two general types of. models were used to p red ict each of
the three dependent variab les:
(a) persistence/non-persistence,
(b) MSU cumulative grade point average, and (c) persistence/low GPA
non-persistence (where non-persisting students with GPA's of 2.5 or
greater were elim inated from the group).
fo r analysis of the data.
Table 8 ou tlin es the design
Additional "re s tric te d " models were
developed fo r the prediction o f persistence and cumulative GPA by
52
Table 7
Independent Variables Associated with Two I n i t i a l Regression Models
Variable
Number
Basic Model
Complete Model
I
Quarter o f Entry
Quarter o f Entry
2
Country o f C itizenship
Country o f C itizenship
3
Sex.
Sex
4
Age
5
Curriculum Entered
Curriculum Entered
6
Number of In s titu tio n s
Previously Attended
Number o f In s titu tio n s
Previously Attended
7
Previous In s titu tio n Type
Previous In s titu tio n Type
8
Time Between In s titu tio n s
Time Between In s titu tio n s
9
Number o f Credits Accepted
fo r Transfer
Number o f Credits Accept­
ed fo r Transfer
10
Highest Level o f Math
Attained
Highest Level o f Math
Attained
11
MSU Cumulative GPA
MSU Cumulative GPA
12
Cumulative GPA from P rio r
In s t itu tio n (s )
Cumulative GPA from P rio r
In s t it u t i on(s)
13
Associate Degree or None
Associate Degree or None
,
Age
14
ACT Q u an tita tiv e Score
15
ACT Verbal Score
16
ACT Composite Score
17
High School Rank a t
Graduation
53
O
r e s tr ic tin g the independent variables to only those producing an R
change o f 1% or g re a ter.
These eigh t re s tric te d models, having four
to seven independent v a ria b le s , are discussed in d e ta il in Chapter 4.
Four o f these models were selected fo r cross v a lid a tio n in order to
determine the p ra c tic a l sign ificance o f a p p lic a b ility to other popu­
la tio n s .
R estricted models were not developed on persistence/low GPA
non-persistence.
The basic and complete models of the low GPA non-
p e rs is te r were used to provide addition al information to a s s is t the
researcher in deciding which independent v a riab le to include in the
e ig h t re s tric te d models.
Table 8
Summary of Regression Analyses Based on Dependent Variables
Independent V ariable Models and Sub-Populations
Dependent
Variable
Engineering
Students
Technology
Students
P ersistence/Ndn-Persistence
Basic Model*
Complete Model *
Basic Model *
Complete Model*
MSU Cumulative GPA
Basic Model*
Complete Model*
Basic Model *
Complete Model*
Basic Model
Complete Model
Basic Model
Complete Model
P ersistence/Low GPA NonPersistence
-
*R e s tric ted Models Developed; Cross Validated Models Underlined
54
Cross V alid atio n
A m u ltip le regression equation is often developed on one popu­
la tio n so th a t i t can be used la te r to p re d ic t how in divid u als from
other populations w ill perform on a given dependent v a ria b le .
i f the R
Even
is s ig n ific a n t, a researcher cannot have complete confidence
th a t the regression equation w ill work e ffe c tiv e ly on the new popula­
tio n (Huck, Cormier, and Bounds; 1974).
In order to resolve th is
problem and to determine i f a prediction equation w ill hold up under
another population, cross v a lid atio n s were conducted on four re s tric te d
models.
Hosier (1951) summarizes fiv e d is tin c t types o f cross v a lid a tio n .
He indicates th a t the major weakness o f each of the fiv e cross v a lid a ­
tio n techniques is the "waste of h a lf o f the data and, in p a rtic u la r,
determines the g's on only h a lf o f the a v a ila b le cases" (pg. 11).
In
order to resolve th is problem, he proposes double c ro s s -v a lid a tio n .
The follow ing double cro s s-v a lid a tio n procedure was used.
The
engineering and technology populations were each randomly divided in to
two subgroups, A and B.
A p re d ic tiv e equation, using the set of
independent variables developed in the r e s tr ic tiv e model, was developed
on A and then applied to the other subgroup B to obtain the predicted
Y's fo r th a t subgroup.
Those predicted Y's of B were then compared
with the actual Y's of B.
The procedure was repeated, th is time
using an equation developed on B to p re d ic t the Y's o f A.
In both
55
cases the c o rre la tio n between the predicted Y's and the actual Y's
was examined using the Pearson r .
equations are accurate predictio ns.
High correlatio n s in d icate the
S im ila r c o rre la tio n s , whether
both high or low, in d ic a te th a t the equations were not unduly in ­
fluenced by abnorm alities in the population on which they were based
and th a t the model w ill probably be accurate fo r a new population, pro
vided the new population is not ra d ic a lly d iffe r e n t from the one on
which i t was developed.
In Chapter 4, the r 's are compared as well as
p
the R developed on each o f the subgroups.
Missing Data
In working with m u ltip le regression, i t is important th a t any
case which has one or more item o f missing data be elim inated fropi
the study or otherwise c a re fu lly tre a te d .
In th is study such cases
were elim inated from regression analysis through two d iffe r e n t
approaches.
F ir s t , the number of variables required fo r each case
was reduced so th a t a large number o f cases could be considered
(re s u ltin g in basic and complete models).
Secondly, in divid ual cases
which did not have a complete set o f data fo r each o f the two models
were elim inated from the study.
Lim itations of Regression Analysis
Regression analysis is a re a d ily a v a ila b le tool fo r discovering
relationsh ips among variables and fo r predicting resu lts fo r new
APPENDIX A-2
ACT English Score-Equivalents for SAT Verbal Scores
(Pearson r = .733)
ACT:E Scores
•Equivalent
SAT Verbal Scores
ACT:E Scores
Equivalent
SAT Verbal Scores
LO
LO
21
22
23
24
25
430
450
480
510
540
11
12
13
14
15
270 - 280
290 - 300
310
. 320
330 - 340
26
27
28
29
30
570
590
610
630
650
-
31
32
33
670
710
730
-
I
. 9
10
200
210
220 - 230
240
250 - 260
7
8
16
17
18
19
20
350
360
380
390
410
-
370
-
400
420
-
.
(Langston and Watkins, University of Illinois, N = 12,014, 1980)
-
-
-
440
470
500
530
560
580
600
620
640
660
700
720
800
APPENDIX A-3
ACT Composite Score Equivalents for SAT Total Scores
(Pearson r = .883)
ACT:C Scores
Equivalent
SAT Total Scores
3
4
5
450
460
470
6
7
8
9
10
480
500
520
530
560
11
12
13
14 •
15
16
17
18
19
20
. 580
600
630
670
700
720
750
790
810
840
-
-
-
-
ACT:C Scores
21
22
23
24
490
510
550
570
590
620
660
690
710
740
770
800
830
860
870
910
940
980
1020
-
900
930
970
1010
1050
29
on
J
U
1060
. 1100
1150
1200
1260
-
1090
1140
1190
1250
1300
31
32
33
34
1310
1360
1420
1480
-
1350
1410
1470
1540
O C
L . 0
26
27
.
Equivalent
SAT Total Scores
28
(Langston and Watkins, University of Illinois, N = 12,014, 1980)
APPENDIX B
COMPARISON OF TWO METHODS FOR DETERMINING THE
DIVISION POINT SEPARATING PERSISTERS FROM NON-PERSISTERS
176
COMPARISON OF TWO METHODS FOR DETERMINING THE DIVISION
POINT SEPARATING PERSISTERS FROM NON-PERSISTERS
In the te s tin g fo r accuracy o f the p ersisten ce /n o n -p ersis ten ce
regression models, the researcher must determ ine the p o in t a t which
p e rs is te rs w il l be d iff e r e n t ia te d from n o n -p e rs is te rs .
P rio r to the
an a lys is o f d a ta , th is researcher chose the m idpoint o f the range o f
the coding o f the dependent v a ria b le as the s p e c ific q u to ff p o in t.
Since p e rs is te rs were coded as ]_ and n o n -p e rs is te rs were coded as 0,
the value o f .5 was selected as the p o in t to d iv id e the two groups.
Thus, students w ith p red icted values ranging from .5 to 1 .0 were
p redicted as p e r s is te r s .
Those w ith pred icted values less than .5
were p red icted as n o n -p e rs is te rs .
This procedure was follow ed through­
out the double c ro s s -v a lid a tio n an a ly s is in Chapter 4.
Snedecor and Cochran (1968) in d ic a te th a t the researcher may
a d ju s t the boundary p o in t d if f e r e n t ia t in g groups i f the researcher
has a d d itio n a l knowledge o f s p e c ific parameters o f the population
being considered.
In th is study o f p re d ic tin g persisten ce/n o n ­
p ersistence o f tra n s fe r engineering studen ts, the researcher has
knowledge o f the number o f p e rs is te rs and n o n -p e rs is te rs .
If a
research assumption is made th a t the fu tu re r a t io o f p e rs is te rs /n o n p e rs is te rs w il l remain con stant, one may s e le c t another p o in t (more
r e a l i s t i c than the simple .5 ) a t which p e rs is te rs may be d iff e r e n t ia te d
from n o n -p e rs is te rs .
177
To study th is problem, the researcher c a lc u la te d the predicted
Y' values fo r each o f the 316 engineering tra n s fe r studen ts.
values were arranged in ascending o rd e r.
These
Since in a c t u a lit y there
were 166 p e rs is te rs and 150 n o n -p e rs is te rs , a r e a l i s t i c value fo r
d iv id in g these two groups would be a number between the predicted
value student number a t 150 and 151 (when arranged in ascending o rd e r).
That i s , the d iv id in g p o in t was selected so th a t p redicted p e rs is ­
te rs would be in the same proportion to the to ta l as the actual
p e r s is te r s .
In the basic r e s tr ic te d model f o r engineering students
th is value was .3661.
The comparison o f using .3661 and .5000 are
presented on page 177.
The o v e ra ll accuracy o f the basic r e s tr ic te d model f o r engineering
students using .5 as the c u to ff fo r d if f e r e n t ia t in g p e rs is te rs from
n o n -p e rs isters was 58.3%.
The adjustm ent o f the c u to ff p o in t to
.3661, based upon knowledge o f the actu al p ersisten ce /n o n -p ersis ten ce
r a t io , increased the o v e ra ll accuracy o f the model to 65.8%.
In
a d d itio n , one w il l note th a t the amount o f e rro r is balanced a t 17.1%
f o r the two in c o rre c t c e lls when .3661 is used as the d iv id in g p o in t.
In order to examine th is problem f u r th e r , the researcher tested
the accuracy o f the basic r e s tr ic te d model fo r engineering students
on the p e rs is te n c e /I ow GPA non-persistence group t o ta lin g 268 stu ­
dents.
To review , th is group consists o f a l l engineering p e rs is te rs
and a l l n o n -p e rs is te rs w ith MSU cum ulative grade p o in t averages w ith
178
COMPARISON OF BASIC RESTRICTED ENGINEERING MODEL
USING THE VALUES QF .5 0 .AND .3661 AS DIVIDING POINT
DIFFERENTIATING PERSISTERS FROM NON-PERSISTERS
METHOD A - I :
P e rs is te r
CO
3
«
4->
OO
.50 AS THE CUTOFF
N = 316
Actual Status
J
N o n-P ersister
P e rs is te r
C o rrect
52
(16.5%)
E rror
18
(5.7%)
N o n -P ersiste r
E rro r
114
(36.1%)
Correct
132
(41.8%)
Total
70
-TD
CU
+J
U
1S
<D
StQ-
TOTAL
I
166
I
246
Total
,150
I Correct
184
(58.3%)
METHOD B - I :
P e rs is te r
(O
m
4->
OO
CU
5Q-
Actual Status
I
N o n-P ersister
166
(35.4%)
E rro r
54
(17.1%)
Correct
96
(30.4%)
150
112
•
N o n -P ersister
I Total
I
TOTAL
I Total
E rror
54
(17.1%)
C orrect
P e rs is te r
"O
CU
4- )
U
xj
.3661 AS THE CUTOFF
N = 316
166
150
Correct
208
I' (65.8%)
|
179
less than 2 .5 0 .
The researcher compared the accuracy o f th is model
using both .50 and .3308 (th e new p red icted value based on the 102nd
and 103rd p o in t).
The s p e c ific fin d in g s are found on page 179.
The reader w i l l note th a t the .50 c u to ff p o in t y ie ld e d an o v e ra ll
accuracy o f 54.8% o f c o r r e c tly p re d ic tin g 19.4% o f the p e rs is te rs and
35.4% o f the n o n -p p rs is te rs .
A djusting the c u to ff p o in t to .3308
increases the o v e ra ll e ffe c tiv e n e s s to 67.9%, c o r r e c tly p re d ic tin g
45.9% o f the p e rs is te rs and 22.0% o f the n o n -p e rs is te rs .
I f the College or the U n iv e rs ity should choose to implement any
regression model p re d ic tin g p e rs is te n c e /n o n -p e rs is te n c e , the s e le c tio n
o f the c u to ff value d if f e r e n t ia t in g the two groups should be given
c a re fu l c o n s id e ra tio n .
180
COMPARISON OF THE BASIC RESTRICTED MODEL FOR ENGINEERING
APPLIED TO THE PERSISTENCE/LOW GPA NON-PERSISTENCE USING THE
VALUES OF .50 AND ,3308 AS THE DIVIDING POINT DIFFERENTIATING
PERSISTERS FROM NON-PERSISTERS
METHOD A -2:
.50 AS THE CUTOFF
N = 268
Actua
P e rs is te r
W'
3
"£ Peirsister
OO
"O
OJ
4->
O
‘■5 N o n -P ersiste r
(LI
5Q-
N o n-P ersister
Error
7
( 2.6%)
59
E rro r
114
(42.5%)
Correct
95
(35.4%)
209
102
METHOD B -2:
Actual Status
I P e rs is te r
|
N o n-P ersister
TOTAL
C o rrect
123
(45.9%)
E rro r
43
(.16.0%)
166
O verall
147
(54.8%)
.3308 AS THE CUTOFF
N = 268
I
V)
3
re P e rs is te r
+j
CO
"O
<D
0
'■o N o n -P ersiste r
01
SQ-
Total
C o rrect
52
(19.4%)
166
TOTAL
Status
.
..................
Total
E rro r
43
(16.0%)
166
Correct
59
(22.0%)
102
- 102
O verall
182
I (67.9%)
I
O^
=2-
58
The model in t h i s st udy avoided t h e o r d e r i n g problem by c o n s i d e r i n g
onl y a simple dichotomous, r a t h e r than polychotomous, c r i t e r i o n
variable.
F i n a l l y , t h e d i s c u s s i o n o f m u l t i p l e r e g r e s s i o n has r e p e a t e d l y
su ggested t h a t s t r o n g m u l t i c o l l i n e a r i t y among t h e independent p re ­
d i c t o r v a r i a b l e s d e s t r o y s t h e r e l i a b i l i t y o f c o n c lu s io n s about the
r e l a t i v e importance o f t h e s e v a r i a b l e s .
The o v e r a l l r e g r e s s i o n
2
e q u a ti o n may be s a t i s f a c t o r y , but t h e change in R may u n d e re st im at e
t h e va lu e o f a v a r i a b l e in improving t h e r e g r e s s i o n p r e d i c t i o n .
The r e s e a r c h e r examined each m a tr ix t a b l e f o r extreme m u l t i c o l l i n e a r ­
ity.
Severe m u l t i c o l l i n e a r i t y was n o t e v i d e n t in t h i s s tu dy.
P r e c a u t i o n s Taken f o r Accuracy
Since a l l d a t a c o l l e c t e d f o r t h i s s tu dy were c o l l e c t e d by hand
from o r i g i n a l sou rce documents, t h e r e s e a r c h e r was a b le t o s pot
d a ta i r r e g u l a r i t i e s o r e xtra neo us in fo r m a t io n which might oth e rw is e
have been overlooked i f only computerized d a t a were examined.
All
hand t a b u l a t i o n s were c a r e f u l l y made and t h e Doctoral Committee Chair
man s p o t checked t h e s e d uri ng th e d a t a c o l l e c t i o n p ro c e du re.
The
A s s i s t a n t Dean o f th e College of Engineering was a l s o c o n s u l t e d in
o r d e r to e l i m i n a t e pro ce dur al e r r o r s d uri ng d a ta c o l l e c t i o n .
The
d a ta were c o n s t a n t l y checked and rechecked f o r programming e r r o r s
thr ou gh out t h e s tu d y .
Once t h e i n p u t d a t a were c o r r e c t and e n te r e d
59
i n t o t h e computer f o r a n a l y s i s , i t was assumed t h a t t h e programs and
computer o p e r a t i o n s were a c c u r a t e .
This assumption a ppea rs v a l i d
because s t a n d a r d , n a t i o n a l l y known, programs (SPSS) (NIE and o t h e r s ,
1975) were used.
Throughout t h e s t a t i s t i c a l a n a l y s i s , members of the
committee and computer s p e c i a l i s t s were u t i l i z e d to t h e f u l l e s t ex­
tent.
A log was kep t o u t l i n i n g any i r r e g u l a r i t i e s and s p e c i f i c
d e c i s i o n s made.
These a c t i o n s were reviewed on a r e g u l a r b a s i s by
t h e committee chairman.
E t h i c a l Standards
Due t o t h e n a t u r e o f t h i s s tu d y , t h e r e s e a r c h e r adhered to
s t r i c t ethical standards.
Throughout t h i s i n v e s t i g a t i o n anonymity
o f s t u d e n t s u b j e c t s and c o n f i d e n t i a l i t y o f t h e i r re c o rd s was pre-
I
s er ve d.
All r e p o r t i n g was handled a cc ord in g to th e g en eral c a t e g o r i e s
o u t l i n e d in th e d e s c r i p t i o n o f c r i t e r i o n and independent v a r i a b l e s .
No i n d i v i d u a l o r i n s t i t u t i o n was rec ogn iz e d by name.
With r e s p e c t
to t h e "Buckley Amendment", (a f e d e r a l law r e s t r i c t i n g d i s c l o s u r e o f
s t u d e n t r e c o r d s ) , s i n c e th e r e s e a r c h e r was an employee o f Montana
S t a t e U n i v e r s i t y and a c te d on b e h a l f o f th e College o f Eng ineering,
t h i s r e s e a r c h was w i t h i n th e p r o v i s i o n s o f the law.
Summary
The proce dures and s t a t i s t i c a l - a n a l y s i s o u t l i n e d in t h i s c h a p t e r
60
have enabled t h e r e s e a r c h e r to answer s i x major q u e s t i o n s a s s o c i a t e d
with t h e p e r s i s t e n c e o r n o n - p e r s i s t e n c e o f t r a n s f e r s t u d e n t s in
e n g in e e ri n g c u r r i c u l a a t Montana S t a t e U n i v e r s i t y .
The primary a n a ly ­
s i s o f t h e d a t a c e n t e r e d around t h e u t i l i z a t i o n o f m u l t i p l e r e g r e s s i o n
t o fo r m u la t e a p r e d i c t i o n eq u at i o n f o r p e r s i s t e n c e and MSU cumulative
GPA.
Ad diti on a l a n a l y s i s confirmed t h e r e s u l t s o f t h e r e g r e s s i o n
a n a l y s i s and i n d i c a t e d t h e r e l a t i v e importance o f p r e d i c t o r v a r i a b l e s .
The procedures and o p e r a t i o n s o u t l i n e d in t h i s c h a p t e r pro vid e an
a c c u r a t e a n a l y s i s o f a t o p i c o f both fundamental r e s e a r c h s i g n i f i c a n c e
and o f immediate p r a c t i c a l b e n e f i t t o t h e u n i v e r s i t y .
CHAPTER 4
RESULTS AND FINDINGS OF THE STUDY
This stud y examined f a c t o r s which p r e d i c t p e r s i s t e n c e and cumula­
t i v e grade p o i n t av erage o f t r a n s f e r s t u d e n t s e n t e r i n g e n g in e e ri n g and
techn ol ogy c u r r i c u l a a t Montana S t a t e U n i v e r s i t y .
This c h a p t e r p re ­
s e n t s t h e r e s u l t s and f i n d i n g s o f t h i s stu dy and i s o r ga niz ed in the
fo ll o w i n g manner.
F i r s t , r e s e a r c h q u e s t i o n s one, two and t h r e e a re
d i s c u s s e d with d a t a comparing p e r s i s t e r and n o n - p e r s i s t e r s t u d e n t s
from t h e two s u b p o p u l a t i o n s , e n g in e e r in g and tech nol ogy .
Second,
r e s e a r c h q u e s t i o n s f o u r to s i x a r e d i s c u s s e d r e l a t i v e to t h e e n g i n e e r ­
ing su b p o p u la t io n .
T h i r d , r e s e a r c h q u e s t i o n s f o u r t o s i x a r e answered
on t h e techn ol ogy s t u d e n t s .
Fo urt h, r e s u l t s o f th e c r o s s v a l i d a t i o n
o f f o u r p r e d i c t i v e models a r e d i s c u s s e d .
F in a lly , the chapter closes
with a comparison of i n t e r n a t i o n a l and USA/Canadian s t u d e n t s .
Before d i s c u s s i n g t h e s e q u e s t i o n s , t h e r e s e a r c h p o p u la ti o n w il l
be reviewed.
T r a n s f e r s t u d e n t s who i n i t i a l l y e n t e r e d t h e College of
Engineering during th e Autumn, Winter, and Spring Q u a rt e rs o f academic
y e a r s 1977 and 1978 and Autumn Q ua rte r o f 1979 were in c lu d e d in t h i s
s tu d y .
This t o t a l e d 394 s t u d e n t s .
The pop u la ti o n was d i v i d e d in t o
two s e p a r a t e su b p o p u la t io n s f o r a n a l y s i s .
su bpo p u la t io n with 316 s t u d e n t s .
Engineering was th e l a r g e s t
The te chnology su bp op u la t io n t o t a l e d
78 s t u d e n t s .
The d i s c u s s i o n o f r e s e a r c h q u e s t i o n s f o u r t o s i x w i l l f r e q u e n t l y
in v o lv e m u l t i p l e r e g r e s s i o n models.
In o r d e r to p r e s e n t t h e f i n d i n g s
62
as c o n c i s e l y as p o s s i b l e , a b b r e v i a t i o n s f o r th e dependent and indepen­
den t v a r i a b l e s a r e used.
Table 9 p r e s e n t s th e a b b r e v i a t i o n s a s s o c i a t e d
with each v a r i a b l e used in th e s t a t i s t i c a l a n a l y s i s .
Engineering and Technology Stu de nts - Research Questions 1-3
Research Question One
What pe rc en ta ge o f t r a n s f e r s t u d e n t s p e r s i s t in e n g in e e r in g and
te chnology programs two y e a r s a f t e r i n i t i a l entry?-
Engineering s t u ­
de nt s in comparison with technology s t u d e n t s tended to have a hig he r
pe rc en ta ge o f g r a d u a ti o n and a h i g h e r p e rc en ta g e o f e n ro ll m e n t a t the
end o f t h e two y e a r p e r i o d .
During th e two y e a r p e r i o d , seventeen
e n g in e e r in g s t u d e n t s (5.4%) graduated w hil e 2.6% o f t h e technology
s t u d e n t s o b ta in e d a B a c h e l o r ' s Degree.
The e n g in e e ri n g n o n - p e r s i s t e n c e
r a t e was 47.5% while th e technology s ub popul at io n had a n o n - p e r s i s t e n c e
r a t e o f 53.8%.
Ad di tio na l d a t a i s r e p o r t e d in Tables 10 and 11.
A review o f th e l a t t e r t a b l e i n d i c a t e s t h a t the o v e r a l l p e r s i s ­
te n c e r a t e f o r t h e t o t a l p o p u la ti o n o f t r a n s f e r s t u d e n t s in th e College
o f Engineering was 51.3%.
The p e r s i s t e n c e r a t e f o r e n g in e e ri n g s t u ­
de nts was 52.5% and was 46.2% f o r technology s t u d e n t s .
The p e r s i s ­
t e n c e / n o n - p e r s i s t e n c e r a t e s v a r i e d among t h e d i f f e r e n t c u r r i c u l a .
63
Table 9
A b b re v ia ti o n s Used f o r V a ri a ble s
A b b re vi a tio n
V a ri a b le
PNP
*Persistence/N on-Persistence
MSUGPA
*MSU Cumulative Grade P o in t Average
Ql
Q2
Q3
Q ua rt e r o f I n i t i a l Entry
Winter
Spring
Autumn
CSHP
Citizenship
SEX
Sex
AGE
Age
CENT!
CENT2
CENTS
CENT4
CENTS
CENT6
CENT?
CENTS
CENT9
CENTlO
NINST
ITYPl
ITYP2
ITYP3
Curriculum e n t e r e d
A g r i c u l t u r a l Engineering
Chemical Engineering
C iv il Engineering
E l e c t r i c a l Engineering
E l e c t r i c a l and E l e c t r o n i c s Engineer­
ing Technology
Engineering Science
I n d u s t r i a l and Management Engineer­
ing
Mechanical Engineering
Mechanical E n g i n e e r ing Technology
C o n s tr u c t io n Engineering Technology
Number o f I n s t i t u t i o n s P r e v i o u s l y Atte nded
Previous I n s t i t u t i o n Type
Community/J u n i o r College
Four Year College
University
64
Table 9 (Continued)
A b br e via tio n
V a ria ble
TBET
Time Between I n s t i t u t i o n s
NCRED
Number o f C r e d i t s Accepted For T r a n s f e r
MATH!
MATH2
MATH3
MATH4
Highest Level o f Math A tt a in e d
None
Algebra
Trigonometry
Cal culus
CGPA
Cumulative GPA f o r P r i o r I n s t i t u t i o n ( s )
DEG
Degree
Yl
Y2
Y3
Year o f Entry
1977
1978
1979
ACTQ
ACTV
ACTC
ACT A p t i t u d e Tes t
Quantitative
Verbal
Composite
HSRNK
High School Rank a t Graduation
*Dependent o r C r i t e r i o n V a ria bles
65
Table 10
G radua ti on, Dropout, and Enrollment o f Engineering and
Technology Po pula tio ns
Engineering
Technology
N
%
r
Graduated Within 2 Years
17
5 .4
. 2
2.6
Dropped out During th e .
Two Years
150
47.5
42
53.8
En r o ll e d a t End o f Two
Year Period
149
47.1
34
. 43.6
Total
316
100.0
78
100.0
.
%
Table 11
P e r s i s t e n c e and N o n -P e rs is te n c e Rates f o r Engineering and Technology St uden ts
(N = 394)
Persisters
Non-Persisters
N
%
N
Total College o f Engi nee r­
ing
202 '
51.3
192
48.7
394
100
Engineering Stu de nts
166
52.5
150
47.5
316
TOO
Technology St u d e n ts
36
46.2
42
53.8
78
100
T r a n s f e r St ude nt Subp o p u la ti o n s
%
Total
N
%
Table 11 (Continued)
Persisters
Demographic C h a r a c t e r i s t i c s
Non-- P e r s i s t e r s
N.
Total
N
%
5
31.3
11
68.7
16
100
Chemical Engineering
22
52.4
20
47.6
42
100
C i v i l Engineering
52
57.8
38
42.2
90
100
E l e c t r i c a l Engineering
32
43.8
41
56.2
73
100
Engi neering Science
18
62.1
11
37.9
29
100
8
72.7
3
27.3
11
100
29
52.7
26
47.3
55'
100
166
52.5
150
47.5
316
100
C o n s t r u c t i o n Engineering
17
47.2
19
52.3
36
100
E l e c t r i c a l and E l e c t r o n i c s
Engin eerin g
10
50.0
10
50.0
20
100
9
40.9
13
. 59.1
22
100
36
46.2
42
53.8
78
100
%
N
%
Engi neering C u r r i c u l a Entered
A g r i c u l t u r a l Engineering
I n d u s t r i a l & Management
Engineering
Mechanical Engineering
TOTAL
Technology C u r r i c u l a Entered
Mechanical Engineering
TOTAL
68
Research Question Two
How do p e r s i s t e r and n o n - p e r s i s t e r t r a n s f e r s t u d e n t s in e n g i ­
ne er in g and te chnology programs compare on v a ri o u s demographic
characteristics?
Non -continuous demographic c h a r a c t e r i s t i c s a r e p r e ­
sen te d in Table 12.
Continuous demographic measures a r e pre s e n te d in
Table 13 f o r e n g i n e e r i n g s t u d e n t s and Table 14 f o r te chn ol ogy s t u d e n t s
A d i s c u s s i o n o f t h e most im po rt an t f i n d i n g s fo l l o w s .
There appea rs t o be l i t t l e or no d i f f e r e n c e between p e r s i s t e r and
n o n - p e r s i s t e r te chnology and e n g in e e r in g s t u d e n t s r e l a t i v e to the
fo ll o w i n g v a r i a b l e s :
number o f i n s t i t u t i o n s , age, and t h e holding o f
an a s s o c i a t e de gr e e.
However, p e r s i s t e r and n o n - p e r s i s t e r s do vary
on t h e fo ll o w i n g demographic v a r i a b l e s :
Q ua rt e r o f E n t r y :
For e n g in e e r in g s t u d e n t s th e time o f e n t r y to
Montana S t a t e f o r p e r s i s t e r s and n o n - p e r s i s t e r s i s p r a c t i c a l l y the
same.
This i s h o t t r u e f o r te chnology s t u d e n t s .
A g r e a t e r number
o f n o n - p e r s i s t e r s e n t e r during t h e Winter and Spring q u a r t e r s than
p e r s i s t i n g techn olo gy s t u d e n t s .
Year o f E n t r y :
For e n g in e e r in g s t u d e n t s , th e y e a r o f e n t r y was
s i m i l a r f o r th e p e r s i s t e r s and t h e n o n - p e r s i s t e r s .
A la rg e r percent­
age o f n o n - p e r s i s t e r techn olo gy s t u d e n t s e n te r e d d uri ng 1977 than did
p e r s i s t e r s and fewer n o n - p e r s i s t e r s e n t e r e d during Autumn 1979.
Country o f C i t i z e n s h i p :
The n o n - p e r s i s t e n c e r a t e f o r i n t e r -
Table 12
Demographic C h a r a c t e r i s t i c s : Numbers and Percen tage s Comparing
P e r s i s t e r s and N o n - P e r s i s t e r s Engineering and Technology Programs
Demographic
Characteristic
Engin eerin g St u d e n ts N-316
Non- P e r s i s t e r s
Persisters
N
%
■
N
%
Technology St ude nt s N=78
Persisters
Non-Persisters
N
%
N
%
Q u a r te r o f Entry
Winter
23
13.9
17
11.3
0
0
3
7.1
Spring
3
1.8
8
5.3
3
8 .3
6
14.3
Autumn
140
84.3
125
83.4
33
91.7
33
78.6
166
100.0
150
100.0
36
100.0
42
100.0
1977 (A9W5S)
40
24.1
37
24.7
7
19.4
17
40.5
1978 (A9W9S)
56
33.7
49
32.7
13
36.2
13
31.0
1979 (A9W9S)
70
42.2
64
42.6
16
44.4
12
28.5
166
100.0
150
100.0
36
100.0
42
100.0
154
92.8
135
90.0
36
100.0
.40
95.2
12
7.2
15
10.0
0
0
2
166
100.0
150
100.0
36
100.0
42
Total
Year o f Entry
Total
Country o f C i t i z e n s h i p
USA/Canada
Other
Total
4. 8 .
100.0
S
Table 12 (Continued)
Demographic
C haracteristic
Engin eerin g S tu d e n ts N-316
Non- P e r s i s t e r s
Persisters
N
N
%
%
Technology Stude nts N=78
P ersisters
Non-Persisters
N
N
%
%
Sex
Male
Female
Total
157
94.6
132
88.0
36
100.0
9
5 .4
18
12.0
0
0
0
0
166
100.0
150
100.0
36
100.0
42
100.0
42 .
100.0
Number o f I n s t i t u t i o n s Pr e v io u s ly
Attended
O
One
121
72.9
115
76.7
28
77.8
33
78.6
Two
35
21.1
23
15.3
7
19.4
7
16.7
Three
8
4.8
9
6 .0
I
2 .8
2
4.7
Four
2
1.2
2
1.3
0
0
0
0
Five
0
0
I
.7
0
0
0
0
166
100.0
150
100.0
36
100.0
42
100.0
35
21.1
40
26.7
11
30.6
14
33.3
Total
Pr ev io us I n s t i t u - .
t i o n Type
Community/Jun­
i o r College
Table 12 (Continued)
Demographic
C haracteristic
Engineering S tu d e n ts N=SlC
Persisters
Non-Persisters
Technology St u d e n ts N=78
Persisters
Non-Persisters
N
%
N
%
N
%
■N
%
Four Year
College
73
44.0
60
40 .0
14
38.9
19
45.3
University
58
34.9
50
33.3
11
30.5
9
21.4
166
100.0
150
100.0
36
100.0
42
100.0
30
18.1
41
27.3
9
25.0
22
52.4
5
3 .0
17
11.4
7
19.4
10
23.8
20
12.0
21
14.0
6
16.7
I
2.4
Calcu lus
111
66.9
71
47.3
14
38.9
9
21.4
Total
166
100.0
150
100.0
36
100.0
42
100.0
One
0
0
31
20.6
0
0
14
33.3
Two
0
0
30
20.0
Q
0
15
35.7
Three
3
1.8
34
22.7
I
2.8
9
21.4
Four
6
3 .6
18
12.0
0
0
3
7.2
Total
H ig he st Level o f
Math A t t a i n e d .
None
Algebra
Trigonometry
Number o f Q u a rt e rs
E n r o l l e d a t MSU .
Table 12 (Continued)
Engineering St u d e n ts N=SlS
Persisters
Non-Persisters
Technology Stu de nts N=78
Persisters
Non-Persisters
N
%
N
N
Five
11
6.6
16
10.7
2
5.6
0
0
Six
119
71.7
20
13.3
25
69.4
I
2 .4
27
16.3
I
.7
8
22.2
0
0
166
100.0
150
100.0
36
100.0
42
100.0
Demographic
C haracteristic
Seven
Total
%
%
N.
■
%
Graduate o r NonGraduate
Associate
Degree
None Earned
Total
IX )
9.0
17
11.3
4
11.1
4
9.5
151
91.0
133
88.7
32
89.9
38
90.5
166
100.0
150
100.0
36
100.0
42
100.0
15
.
Table 13
Demographic and Co gniti ve Measures f o r P e r s i s t e r s and N o n - P e r s i s t e r s
Continuous Data - Engineering
(N-316)
Persisters
C haracter!s t i c/V ariable
N*
Mean
Age ( y e a r s )
166
21.19
Time Between In­
stitutions
166
9.78
Standard
D eviation
Non-- P e r s i s t e r s
Standard
Deviation
N*
Mean
2.63
150
21.30
2.83
17.34
150
11.00
16.31
Demographic C h a r a c t e r i s t i c s
C og niti ve V a r ia b le s
CO
Number o f C r e d i t s
Accepted f o r T r a n s f e r
166
71.16
46.18
150
56.80
39.51
Cumulative GPA from
Prior In s titu tio n (s)
166
2.94
.58
150
2.63
.58
MSU Cumulative GPA
166
2.77
.53
150
2.09
.83
ACT Q u a n t i t a t i v e
81
26.16
4.33
64
23.64
6.00
ACT Verbal
81
20.93
4.39
. 64
18.78
5.00
ACT Composite
81
24.56
3,47
64
22.25
4.55
High School Rank a t
Graduation ( P e r c e n t i l e )
94
72.34
20.53
85
62.19
24.74
*N here i n d i c a t e s th e number o f s t u d e n t s f o r which background c h a r a c t e r i s t i c s and v a r i a b l e
d a t a were a v a i l a b l e .
Table 14
Demographic and C ogniti ve Measures f o r P e r s i s t e r and N o n - P e r s i s t e r
Continuous Data - Technology
(N = 78)
Persisters
C haracteristic/V ariable
N*
Mean
Standard
D ev iation
Non- P e r s i s t e r s
N*
Mean
Standard
Deviation
Demographic C h a r a c t e r i s t i c s
Age (y e a r s )
36
21.42
2.85
42
21.10
2.90
Time Between In­
stitutions
36
11.00
15.20
42
20.71
36.04
Number o f C r e d i t s
Accepted f o r T r a n s f e r
36
64.81
30.23
42
40.62
39.10
Cumulative GPA from
Prior In s titu tio n
36
2.77
.53
42
2.27
1.03
MSU Cumulative GPA
36
2.81
.54
42 .
1.64
.86
ACT Q u a n t i t a t i v e
14
23.0
5.35
23
17.87
6.90
ACT Verbal
14
18.43
3.76
23
17.13
5.50
ACT Composite
14
21.86
4.33
23
18.91
5.62
High School Rank a t
Graduation ( P e r c e n t i l e )
16
56.69
. 22.56
28
51.75
25:31
C o gni tiv e V a r i a b l e s
*N he re i n d i c a t e s t h e number o f s t u d e n t s f o r which background c h a r a c t e r i s t i c s and v a r i a b l e
d a t a were a v a i l a b l e .
75
n a t i o n a l s t u d e n t s appea rs to be s l i g h t l y h ig h e r than f o r USA/Canada .
s t u d e n t s f o r both t h e e n g in e e r in g and te chnology s u b p o p u l a t i o n s .
In e n g i n e e r i n g 7.2% o f t h e p e r s i s t e r s were i n t e r n a t i o n a l s t u d e n t s
w hil e 10.0% o f t h e n o n - p e r s i s t e r s were i n t e r n a t i o n a l s t u d e n t s .
Of
t h e two i n t e r n a t i o n a l s t u d e n t s in t h e techn ol ogy c u r r i c u l a , both
were n o n - p e r s i s t e r s .
Sex:
persisters.
Eighteen o f t h e 27 female e n g in e e r in g s t u d e n t s were nonThis i s c o n s i d e r a b l y h ig h e r than t h e n o n - p e r s i s t e n c e
r a t e f o r males.
Since t h e r e were no females e n r o l l e d in tech no lo gy,
a comparison on t h i s v a r i a b l e cannot be made.
Previ ous I n s t i t u t i o n Type:
Whether e n g in e e ri n g s t u d e n t s t r a n s ­
f e r from a community c o l l e g e , a f o u r y e a r c o l l e g e o r a u n i v e r s i t y
makes l i t t l e d i f f e r e n c e in t h e i r p e r s i s t e n c e a t MSU.
With r e s p e c t to
techn ol ogy s t u d e n t s , fewer n o n - p e r s i s t e r s had a t t e n d e d a u n i v e r s i t y
*
^
than p e r s i s t e r s .
Hig he st Level o f Math A t t a i n e d :
N o n - p e r s i s t e r s in both e n g i ­
ne er in g and te chnology c u r r i c u l a had ta ken l e s s mathematics than
persisters.
ters.
N o n - p e r s i s t i n g e n g in e e r s took l e s s c a l c u l u s than p e r s i s ­
A l a r g e p e r c e n t a g e o f t h e techn ol ogy s t u d e n t s had taken no
math du rin g t h e i r c o l l e g e s t u d i e s p r i o r t o a t t e n d i n g MSU.
A la r g e
p e rc en ta g e o f t h e s e s t u d e n t s were n o n - p e r s i s t e r s .
Number o f Q u a rt e rs Enr oll ed a t HSU:
Due to t h e d e f i n i t i o n of
p e r s i s t e n c e , one would e xp ec t t h a t t h e n o n - p e r s i s t e r s would le ave
76
MSU e a r l i e r than p e r s i s t e r s who s t a y on t o g r a d u a t e .
Such i s the case
in t h i s stu dy as found in Table 12, pages 71 and 72.
The p a t t e r n s o f
dro po ut s app ear to be d i f f e r e n t between e n g i n e e r i n g and technology
students.
Engineering s t u d e n t s appea r t o have a more c o n s i s t e n t r a t e
o f d ro p o u t, with only a s l i g h t l y g r e a t e r number le a v i n g MSU during
t h e i r f i r s t three quarters.
Technology s t u d e n t s , on t h e o t h e r hand,
appea r t o dropout r a p i d l y duri ng th e f i r s t t h r e e q u a r t e r s .
Time Between I n s t i t u t i o n s .
N o n - p e r s i s t i n g e n g in e e r in g s t u d e n t s
showed a s l i g h t l y l o n g e r pe ri o d between i n s t i t u t i o n s than p e r s i s t i n g
engineering students.
Technology n o n - p e r s i s t e r s had a lo n g e r period
(X = 20.7 months) between i n s t i t u t i o n s than p e r s i s t i n g te chnology s t u ­
de n ts (X = 11.0 months).
In most c a s e s , th e d i f f e r e n c e s between p e r s i s t e r s and nonp e r s i s t e r s on v a r io u s demographic v a r i a b l e s were m a r g i n a l .
The
s i g n i f i c a n c e o f t h e s e d i f f e r e n c e s was examined in t h e m u l t i p l e r e g r e s ­
si o n models and i s d i s c u s s e d a t a l a t e r p o i n t in t h i s c h a p t e r .
Research Question Three
How do p e r s i s t e r and n o n - p e r s i s t e r t r a n s f e r s t u d e n t s in e n g i ­
ne er in g and te chnology programs compare on v a r io u s c o g n i t i v e v a r i ­
ables?
In both e n g i n e e r i n g and te ch nolo gy s u b p o p u l a t i o n s , th e non-
p e r s i s t e r s in comparison with p e r s i s t e r s had lower mean va lu es on a l l
seven c o g n i t i v e v a r i a b l e s examined in t h i s s tu dy.
Tables 13 and 14
77
found on pages 73 and 74, r e s p e c t i v e l y , summarize t h e d a ta f o r t h e s e
variables.
A s h o r t summary fo ll o w s :
Number o f C r e d i t s from P r i o r I n s t l t u t i o n ( s ) .
P e r s i s t e r s had
ap pr ox im a te ly one more q u a r t e r o f p r i o r c o l l e g e p r e p a r a t i o n than nonpersisters.
d e n ts
This was t r u e f o r both e n g in e e r in g and te chnology s t u ­
Engineering s t u d e n t s tended t o have more p r i o r c o l l e g e c r e d i t s .
than techn olo gy s t u d e n t s .
Cumulative Grade P o in t Average from P r i o r I n s t i t u t i o n ( s ) .
The
mean cumu la tiv e GPA f o r p e r s i s t i n g e n g in e e r in g s t u d e n t s was 2.94
compared with a 2.63 GPA f o r n o n - p e r s i s t e r s in e n g i n e e r i n g .
The mean
cu mulative GPA f o r p e r s i s t i n g te chnology s t u d e n t s was 2.77 compared
with 2.27 f o r n o n - p e r s i s t i n g techn ol ogy s t u d e n t s .
MSU Cumulative Grade P o in t Average.
The mean GPA c a r r i e d a f t e r
t r a n s f e r r i n g to MSU was c o n s i d e r a b l y lower f o r n o n - p e r s i s t e r s than f o r
persisters.
The d i f f e r e n c e in mean GPA f o r p e r s i s t e r s and n o n - p e r s i s ­
t e r s was .68 (2.77 vs 2.09) f o r e n g in e e r in g s t u d e n t s and was 1.17 (2.81
vs 1.64) f o r te chn ol ogy s t u d e n t s .
ACT Q u a n t i t a t i v e , V erbal , and Composite Te s t S c o r e s .
Persisters
in both th e e n g in e e r in g and technology su bp opula ti ons had mean scor es
which were h ig h e r than n o n - p e r s i s t e r s .
For th e most p a r t , t h e spread
in s c o r e s was a pprox im at el y 3 raw s co r e p o i n t s .
P e r s i s t i n g technology
s t u d e n t s had s c o r e s which were comparable t o n o n - p e r s i s t i n g e n g i ­
n e e r in g s t u d e n t s .
78
High School Rank a t G ra d u a t io n .
P e r s i s t i n g e n g in e e r in g and t e c h ­
nology s t u d e n t s had h i g h e r c l a s s ranks a t g r a d u a ti o n than n o n - p e r s i s t e r s in t h e same s u b p o p u la t io n .
Both eng ine ering , p e r s l s t e r s and non-
p e r s i s t e r s had h i g h e r g ra d u a t i o n c l a s s ranks than p e r s i s t i n g technology
students.
Research q u e s t i o n s f o u r through s i x w i l l now be d i s c u s s e d , e n g i ­
ne er in g s t u d e n t s f i r s t and te chnology s t u d e n t s second.
Engineering St u d e n ts - Research Questions 4^6
Research Question Four
To what e x t e n t do v a ri o u s combinations o f demographic c h a r a c t e r ­
i s t i c s and c o g n i t i v e v a r i a b l e s c o n t r i b u t e to th e p r e d i c t i o n of
p e r s i s t e n c e o f e n g in e e r in g t r a n s f e r s t u d e n t s ?
This q u e s t i o n was
answered by u t i l i z i n g st ep w is e m u l t i p l e r e g r e s s i o n t o deter mine what
independent v a r i a b l e s c o n t r i b u t e t o e x p l a i n i n g t h e v a r i a n c e in the
dependent v a r i a b l e , p e r s i s t e n c e / n o n - p e r s i s t e n c e .
The p r e d i c t i v e
eq u at i o n f o r t h e e n g i n e e r i n g b a s i c r e s t r i c t e d model i s ex pre sse d by
t h e f o ll o w i n g (see page 51 f o r d e f i n i t i o n o f " r e s t r i c t e d " ) :
PNPe n g ( b a s i c ) = • 24450 x CGPA + ' 13031 x WTH4 - .24480 x SEX
- .22021 x MATH2 + .00117 x NCRED - .27207
Before reviewing t h e s i g n i f i c a n c e o f t h i s f i r s t p r e d i c t i v e model,
i t would be h e l p f u l t o review t h e gener al meaning o f t h i s e q u a ti o n .
79
PNP r e f e r s t o p e r s i s t e n c e / n o n - p e r s i s t e n c e , t h e dependent v a r i a b l e .
The maximum range o f t h i s valu e was - . 4 9 and 1.49, c orr esp ondi ng to
th e dependent v a r i a b l e being coded 0 f o r n o n - p e r s i s t e n c e and f o r I f o r
persistence.
The numerical valu es a s s o c i a t e d with an independent
v a r i a b l e a r e u n s ta n d a r d iz e d r e g r e s s i o n c o e f f i c i e n t s .
Any n e g a ti v e
u n s ta n d a r d iz e d r e g r e s s i o n c o e f f i c i e n t w i l l s u b t r a c t from th e t o t a l
equation.
The l a s t numerical value i s t h e c o n s t a n t a s s o c i a t e d with
the equation.
This b a s i c r e s t r i c t e d model with 5 independent v a r i a b l e s was
developed on 316 e n g i n e e r i n g s t u d e n t s .
The c a l c u l a t e d F val ue o f
11.01 in th e o v e r a l l t e s t , shown in Table 15, was g r e a t e r than the
c r i t i c a l F value o f 3.11 f o r p < .01.
This i n d i c a t e s t h a t t h e r e i s
l e s s than a 1% p r o b a b i l i t y t h a t t h i s va lu e could be o b ta in e d by chance
2
a l o n e . The t o t a l R was .15079. Two independent v a r i a b l e s , cumulative
grade p o i n t aver age (CGPA) and c a l c u l u s (MATH4) account f o r much
(.11038) o f t h i s v a lu e . The remaining t h r e e v a r i a b l e s account f o r an
2
i n c r e a s e in th e R o f .0404. The s t a n d a r d e r r o r o f .46460 i s r e l a ­
t i v e l y l a r g e f o r a b i n a r y v a r i a b l e . This f i n d i n g t o g e t h e r with the
2
low R might l i m i t t h i s model f o r p r a c t i c a l use. However, s i n c e t h e s e
independent v a r i a b l e s a r e a l r e a d y a v a i l a b l e on of th e t r a n s f e r s t u d e n t s
and s i n c e t h e o v e r a l l F t e s t was s i g n i f i c a n t a t th e .01 l e v e l , t h i s
80
model was s u b j e c t e d t o c r o s s v a l i d a t i o n .
The s te p w is e m u l t i p l e r e g r e s s i o n j u s t d i s c u s s e d i s d i s p l a y e d in
Tables 15 and 16 with a d d i t i o n a l d e t a i l ap pea rin g in Table 17.
In a d d i t i o n t o de veloping a b a s i c r e s t r i c t e d model, t h e foll owing
complete r e s t r i c t e d model was developed on 145 e n g in e e r in g s t u d e n t s
u t i l i z i n g a complete s e t o f independent v a r i a b l e s .
The eq uat io n
a s s o c i a t e d with t h e p r e d i c t i o n o f p e r s i s t e n c e / n o n - p e r s i s t e n c e with a
complete s e t o f independent v a r i a b l e s i s as fo ll o w s :
PNPeng(complete) = ' 02560 x ACTC * •30834 x SEX + • 18329 x CGPA
+ .18049 x MATH4 + .14583 x NINST + .27897
x DEG - 1.34855
This e q u a ti o n y i e l d e d an R2 o f .21521.
The o v e r a l l F valu e 6.30723
exceeds t h e c r i t i c a l va lu e o f 2.95 a t t h e p < .01.
The ACTC accounted
f o r .97719 o f t h e v a r i a n c e in th e dependent v a r i a b l e , p e r s i s t e n c e / h o n persistence.
Sex, c um ula tiv e grade p o i n t average (CGPA), and c a l c u l u s
2
(MATH4) c o l l e c t i v e l y accounted f o r an i n c r e a s e in R o f .10359.
Num­
ber o f i n s t i t u t i o n s (NINST) p r e v i o u s l y a t t e n d e d and degree accounted
f o r t h e remaining .03443 o f th e v a r i a n c e accounted f o r .
The sta nda rd
e r r o r o f t h e e q u a ti o n was .45091, which i s r a t h e r l a r g e f o r a bin a ry
variable.
The i n t e r c o r r e l a t i o n s between s p e c i f i c v a r i a b l e s , as found
in Table 20, a r e r e l a t i v e l y low.
(PNP and ACTC).
The h i g h e s t c o r r e l a t i o n was .27783
The s te p w is e m u l t i p l e r e g r e s s i o n a n a l y s i s f o r the
Table 15
Stepwise Multiple Regression Variance Summary
Basic Restricted Model (5 Variables)*
Dependent Variable: Persistence/Non-Persistence
Engineering Students: N=316
A nal ys is o f
Variance
Multiple R
.38831
Regression
R2
.15079
Residual
Adjust ed R2
.13709
df
Sum of
Squares
Mean Square
F Value
5
11.88172
2.37634
11.00887**
310
66.91575
.21586
Sta nda rd E r r o r .46460
*P^ e n g ( b a s i c ) '
MATH4 - . 24480 x SEX
' 22450 x CGM + - 13O31 x
.22021 x MATH2 + .00117 x NCRED - .27207
**p < .01 ( C r i t i c a l Value f o r F = 3.11; d f = 5,200)
CO
Table 16
Stepwise Multiple Regression Step Summary
Basic Restricted Model
Dependent Variable: Persistence/Non-Persistence
Engineering Students: N=Sl6
Step Number
V a r ia b le Added
R2 .
2
In c re as e in R
Simple R
I
CGPA
.06448
.06448
.25393
2
MATH4
.11038
.04590
.19738
3
SEX
.12892
.01854
-.11751
4
MATH2
.14163
.01271
-.16327
5
NCRED
.15079
.00916
.16443
CONSTANT
Table 17
Correlation Matrix
Basic Restricted Model
Dependent Variable: Persistence/Non-Persistence
Engineering Students: N=316
PNP
1,00000
CGPA
MATH2
NCRED
-
SEX
MATH2
.25393
.19738
-.11751
-.16327
.16443
1.00000
-.0 6463
.16132
.00357
-.06685
1.00000
-.12715
1.00000
-.03913
-.15117
1.00000
-.15928
g
SEX
MATH4
CO
MATH4
CGPA
I
PNP
. NCRED
.33233
1.00000
84
e n g i n e e r i n g complete r e s t r i c t e d model i s summarized in Tables 18, 19,
and 20.
Research Question Five
To what e x t e n t do v a r io u s combinations o f demographic c h a r a c t e r ­
i s t i c s and c o g n i t i v e v a r i a b l e s c o n t r i b u t e to th e p r e d i c t i o n o f the
Montana S t a t e grade p o i n t av erage f o r e n g in e e r in g s t u d e n t s ?
To answer
t h i s q u e s t i o n , two m u l t i p l e r e g r e s s i o n r e s t r i c t e d models were d e v e l ­
oped.
The f o ll o w i n g e q u a ti o n i s t h e e n g in e e r in g b a s i c r e s t r i c t e d model
to p r e d i c t MSU grade p o i n t av erage:
MSUGPAe ng^b a s i c ) = .53028 x CGPA + ,00645 x TBET - .38872 x MATH2
- .19688 x ITYPl - .24894 x CSHP + .99066
The o v e r a l l F t e s t y i e l d e d a va lu e of 19.51158 which exceeds the
c r i t i c a l va lu e o f 3.11 f o r t h e .01 le vel o f c on fi d e n c e .
Cumulative
grade p o i n t av erage (CGPA) c o n t r i b u t e d .17140 to th e t o t a l R^. The
2
remaining f o u r c o l l e c t i v e l y accounted f o r .06797. The R f o r t h i s
model i s .23937,
The s ta n d a r d e r r o r i s .67122.
An examination o f the
c o r r e l a t i o n m a t r i x t a b l e i n d i c a t e s t h a t MSUGPA and CGPA c o r r e l a t e a t
t h e l e v e l o f .41.
Although t h i s cann ot be c onsi de re d h ig h, t h i s i s
one of t h e h i g h e s t i n t e r c o r r e l a t i o n s d i s c u s s e d to t h i s p o i n t .
Results
o f t h e st ep w is e m u l t i p l e r e g r e s s i o n a n a l y s i s f o r t h e e n g i n e e r i n g b a s ic
r e s t r i c t e d model f o r p r e d i c t i n g MSUGPA a r e found in Tables 21, 22,
Table 18
Stepwise Multiple Regression Variance Summary
Complete Restricted Model (6 Variables)*
Dependent Variable: Persistence/Non-Persistence
Engineering Students: . N=I45
A n a ly s is of
Variance
M u lt ip le R
.46391
Reg ression
R2
.21521
Residual
Adjusted R2
.18109
df
Sum of
Squares
Mean Square
F Value
6
7.69416
1.28236
6.30723**
138
28.05757
.20332
-
ro
C Jl
Standard E r r o r
.45091
*PNPeng(complete) = *02560 x ACTC " •30834 x SEX + *18329 x CGPA + •18049
x MATH4 + .14583 x NINST + .27897 x DEG - 1.34855
**p < .01 ( C r i t i c a l Value f o r F = 2 .95; d f = 6,125)
Table 19
Stepwise Multiple Regression Step Summary
Complete Restricted Model
Dependent Variable: Persistence/Non-Persistence
Engineering Students: N=I45
Step Number
V a r i a b l e Added
2
I n c r e a s e in R
R2
Simple R
I
ACTC
.07719
.07719
.27783
2
SEX
.11330
.03611
-.17081
3
CGPA
.15023
.03693
.22651
4
MATH4
.18078
.03055
.19444
5
NINST
.20106
.02028
.16547
DEG
.21521
.01415
.05899
.6
CONSTANT
■
Table 20.
Complete R e s t r i c t e d Model
Dependent V a r i a b l e : P e r s i s t e n c e / N o n - P e r s i s t e n c e
Engineering S t u d e n t s : N=I45
PNP
PNP
ACTC
MATH4
CGPA
SEX
NINST
DEG
1.00000
ACTC
MATH4
CGPA
.27783
.19444
.22651
-.17081
.16547
.05899
1.00000
.02877
.31420
.06759
.03151
-.04087
1.00000
-.0 9209
-.14915
.00182
-.0 1823
I .00000
.22566
-.02632
-.07395
1.00000
-.10876
.08479
1.00000
-.0 9530
SEX
NINST
DEG
I .00000
88
and 23.
The r e g r e s s i o n e q u a ti o n a s s o c i a t e d with th e e n g i n e e r i n g complete
r e s t r i c t e d model f o r p r e d i c t i n g MSU grade p o i n t average i s as fo ll ows :
MSUGPAeng(complete) = *00981 x HSRNK + *31247 x CGPA + -16966
x NINST = .02201 x ACTC + .26930
This e q u a ti o n was developed on 123 e n g i n e e r i n g t r a n s f e r s t u d e n t s . The
2
t o t a l R f o r t h i s model (complete r e s t r i c t e d ) was .33808. The t o t a l
R f o r t h e pre v io u s model ( b a s i c r e s t r i c t e d ) was .23937.
The v a r i a b l e
added in s t e p number one, high school rank (HSRNK), accounted f o r
.23785 o f t h e v a r i a n c e .
This val ue a lo ne i s almost as l a r g e as th e
t o t a l R2 f o r t h e e n g in e e r in g b a s i c r e s t r i c t e d model f o r p r e d i c t i n g
MSUGPA.
With r e s p e c t to t h e complete r e s t r i c t e d model, t h e o v e r a l l
F va lu e o f 15.06727 exceeds t h e c r i t i c a l va lu e o f 3.51 f o r th e s i g n i ­
f i c a n c e l e v e l o f .01.
The s t a n d a r d e r r o r was .59347.
In viewing
Table 26, C o r r e l a t i o n M at r ix , i t i s e v i d e n t t h a t ACT Composite and
HSRNK a r e s t r o n g l y c o r r e l a t e d , .57720.
I t i s p o s s i b l e t h a t t h i s model
could be f u r t h e r developed using two independent v a r i a b l e s , high
school rank and cu mula tive grade p o i n t a v er ag e .
A f t e r s t e p number 2,
over .31157 of t h e v a r i a n c e was accounted f o r by t h e s e two v a r i a b l e s .
Tables 24-26 summarize t h e MSUGPA e n g i n e e r i n g complete model.
Table 21
Stepwise Multiple Regression Variance Summary
Basic Restricted Model (5 Variables)*
Dependent Variable: MSU Grade Point Average
Engineering Students: N=Sl6
A na ly si s of
Variance
M ultiple.R
.48926
Regression
R2
.23937
Residual
Adjusted R2
.22710
Sum o f
Squares
Mean Square
F Value
5
43.95309
8.79067
19.51158**
310
139.66538
.45053
df
Standard E r r o r .67122
*MSUGPAe ng( b a s 1 c ) = .53028 x CGPA + .00645 x TBET - .38872 x MATH2
-.1 9 6 8 8 x ITYPl - .24894 x CSHP + .99066
**p < .01 ( C r i t i c a l Value f o r F = 3 .1 1 ; d f = 5,200)
OO
MO
Table 22
Stepwise Multiple Regression Step Summary
Basic R estricted Model
Dependent Variable: MSU Grade Point Average
Engineering Students: N=SlG
Step Number
V a ria b le Added
R2
2
In c re a s e in R
Simple R
I
CGPA
.17140
.17140
.41400
2
TBET
.19635
.02495
.11990
3
MATH2
.21652
.02018
-.14447
4
ITYPl
.23140
.01488
-.15367
5
CSHP
.23937
.00797
-.16906
CONSTANT
Table 23
Correlation Matrix
Basic R estricted Model
Dependent Variable: MSU Grade Point Average
Engineering Students: N=Slb
MSUGPA
MSUGPA
CGPA
TBET
MATH2
ITYPl
CSHP
I .00000
CGPA
TBET
MATH2
ITYPl
.41400
.11990
-.14447
-.15367
-.16906
1.00000
-.09038
.00357
-.02105
-.11587
I .00000
-.02527
-.05101
-.09079
1.00000
.11043
.00535
1.00000
.14879
CSHP
1.00000
Table 24
Stepwise Multiple Regression Variance Summary
Complete R estricted Model (4 Variables)*
Dependent Variable: MSU Grade Point Average
Engineering Students: N=I23
A n aly sis, o f
V ariance
M u ltip le R
.58145
R egression
R2
.33808
Residual
A djusted R2
.31564
-df
Sum o f
Squares
Mean Square
F Value.
4
21.22689
5.30672
15.06727**
118
41.55982
.35220
Standard E r ro r .59347
*MSUGPAe n g ( c o m p lete) = .00981 x HSRNK + .31247 x CGPA + .16966 x NINST + .02201 x ACTC + .26930
**p < .01 ( C r i t i c a l Value f o r F = 3 .5 1 ; d f = 4,100)
Table 25
Stepwise Multiple Regression Step Summary
Complete R estricted Model
Dependent Variable: MSU Grade Point Average
Engineering Students: N=I23
Step Number
V a ria b le Added
R2
2
In c re a s e in R
Simple R
I
HSRNK
.23785
. .23785
.48769
2
CGPA
.31157
.07372
.47267
3
NINST
.32939
.01782
.07762
4
ACTC
.33808
.00869
.40797
CONSTANT
w
Table 26
Correlation Matrix
Complete R estricted Model
Dependent Variable: MSU Grade Point Average
Engineering Students: N=123
MSUGPA
HSRNK
CGPA
ACTC
NINST
CGPA
ACTC
NINST
.48769
.47267
.40797
.07762
I .00000
.48111
.57720
-.14162
1.00000
.37050
-.02059
1.00000
.10299
MSUGPA
HSRNK
1.00000
.
1.00000
95
Research Q uestion Six
To what e x t e n t do v a rio u s com binations o f demographic c h a r a c t e r ­
i s t i c s and c o n t r i b u t e to th e p r e d i c t i o n o f “low GPA n o n - p e r s is te n c e 11
f o r e n g in e e rin g s t u d e n t s ?
In o rd e r to answer t h i s q u e s ti o n , two f u l l
models were examined to determ ine which independent v a r i a b l e s
would be most a p p r o p r i a t e in developing r e s t r i c t e d models.
In th e
e n g in e e rin g b a s ic model to p r e d i c t p e r s i s t e n c e from a group o f t r a n s ­
f e r s tu d e n ts where n o n - p e r s i s t e r s w ith a grade p o in t average o f 2.5
o r g r e a t e r were excluded from a n a l y s i s , one f in d s t h a t th e p o p u la tio n
was reduced from 316 s tu d e n t s to 268 s t u d e n t s .
The model developed
c l o s e l y resem bles th e b a s ic r e s t r i c t e d model developed f o r p r e d ic tin g
p e rsiste n c e/n o n -p ersiste n ce .
Cumulative grade p o in t av erag e (CGRA)
was e n te r e d by th e ste p w is e procedure d u rin g th e f i r s t s t e p , r e s u l t i n g
in an R2 in c r e a s e o f .13829.
This compares w ith .06448 R2 in c r e a s e
when CGPA was e n te r e d in th e f i r s t s te p in th e b a s ic r e s t r i c t e d
model.
The c r i t i c a l v a lu e f o r F e q u a ls 1.97 a t th e .01 le v e l o f
sig n ific a n ce .
The c a l c u l a t e d F v a lu e f o r t h i s model was 3.77030.
The s ta n d a rd e r r o r was .43892.
In viewing Table 28, one w ill note
t h a t Yl o r y e a r 1977 was e n te r e d e a r l y in th e a n a ly s is a t s te p 6.
This may i n d i c a t e t h a t t h e r e a r e d i f f e r e n c e s between y e a r s and any
model w ith t h i s v a r i a b l e would need to be updated on an annual b a s i s .
Tables 27 and 28 summarize t h i s model.
In a d d itio n to th e above model, th e e n g in e e rin g com plete model
Table 27
Stepwise Multiple Regression Variance Summary
Basic Model (22 Variables)
Dependent Variable: Persistence/Low GPA Non-Persistence
Engineering Students: N=268
M u ltip le R
. .50292
R2
.25293
A djusted R2
.18584
Mean Square
A n a ly sis o f
Variance
d f
Sum o f
Squares
R egression
22
15.97970
.72635
245
47.19940
.19265
Residual
Stan d ard E r r o r .43892
**p < .01 ( C r i t i c a l Value f o r F = 1 .9 7 ; d f = 20,200)
F Value
3.77030**
VO
<n
Table 28
Stepwise Multiple Regression Step Summary
Basic Model
Dependent Variable: Persistence/Low GPA Non-Persistence
Engineering Students: N-268
Step Number
I
2
3
4
5
6
7
8
9
TO
11
12
13
14
15
16
17
18
19
20
21
22
V a ria b le Added
.
CGPA
MATH2
NCRED
DEGl
Ql
Yl
CENT4
SEX
CENT!
CSHP
MATH4
ITYPl
CENT7
AGE
NINST
MATHS
YS
CENTS
CENTS
TBET
ITYP2
CENTS
CONSTANT
R2
.13829
.16915
.18836
.20535
.21204
.21733
.22269
.22749
.23181
.23598
.23993
.24280
.24454
.24588
.24774
.24932
.25052
.25153
.25227
.25277
.25289
.25293
?
In c re a s e in R
.13829
.03085
.01921
.01699
.00669
.00529
.00536
.00480
.00432
.00417
.00396
.00287
.00174
.00134
.00186
.00158
.00120
.00101
.00074
.00050
.00012
.00004
Simple R
.37188
-.18880
.15939
-.07329
.05987
-.01571
-.10601
-.02953
-.04342
-.10657
.15802
-.11536
.07323
-.03159
-.00710
-.01030
.00012.
.04111
.03258
.00159
.03712
-.00226
B
.32150
-.23120
.00211
-.15342
.13517
.06896
-.06053
-.16018
-.16036
-.12173
.05604
-.08585
.14968
-.01112
.04797
-.06091
.03800
.04240
.04728
.06758
-.01342
-.01179
-.20834
v
'
98
f o r th e p r e d i c t i o n o f low GPA n o n - p e r s is te n c e was developed using 100
stu d en ts.
By e lim in a tin g n o n - p e r s i s t e r s w ith a MSUGPA o f 2 .5 or
p
g r e a t e r , 45 s tu d e n t s were e lim in a te d from th e group. The R from t h i s
complete model i s alm ost tw ice t h a t o f th e one re p o rte d f o r th e
complete r e s t r i c t e d model f o r p e r s is te n c e /l o w GPA n o n - p e r s is te n c e .
2
The R f o r t h i s complete model i s .43070 w ith a s ta n d a rd e r r o r o f
.39512.
Again, th e c r i t i c a l F v a lu e o f 2.81007 exceeds th e .01 le v e l
o f s i g n i f i c a n c e which i s 2 .1 5 .
Cumulative grade p o in t av erage (CGPA)
acco u n ts f o r an R i n c r e a s e o f .20673.
.05294 to th e R^.
ACT composite (ACTC) added
Number o f c r e d i t s (NCRED), c i t i z e n s h i p (CSHP), and
having an a s s o c i a t e deg ree (DEGl) each c o n tr ib u te d a p p ro x im ately .02
2
to th e R in c r e a s e w ith o th e r v a r i a b l e s c o n t r i b u t i n g s m a lle r amounts.
A number o f s tu d e n t s did no t p e r s i s t even though th e y achieved
a c c e p ta b le GPA l e v e l s .
Because p o t e n t i a l u s e rs o f th e model may con­
s i d e r such s tu d e n t s an u n d e s ir e a b le confounding f a c t o r , a n o th e r model
was developed which d id no t c o n s id e r th e s e s tu d e n t s .
The e lim in a tio n o f s tu d e n ts w ith MSU grade p o in t a v erag e s o f 2.50
o r g r e a t e r who were n o n - p e r s i s t e r s , r e s u l t e d in o b ta in in g a h ig h er
p
t o t a l R and lower s ta n d a rd e r r o r . Thus, th e e lim in a tio n o f 45 nonp e r s i s t e r s w ith GPA's g r e a t e r than 2 .5 a p p ears to s tr e n g th e n th e p re ­
d i c t i v e v alu e o f th e s p e c i f i c independent v a r i a b l e s .
The e n g in ee rin g
complete p e r s i s t e n c e / l o w GPA n o n - p e r s is te n c e r e s u l t s a r e r e p o r te d in
Tables 29 and 30.
Table 29
Stepwise Multiple Regression Variance Summary
Complete Model (21 Variables)
Dependent Variable: Persistence/Low GPA Non-Persistence
Engineering Students: N=IOO
A n a ly sis o f
V ariance
df
. Sum o f
Squares
Mean Square
M u ltip le R
.65623
R egression
21
9.21278
.43870
R2
.43070
Residual
78
12.17722
.15612
A djusted R2
.27743
F Value
2.81007**
g
Standard E r ro r .39512
**p < .01 ( C r i t i c a l Value f o r F = 2 .1 5 ; d f = 20,700)
'
‘
:
Table 30
Stepwise Multiple Regression Step Summary
Complete Model
Dependent Variable: Persistence/Low GPA Non-Persistence
Engineering Students: N=IOO
Step Number
I
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
R2
V a ria b le Added
CGPA
ACTC
NCRED
CSHP
DEGl
CENTS
CENTS
NINST
Y3
CENT4 '
HSRNK
SEX
ITYP3
ACTQ
MATHl
TBET
CENT!
AGE
MATH4
Yl
CENT7
CONSTANT
.
'
.20673
.25877
.28614
.30602
.32986
.34688
.36356
.38073
.39742
.40323
.40779
.41228
.41530
.41876
.42074
.42479
.42620
.42771
.42907
.43049
.43070
2
In c re a s e in R
.20673
.05204
.02737
.01988
.02385
.01702
.01667
.01717
.01669
.00581
.00457
.00449
.00302
.00346
.00198
.00405
.00141
.00151
.00136
,00142
.00021
Simple R
.
.45468
.38771
.18199
-.26237
-.05869
.09744
.07290
.16692
.02465
-.14492
.32611
.03826
.08386
.28580
-.12809
-.00548
.02648
.07833
.17602
-.03541
-.04464
B
.26484
.02186
.00339
-.56393
-.84068
.22827
.22289 ■
.13615
-.19572
-.06288
.00239
-.07491 .
-.13036
-.00408
-.15492
.00241
.12664
.01386
-.07201
-.04884
.03473
-1.04247
101
T ech n o lo g y .S tu d en ts - Research Q uestions 4-6
Research Q uestion Four
To what e x t e n t do v a rio u s com binations o f demographic c h a r a c t e r ­
i s t i c s and c o g n i t i v e v a r i a b l e s c o n t r i b u t e to th e p r e d i c t i o n o f
p e r s i s t e n c e in te ch n o lo g y t r a n s f e r s tu d e n t s ?
Stepw ise m u ltip l e
r e g r e s s i o n was used to f i n d answers to t h i s q u e s tio n .
models were developed.
Two r e s t r i c t e d
The b a s ic r e s t r i c t e d model to p r e d i c t p e r s i s ­
te n c e o f tech n o lo g y s tu d e n t s i s p re s e n te d in th e fo llo w in g e q u a tio n :
PNPt e c h ( b a s i c ) = •00434 x NCRED - .76661 x CSHP + .35019 x MATH3
- .50627 x.Q2 + .10747 x CGPA - .00299 x TBET
+ .02447
The R^ f o r t h i s model i s .31098.
The c a l c u l a t e d F value o f 5.34079
exceeds th e c r i t i c a l v a lu e o f 3.07 f o r s i g n i f i c a n c e a t th e .01 l e v e l .
The s ta n d a rd e r r o r f o r th e t o t a l e q u a tio n i s .43373.
Entered on the
p
f i r s t s t e p , number o f c r e d i t s (NCRED) accounted f o r an R in c r e a s e o f
.10698.
The in c r e a s e in R^ f o r c i t i z e n s h i p (CSHP) was .06351 w hile
trig o n o m e try (MATH3) and Spring Q u a rte r (Q2) each accounted f o r an R2
in c r e a s e o f a p p ro x im a te ly .04.
Cumulative GPA (CGPA) and time
O
between t r a n s f e r (TBET) c o n tr ib u te d a p p ro x im ately .02 to th e R i n ­
crease.
Tables 31-33 o u t l i n e th e f in d i n g s a p p r o p r ia te to t h i s model.
The complete r e s t r i c t e d model to p r e d i c t p e r s i s t e n c e o f t e c h ­
nology s tu d e n t s was a ls o developed.
T h i r t y - t h r e e tech n o lo g y s tu d e n ts
Table 31
Stepwise Multiple Regression Variance Summary
Basic R estricted Model (6 Variables)*
Dependent Variable: Persistence/N on-Persistence
Technology Students: N=78
A n a ly sis o f
V ariance
M u ltip le R
.55765
R egression
R2
.31098
Residual
A d ju sted R2
.25275
df
Sum o f
Squares
Mean Square
F Value
6
6.02821
1.00470
5.34079**
71
13.35641
.18812
S tan d ard E r ro r .43373
+ .35019 x MATH3 - .50627
* PN P tech(basic) = '00434 x NCRED - .76661 x CSHP
x Q2 + .10747 x CGPA - .00299 x TBET + .02447
**p < .01 ( C r i t i c a l Value f o r F = 3 .0 7 ; d f = 6, 70)
O
PO
Table 32
Stepwise M ultiple Regression Step Summary
Basic R estricted Model
Dependent Variable: Persistence/N on-Persistence
Technology Students: N=78
Step Number
V a ria b le Added
R2
2
In c re a s e in R
Simple R
I
NCRED
.10698
.10698
2
CSHP
.17048
.06351
3
MATH3
.21831
.04782
.24917
Q2
.25936
.04106
-.18516
5
CGPA
.28253
.02317
.29127
6
TBET
.31098
.02845
-.17017
. 4
CONSTANT
CD
CJl
O
I
.32707
Table 33
Correlation Matrix
Basic R estricted Model
Dependent Variable: Persistence/N on-Persistence
Technology Students: N-78
MATHS
Q2
TBET
CGPA
. MATH3
I . 00000
.32707
-.1 5 0 1 9
.24917
I . 00000
.28039
.04444
I . 00000
TBET
CGPA
I
CSHP
CSHP
-.17 017
.29127
.05724
-.0 3 6 9 4
,37633
-.0 5 0 9 4
-.0 3 2 4 4
-.0 7 5 4 2
-.0 3 3 4 3
1 .0 0 0 0 0
-.0 6 2 8 0
-.0 1 2 0 0
,03994
1 .0 0 0 0 0
.10614
.07872
1 .0 0 0 0 0
.09442
i
NCRED
NCRED
CM
C r
PNP
PNP
1.00000
105
had a f u l l s e t o f d a ta .
The e q u a tio n f o r t h i s model i s :
PNPtp rh frn m D lefp ) = -00249 x NCRED + .04686 x ACTQ - .55584 x Q2
- .00903 x HSRNK + .15084 x CGPA - .37189
x ITYRl - .37270
The c a l c u l a t e d F v alu e o f 3.73837 exceeds th e c r i t i c a l F v alu e o f 3.59
a t th e .01 le v e l o f s i g n i f i c a n c e .
e q u a tio n i s .39709.
The s ta n d a rd e r r o r f o r th e e n t i r e
Of th e s ix independent v a r i a b l e s in t h i s equa­
t i o n , number o f c r e d i t s (NCRED) accounted f o r .16346 o u t o f th e t o t a l
2
R f o r t h i s complete model o f .46314.
2
b a s ic model y ie ld e d a t o t a l R o f .31.
In comparison, th e co rresp o n d in g
A review o f th e c o r r e l a t i o n
m a trix i n d i c a t e s t h a t ACT q u a n t i t a t i v e (ACTQ) and high school rank
(HSRNK) i n t e r c o r r e l a t e a t r = .70.
Tables 34, 35 and 36 summarize
■ I
th e f in d i n g s o f t h i s model.
Research Q uestion Five
To what e x t e n t do v a rio u s com binations o f demographic c h a r a c t e r ­
i s t i c s and c o g n itiv e v a r i a b l e s c o n t r i b u t e to th e p r e d i c t i o n o f th e
Montana S t a t e U n iv e r s ity grade p o in t av erag e f o r technology s tu d e n ts ?
The b a s ic r e s t r i c t e d model f o r p r e d i c t i n g grade p o in t av erag e f o r
tech n o lo g y s tu d e n t s i s p re s e n te d as fo llo w s :
MSUGPAte c h ( b a s 1 c ) = .36661 x CGPA + .53645 x ITYP3 - .31237
x MATH! - 1.69425 x CSHP + .00578 x NCRED
- .19464 x ITYPl + 1.05918
Table 34
Stepwise Multiple Regression Variance Summary
Complete R estricted Model (6 Variables)*
Dependent Variable: Persistence/N on-Persistence
Technology Students: N-33
A n a ly sis o f
V ariance
M u ltip le R
.68055
R egression
R2
.46314
Residual
A djusted R2
.33926
df
Sum o f
Squares
Mean Square
6
3.53674
.58946
26
4.09962
.15768
Stan d ard E r ro r .39709
*PNPtech(complete) = ' 00249 x NCRED + ‘04686 x ACTQ " ‘55584 x Q2 " ’00903 x HSRNK
+ .15084 x CGPA - .37189 x ITYPl - .37270
**p < .01 ( C r i t i c a l Value f o r F = 3 .5 9 ; d f = 6 , 26)
F Value
.373837***
Table 35
Stepwise Multiple Regression Step Summary
Complete R estricted Model
Dependent Variable: Persistence/N on-Persistence
Technology Students: N=33
Step Number
V a ria b le Added
R2
2
In c re a s e in R
Simple R
I
NCRED
.16346
.16346
.40430
2
ACTQ
.23443
.07097
.32195
3
Q2
.29133
.05690
-.19201
4
HSRNK
.38880
.04747
.08959
5
CGPA .
.39143
.05263
.26759
.6
ITYPl
.46314
.07171
-.39223
'
CONSTANT
Table 36
Correlation Matrix
Complete R estricted Model
Dependent Variable: Persistence/N on-Persistence
Technology Students: N=33
PNP
PNP
NCRED
HSRNK
CGPA
ACTQ
Q2
ITYPl
1.00000
Q2
-ITYPl
.32195
-.19201
-.39223
.29557
.14428
-.05209
-.47283
.26217
.70375
.09705
-.09006
1.00000
.15285
.17183
.02969
1.00000
.21879
-.04020
I .00000
. 17886
NCRED
HSRNK
CGPA
ACTQ
.40430
.08959
.26759
I .00000
.11118
I .00000
1.00000
109
p
The R f o r t h i s model i s .38668.
The o v e r a ll e q u atio n was s i g n i f i ­
c a n t a t th e .01 p r o b a b i l i t y le v e l s in c e th e c a l c u l a t e F o f 7.46063
exceeded th e c r i t i c a l v a lu e o f 3 .0 7 .
The s ta n d a rd e r r o r i s .76062.
Cumulative grade p o in t av erag e c o n t r i b u t e s .19124 o f th e t o t a l R2
o f .38668.
U n iv e rs ity (ITYP3) accounted f o r .06415 in c r e a s e in R2
p
w h ile c i t i z e n s h i p (CSHP) in c re a s e d th e R .03935. In examining the
c o r r e l a t i o n m a tr ix , no mathem atics (MATH!) c o r r e l a t e d w ith MSU grade
p o in t av erag e a t - .2 7 6 .
In a d d i t i o n , number o f c r e d i t s (NCRED) and
MSU grade p o in t av erag e (MSUGPA) c o r r e l a t e d a t .377.
This model in
comparison to th e b a s ic r e s t r i c t e d model f o r p r e d ic tin g p e r s i s t e n c e f o r
th e tech n o lo g y s tu d e n ts i s s l i g h t l y s t r o n g e r .
i s ap p ro x im a te ly .0 7 .
2
The d i f f e r e n c e in R s
Tables 37-39 summarize th e b a s ic r e s t r i c t e d
model f o r p r e d i c t i n g MSUGPA f o r technology s tu d e n t s .
The com plete r e s t r i c t e d model f o r p r e d i c t i n g MSU grade p o in t
av erage f o r tech n o lo g y s tu d e n t s can be re p r e s e n te d by th e fo llo w in g
e q u a tio n :
MSUGPAtech( c o m p lete) = - 01146 x NCRED + •09750 x ACTQ + *31833
x CGPA - .90933 x Q2 + .43145 x MATHl
- .49474 x ITYPl - .00995 x HSRNK - .59666
This e q u a tio n was developed on 33 o f th e 78 technology s t u d e n t s .
R2 f o r t h i s model is .52681.
le v el.
The
This e q u a tio n i s s i g n i f i c a n t a t th e .01
The c a l c u l a t e d F v a lu e i s 3.97610 compared w ith th e c r i t i c a l
Table 37
Stepwise Multiple Regression Variance Summary
Basic R estricted Model (6 Variables)*
Dependent Variable: MSU Grade Point Average
Technology Students: N=78
A n a ly sis o f
V ariance
M u ltip le R
.62184
R egression
R2 .
.38668
Residual
A d ju sted R2
.33485
df
Sum o f
Squares
Mean Square
F Value
6
25.90469
4.31745
7.46063***
71
41.08752
.57870
S tan d ard E r ro r .76072
*MSUGPAt e c h ^b a s i c j = .36661 x CGPA + .53645 x ITYP3 - .31237 x MATHl - 1.69425
x CSHP + .00578 x NCRED - .19464 x ITYPl + 1.05918
**p < .01 ( C r i t i c a l Value f o r F - 3 .0 7 ; d f = 6 , 70)
Table 38
Stepwise Multiple Regression Step Summary
Basic R estricted Model
Dependent Variable: MSU Grade Point Average
Technology Students: N=78
Step Number
.
V a ria b le Added
R2
2
In c re a s e in R
Simple R
I
CGPA
.19124
.19124
.43731
2
ITYP3
.25539
.06415
.21992
3
MATH!
.29474
.03935
-.27589
4 .
CSHP
.34606
.05132
-.13337
5
NCRED
.37876
.03269
.37749
.38668
.00793
-.11377
6
. ITYPl
CONSTANT
Table 39
Correlation Matrix
Basic R estricted Model
Dependent Variable: MSU Grade Point Average
Technology Students: N=78
MSUGPA
MSUGPA
CGPA
ITYP3
MATHT
CSHP
NCRED
ITYPl
I .00000
CGPA
ITYP3
MATHl
CSHP
NCRED
ITYPl
.43731
.21992
-.27589
-.13337
.37749
-.11377
1.00000
-.0 7 4 6 8
-.17863
-.03343
.37633
.10557
1.00000
.00308
.27625
.14524
-.40330
1.00000
-.13175
-.40880
-.05254
1.00000
.28039
-.11141
1.00000 .
. .04058
1.00000
no
113
F v a lu e o f 3 .4 6 .
in
Number o f c r e d i t s (NCRED) accounted f o r an in c re a s e
o f ,20146 w h ile ACT q u a n t i t a t i v e (ACTQ) accounted f o r an i n ­
c re a s e o f .14379.
The rem aining f i v e independent v a r i a b l e s each
2
c o n tr ib u te d in c r e a s e s o f .02 to alm ost .05 in R .
High school rank
(HSRNK) and ACT q u a n t i t a t i v e (ACTQ) i n t e r c o r r e l a t e d a t .70.
Spring
Q u a rte r (Q2), com m unity/junior c o lle g e (ITYPl) and high school rank
(HSRNK) were n e g a tiv e c o n t r i b u t o r s to t h i s e q u a tio n .
This model is
summarized in Tables 40, 41, and 42.
Research Q uestion Six
To what e x t e n t do v a rio u s com binations o f demographic c h a r a c t e r ­
i s t i c s and c o g n itiv e v a r i a b l e s c o n t r i b u t e to th e p r e d i c t i o n o f "low
GPA n o n - p e r s is te, n c e, " fI o r technology
s tu d e n ts
?
;
r - "
;
Basic and complete
r e g r e s s io n models were developed to. answer t h i s q u e s tio n .
N o n -p e rsis-
t e r technology s tu d e n t s w ith an MSU grade p o in t average o f 2.50 o r
g r e a t e r (N=6) were excluded from th e s u b p o p u la tio n .
Using th e remain-
ing 72 c a s e s , th e b a s ic model y ie ld e d an R o f .46882.
This i s con­
s i d e r a b l y h ig h e r than th e b a s ic r e s t r i c t e d model to p r e d i c t
p e r s i s t e n c e developed on th e t o t a l group.
.15 low er.
2
The R was ap p ro x im ately
In both models, number o f c r e d i t s (NCRED) accounted f o r
th e most v a ria n c e in th e dependent v a r i a b l e .
C itiz e n s h ip (CSHP),
trig o n o m e try (MATH3 ) , and time between t r a n s f e r (TBET) were e n te re d
Table 40
Stepwise Multiple Regression Variance Summary
Complete R estricted Model (7 Variables)*
Dependent Variable: MSU Grade Point Average
Technology Students: N=33
A n a ly sis o f
Variance
.72582
R egression
R2
.52681
Residual
A djusted R2
.39431
Sum o f
Squares
Mean Square
7
15.13680
2.16240
25
13.59623
.54385
F Value
3.97610***
114
M u ltip le R
df
S tan d ard E r r o r . 73746
*MSUGPAt e c h ( co m p le te ) = .01146 x NCRED + .09750 x ACTQ + .31833 x CGPA - .90933 x Q2
+ .43145 x MATH! - .49474 x ITYPl - .00995 x HSRNK - .59666
**p .< .01 ( C r i t i c a l Value f o r F = 3.46 ; d f = 7, 25)
Table 41
Stepwise Multiple Regression Step Summary
Complete R estricted Model
. Dependent Variable: MSU Grade Point Average
Technology Students: N=33
V a ria b le Added
Step Number
R2
2
In c re a s e in R
Simple R
I
NCRED
.20146
.20146
.44884
2
ACTQ
.34524
.14379
.43998
3
CGPA
.38292
.03767
.35906
4
Q2
.42998
.04707
-.1 1 1 1 9
MATH!
.47128
.04130
-.15279
6
ITYPl
.49800
.02672
-.33280
7
HSRNK
.52681
.02881
.25538
5
-
CONSTANT
Table 42
Correlation Matrix
Complete R estricted Model
Dependent Variable: MSU Grade Point Average
Technology Students: N=33
MSUGPA
NCRED
ACTQ
CGPA
Q2
MATH!
ITYPl .
HSRNK
MSUGPA
NCRED
I .00000
Q2
MATH!,
ITYPl
HSRNK
.35906
-.11119
-.15279
-.33280
.25538
.14428
.29557
-.05209
-.39112
-.47283
.11118
I .00000
.15285
.21879
-.39602
-.04020
.70375 __
1.00000
.17183
-.20342
.02969
.26217 m
1.00000
-.02319
.17886 .
.09705
1.00000
.17593
-.32280
1.00000
-.09006
ACTQ
CGPA
.44884
.43998
. 1.00000
-
1.00000
117
e a r l y in th e r e g r e s s io n a n a l y s i s .
m ately .03 i n c r e a s e in
.
Y3 o r 1979 accounted f o r a p p ro x i­
Tables 43 and 44 o u t l i n e th e f in d i n g s .
The com plete r e g r e s s i o n model to p r e d i c t low GPA n o n - p e r s is te n c e
w?s developed on 31 o f th e 33 e l i g i b l e technology s t u d e n t s .
n o n - p e r s i s t e r s had MSUGPA1s o f 2 .5 o r g r e a t e r .
was .82004.
The
Only two
o f t h i s model
The o v e r a ll F v alu e o f 2.63807 did no t exceed th e c r i t i c a l
v alu e f o r F a t th e .05 le v e l o f s i g n i f i c a n c e ( 2 .7 0 ) .
Number o f c r e d i t s
(NCRED) accounted f o r .17487 toward th e t o t a l R^ w hile ACT q u a n t i t a t i v e
(ACTQ) accounted f o r an a d d itio n a l .12346.
High school rank (HSRNK),
cu m u lativ e grade p o in t av erage (CGPA)1 and 1979 (Y3) each c o n tr ib u te d
2
ap p ro x im a te ly .07 fo th e R i n c r e a s e .
The fin d in g s a r e summarized in
Tables 45 and 46.
Before d e p a r tin g from th e d is c u s s io n o f th e b a s ic and complete
models, th e w r i t e r w ishes to draw th e r e a d e r ' s a t t e n t i o n to th e
fo llo w in g p o i n t .
In ev ery i n s t a n c e , r e g a r d l e s s o f th e dependent
v a r i a b l e , th e complete r e g r e s s io n model accounted f o r a g r e a t e r p e r ­
c e n t o f th e v a ria n c e in comparison w ith th e b a sic r e g r e s s io n model.
2
In most c a se s d i f f e r e n c e s in th e t o t a l R s was in e x cess o f .10. The
a d d itio n o f ACT a p t i t u d e t e s t s c o re s and high school rank in c re ase d
th e e f f e c t i v e n e s s o f th e r e g r e s s io n model.
Although t h i s statem e n t
i s w idely su p p o rted by th e l i t e r a t u r e , th e r e s e a r c h e r p r e s e n ts th e
fo llo w in g c a v e a t.
The b a s ic models were developed on e n g in e e rin g and technology sub­
118
p o p u la tio n s number 316 and 78 r e s p e c t i v e l y .
The complete models were
developed u t i l i z i n g 123 e n g in e e rin g s tu d e n t s and 33 technology s t u ­
d e n ts .
The d i f f e r e n c e in th e samples upon which th e r e g r e s s i o n mod­
e l s were developed may, in p a r t , acco u n t f o r th e improvement in R2 o f
th e complete, model over th e b a s ic m odel.
For example, th e group o f
33 te ch n o lo g y t r a n s f e r s tu d e n t s who su b m itted ACT s c o re s may simply
r e p r e s e n t a d i f f e r e n t ty p e o f s tu d e n t than th o s e who did n o t submit
scores.
I f so , th e s e 33 s tu d e n t s may be i n h e r e n t l y more p r e d i c t a b l e ,
2
and th e .10 R improvement may be caused p a r t l y by t h i s non-random
s e l e c t i o n o f t h i s s u b p o p u latio n r a t h e r th an being caused e n t i r e l y by
th e use o f a d d i t i o n a l d a t a .
t h i s h y p o th e s is ,
P re lim in a ry re s e a rc h appears to confirm
2
The improvement in R s o f th e complete models over
th e b a s ic models i s due to both th e a d d itio n o f independent v a r ia b le s
and to d i f f e r e n c e s in th e s u b -p o p u la tio n s .
Double C ro s s -V a lid a tio n o f Four R e s t r i c t e d Models
Two b a s ic and two complete r e s t r i c t e d models to p r e d i c t Montana
S t a t e U n iv e r s ity grade p o in t av erage and to p r e d i c t p e r s i s t e n c e / n o n ­
p e r s i s t e n c e were, developed in t h i s s tu d y .
summarized in Tables 47 and 48.
These e i g h t models a re
For each o f th e e i g h t , th e o r ig i n a l
model r e f e r s to th e f i r s t step w ise m u l t i p l e r e g r e s s io n e n t e r i n g a l l
a v a i l a b l e independent v a r i a b l e s .
The t o t a l number o f independent
.
v a r i a b l e s a v a i l a b l e f o r c o n s id e r a tio n by t h e ste p w ise p ro ced u re were
Table 43
Stepw ise M u ltip le R egression V ariance Summary
B asic Model (13 V a ria b le s )
Dependent V a ria b le : P e rsiste n c e /L o w GPA N o n -P e rsiste n ce
Technology S tu d e n ts : N=72
A n a ly sis o f
Variance
df
Sum o f
Squares
8.43879
.64914
9.56121
.16485
M u ltip le R
.68471
R egression
13
R2
.46882
Residual
58
A d ju sted
.34976
.
S ta n d a rd E r ro r .40602
**p < .01 ( C r i t i c a l Value f o r F = 2 .5 3 ; d f = 12, 55)
Mean Square
F Value
3.93779**
Table 44
Stepwise M ultiple Regression Step Summary
Basic Model
Dependent Variable: Persistence/Low GPA Non-Persistence
Technology Students: N=72
Step Number
I
2
3
4
5
6
7
8
9
10
11
12
13
V a ria b le Added
-
NCRED
CSHP
Y3
MATHS
DEG
CGPA
TBET
ITYP3
CENT9
Q2
Yl
NINST
AGE
CONSTANT
R2
.18260
.28677
.32000
.35647
.38137
.40408
.42160
.43527
.44814
.45769
.46742
.46804
.46882
2
In c re a s e in R
.18260
.10417
.03323
.03647
.02498
.02270
.01752
.01367
.01287
.00955
.00973
.00063
.00078
Simple R
.42732
-.16903
.14344
.23440
.04688
.37672
-.16223
.09454
-.12060
-.1 1 8 6 8
-.24121
.04419
.05757
B
.00677
-1.14900
.05173
.37336
-.33071
.09691
.00204
.16398
-.14816
-.43261
-.14016
-.03122
.00584
-.06883
Table 45
Stepw ise M u ltip le R egression Variance Summary
Complete Model (19 V a ria b le s )
Dependent V a ria b le : P e rsiste n c e /L o w GPA N o n -P e rsiste n ce
Technology S tu d e n ts : N=Sl
A n a ly sis o f
V ariance
df
Sum o f
Squares
M u ltip le R
.90556
R egression
19
6.03123
R2
.82004
Residual
11
1.32361
A djusted R2
.50919
Standard E r ro r .34688
( C r i t i c a l Value f o r F = 2.7 0 @ p < .05; d f = 16, 11)
Mean Square
.31743
.
F Value
2.63807
.12033
ro
Table 46
Stepwise M u l t i p l e Regression Step Summary
Complete Model
Dependent V a r ia b le : P e rs is te n c e /L o w GPA N o n -P e rs is te n ce
Technology S tu d e n ts : N=31
Step Number
I
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
-
2
I n c r e a s e in R
V a ri a b le Added
NCRED
ACTQ
HSRNK
CGPA
Y3
ITYPl
Q3
Q2
MATH2
CENTlO
. Y2
NINST ■
MATH4 .
AGE
ITYP2
ACTC
CENTS
TBET
ACTV
CONSTANT
.17487
.29833
.37110
.45107
.52776
.63188
.72148
.76242
.78171
.79443
.80776
.81283
.81600
.81735
.81861
.81883
.81914
.81942
.82004
.17487
.12346
.07277
.07997
.07669
.10411
.08961
.04094
.01929
.01272
.01333
.00506
.00318
.00135
.00126
.00021
.00031
.00028
-.00062
Simple R
.41817
.41781
.11035
.32970
.24111
-.38933
.11241
-.14510
.20438
.15817
-.0 7060
. .00956
.19168
.18219
.10687
.29414
.01162
.03926
.14179
B
.00093
.07224
-.01651
.20005
.52228
-.61097
.26508
-.87407
-.20409
.16676
-.09397
-.07701
-.06231
.00873
-.03895
.01639
-.04817
-.00163
-.00887
-.99270
Table 47
Summary o f M u l t i p l e Reg ression O rig in a l and R e s t r i c t e d
Models Developed on Engineering St uden ts
V
.
RESTRICTEDM
ODEL
ORIGINAL M
ODEL
Independent Variables with R2 Change
No. of
Independent
N Variables
No. of
Independent Variables
Rt
R2
M
ODEL
Dependent
Variable
BASIC
PNP
'316
22
.198**
5
.151**
COMPLETE
PNP
123
24
.301*
6a
BASIC
MSUGPA 316
21
.277**
COMPLETE
MSUGPA 123
25
.426**
Step 2
Step 3
Step 4
Step 5
CGPA 06
MATH4 .04
SEX .01
MATH2 .01
NCRED .00
.215**
ACTC 07
SEX .03
CGPA .03
MATH4 .03
NINST .02
5
.239**
CGPA 17
TBET .02
MATH2 .02
ITYPl .01
CSHP .00
4
.338**
HSRNK 23
CGPA .07
NINST .01
ACTC .00
Step
Independent variables with negative regression coefficients are underlined.
eP < .05
**p < .01
aH= 145 on restricted model due to the elimination of the independent variaule HSRNK. Thus, the total Hincreased from 123 to 145.
Step 6
DEG .02
Table 48
Summary o f M u l t i p l e Reg ression O ri g in a l and R e s t r i c t e d
Models Developed on Technology St ude nts
ORIGINAL M
ODEL
. RESTRICTEDM
ODEL
' '
Independent Variables with R2 Change
No. of
Independent
N Variables
-
No. Of .
Independent «
Variables Rt
M
ODEL
Dependent
Variable
BASIC
PNP
78
16
.43**
6
.310**
NCRED .10 CSHP .06 MATH3 104 Q2
COMPLETE
PNP
33
19
.75
6
.463**
NCRED .16 ACTQ .07 Q2
BASIC
MSUGPA
78
18
.47**
6
.386**
CGPA .19 ITYP3 .06 MATH! .03 CSHP .05 NCRED .03 ITYPl .0
COMPLETE
'MSUGPA
33
20
.71
7
.526**
NCREO.20 ACTO .14 CGPA .03 02
Step I
Independent variables with negative regression coefficients are underlined.
**p <.01
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
.04 ' CGPA .02 TBET .02
.05 HSRNK.04 CGPA .05 ITYPl .07
.04 MATHl .04 ITYPl .02 HSRNK.02
125
32 f o r t h e b a s i c models and 36 f o r t h e complete models.
Independent
v a r i a b l e s were only e n t e r e d i f th e p r o p o r t i o n o f i t s v a r i a n c e not ex­
p l a i n e d by o t h e r indepen de nt v a r i a b l e s merely exceeds .00001.
This
f a c t a cc ounts f o r th e d i f f e r e n c e in t h e number o f independent v a r i a b l e s
shown in Tables 47 and 48.
V a r i a b l e s which provided a n e g a t i v e c o n t r i ­
bu ti o n t o t h e r e s t r i c t e d e q u a t i o n s a r e u n d e r l i n e d .
The l e v e l o f
s i g n i f i c a n c e usi ng t h e o v e r a l l F t e s t i s r e p o r t e d a t t h e .05 and .01
levels.
From th e e i g h t r e s t r i c t e d models r e p o r t e d in Tables 47 and 48,
t h e r e s e a r c h e r s e l e c t e d f o u r r e s t r i c t e d models f o r double c r o s s validation.
The r a t i o n a l e f o r s e l e c t i n g t h e s e f o u r fo l l o w s .
2
F i r s t , t h e R o f each o f th e r e s t r i c t e d models were compared.
With r e s p e c t t o t h e dependent v a r i a b l e , MSUGPA, t h e complete r e s t r i c t e d
model f o r both th e e n g i n e e r i n g and te chnology su b p o p u la t io n s e x h i b i t e d
2
t h e h i g h e s t R f o r t h e i r r e s p e c t i v e groups and were s i g n i f i c a n t a t
t h e .01 l e v e l .
These two models were developed u t i l i z i n g a l l o f the
s t u d e n t s who had a complete s e t o f 36 independent v a r i a b l e s .
Of the
o r i g i n a l e n g i n e e r i n g s ubp opu la ti on o f 316 s t u d e n t s only 123 had com­
p l e t e d a t a ; of t h e o r i g i n a l technology s ubpopula ti on o f 78, 33 had
complete d a t a .
As one would e x p e c t , complete models produced highe r
2
R than t h e b a s i c models.
9
However, t h e h i g h e r R could have been
caused simply by chance through t h e s e l e c t i o n of s t u d e n t s in the
s m a l l e r p o p u la ti o n r a t h e r than by t h e a d d i t i o n o f ACT s c o r e s and
high school rank as p r e d i c t i v e v a r i a b l e s .
Double c r o s s - v a l i d a t i o n
.
.
126
was used t o deter mine whether chance s e l e c t i o n o f s t u d e n t s was a
f a c t o r o r whether th e models were a pprox im at el y the same even i f
d i f f e r e n t s t u d e n t p o p u l a t i o n s had been used.
In a d d i t i o n to th e two complete r e s t r i c t e d models to p r e d i c t
MSUGPA f o r e n g i n e e r i n g and te chnology s t u d e n t s , two p e r s i s t e n c e / n o n ­
p e r s i s t e n c e b a s i c r e s t r i c t e d models (one f o r e n g in e e r in g and one f o r
te chn ol ogy) were a l s o s e l e c t e d f o r double c r o s s - v a l i d a t i o n because
o f t h e i r pragmatic importance to t h e U n i v e r s i t y .
The independent
v a r i a b l e s a s s o c i a t e d with t h e s e b a s i c models a r e r e a d i l y a v a i l a b l e on
ev ery t r a n s f e r s t u d e n t and th e p r e d i c t i o n o f p e r s i s t e n c e / n o n - p e r s i s ­
te n c e i s o f g r e a t i n t e r e s t to U n i v e r s i t y a d m i n i s t r a t i o n .
Before
st udy in g th e v a l i d a t i o n f i n d i n g s o f each o f th e fo u r models, the
r e a d e r may wish to r e f e r to Chapter 3 to review t h e double c r o s s v a l i d a t i o n p ro c e dur e.
Each o f t h e f o u r r e s t r i c t e d models were t e s t e d using th e
fo ll o w i n g pro ce du re.
The group upon which t h e s p e c i f i c r e s t r i c t e d
model was developed was d iv id e d i n t o two equal samples (A and B).
Using th e independent v a r i a b l e s from th e r e s t r i c t e d model, ste pw is e
m u l t i p l e r e g r e s s i o n was a p p l i e d t o sample A t o develop a new r e g r e s ­
si on e q u a t i o n .
This new e q u a ti o n was then used to p r e d i c t the
dependent v a r i a b l e , MSUGPA o r p e r s i s t e n c e / n o n - p e r s i s t e n c e , f o r
s t u d e n t s in sample B.
The p r e d i c t e d dependent v a r i a b l e v a lu es
(Y 's ) were c o r r e l a t e d with th e a c t u a l dependent v a r i a b l e va lu es
127
(Ys) f o r each s u b j e c t in sample B by using a Pearson c o r r e l a t i o n
pr oc ed ure.
This simply measured the accura cy o f th e p r e d i c t i o n .
The
r e v e r s e was then performed, de veloping an e quat io n on sample B,
appl yin g t h a t e q u a ti o n on A, and comparing th e a c t u a l v e rs us the p r e ­
dicted values.
The goal o f t h i s pro c e ss was not to n e c e s s a r i l y im­
prove t h e p r e d i c t i v e a c c u ra c y , but t o see i f t h a t a cc ura cy was roughly
c o n s t a n t when developed on d i f f e r e n t groups of s t u d e n t s .
In t h e case o f comparing p r e d i c t e d MSUGPA with a c t u a l MSUGPA, no
co nv er sio n o f t h e p r e d i c t e d va lu es was n e c e s s a ry s i n c e th e dependent
v a r i a b l e i s c o n ti n u o u s .
However, in t h e case o f a dichotomous v a r i ­
a b l e such as p e r s i s t e n c e / n o n - p e r s i s t e n c e , i t would no t be a p p r o p r i a t e
t o c o r r e l a t e a co ntin uous p r e d i c t e d val ue with an a c t u a l bin a ry v a lu e.
T h e r e f o r e , th e p r e d i c t e d va lu es f o r p e r s i s t e n c e / n o n - p e r s i s t e n c e were
rounded to 0 and I..
Y1 va lu es g r e a t e r than .49995 were ass ig ne d a
v a lu e o f I ; Y1 va lu es l e s s than .50000 were as s ig ne d a va lu e o f 0
p r i o r to using t h e Pearson c o r r e l a t i o n s t a t i s t i c a l pro ce dure.
MSUGPA Models
The double c r o s s - v a l i d a t i o n o f th e e n g in e e r in g complete r e s t r i c t e d
model and t h e te chnology complete r e s t r i c t e d model i s pre s e n te d in
p
Table 49. The R and t h e s ta n d a rd e r r o r o f the e q u a t i o n s developed
a r e p r e s e n te d as well as th e Pearson r ' s c o r r e l a t i n g th e a c t u a l Y1s
with t h e p r e d i c t e d Y's on each o f th e samples.
128
Table 49
R e s u lt s o f Double C r o s s - V a l i d a t i o n
Dependent V a r ia b le : MSU Grade Po in t Average
Pearson
Correlation
( a c t u a l vs p r e d i c t e d )
R2 '
Standard
Error
123
.338**
.59347
New Version Developed
on Sample A and
Applied to Sample B
62
.324**
.61436
.587**
New Version Developed
on Sample B and
Applied to Sample A
61
.349**
.59733
.566**
Complete R e s t r i c t e d
33
.526**
.73746
New Version Developed
on Sample A and
Applied to Sample B
17
.584
.86972
.446*
New Version Developed
on Sample B and
Applied to Sample A
16
.724*
.56306
.357
Model
N
'
Engineering Models
Complete R e s t r i c t e d
—
Technology Models
*p < .05
**p < .01
—
129
The e n g i n e e r i n g complete r e s t r i c t e d model developed on sample A
2
with an R o f .324 was a p p l i e d to sample B. The r e s u l t a n t Pearson r
was .587, s i g n i f i c a n t a t th e .01 l e v e l .
The eq uat io n developed on
2
sample B with an R o f .349 and a s ta n d a r d e r r o r o f .59733 was a p p li e d
t o sample A.
The a c t u a l v e rs us t h e p r e d i c t e d MSUGRA were c o r r e l a t e d
using th e Pearson method.
a t th e .01 l e v e l .
The Pearson r was .566 and was s i g n i f i c a n t
This model, in comparison with th e o t h e r t h r e e ,
held up t h e b e s t under double c r o s s - v a l i d a t i o n .
The outcome o f th e double c r o s s - v a l i d a t i o n o f th e complete
r e s t r i c t e d model on techn olo gy s t u d e n t s i s a l s o summarized in Table
2
49. The e q u a ti o n developed on sample I y i e l d e d an R o f .584. This
eq u at i o n when a p p l i e d to sample 2 y i e l d e d a Pearson c o r r e l a t i o n of
.446, with a .05 le v el o f s i g n i f i c a n c e . The eq u at i o n developed on .
2
sample 2, r e s u l t i n g in an R o f .724 and a s ta n d a rd e r r o r o f .56306,
was a p p l i e d to sample 2.
The Pearson r was .357 and was not s i g n i f i ­
cant.
P e r s i s t e n c e / N o n - P e r s i s t e n c e Models
The two models p r e d i c t i n g p e r s i s t e n c e / n o n - p e r s i s t e n c e t h a t were
s e l e c t e d f o r double c r o s s - v a l i d a t i o n were th e b a s ic r e s t r i c t e d model
f o r e n g i n e e r i n g and th e b a s ic r e s t r i c t e d model f o r t e c hnol ogy.
These
models were analyzed acc or din g t o th e procedure o u t l i n e d e a r l i e r in
t h i s s e c t i o n e xce pt t h a t t h e d i s c r e e t n a t u r e o f p e r s i s t e n c e / r i o n -
130
p e r s i s t e n c e r e q u i r e d a s l i g h t l y d i f f e r e n t method o f measuring the
accura cy o f th e models.
The r e s u l t s f o r th e o r i g i n a l e n g in e e ri n g
model a r e shown in Table 50 while th e r e s u l t s o f the e n g in e e r in g
sample A v e r s i o n and sample B v e rs io n a r e p re s e n te d in Tables 51 and
52, r e s p e c t i v e l y .
The r e s u l t s o f th e o r i g i n a l technology model are
p r e s e n te d in Table 53 w hi le th e r e s u l t s o f th e technology sample A
v e r s i o n and sample B v e r s i o n a r e p r e s e n te d in Tables 54 and 55.
The
R2 | s , Pearson r ' s , and p e r c e n t o f a ccu racy a r e summarized in Table 56.
With r e s p e c t to t h e b a s i c r e s t r i c t e d model f o r e n g i n e e r i n g s t u ­
d e n t s , t h e p r a c t i c a l accuracy o f t h i s model as r e p o r t e d in Table 50
i s 58.3% f o r t h e t o t a l p o p u l a t i o n .
The model developed on 316 e n g i ­
ne ers and a p p l i e d to t h a t s ubp op ula ti on c o r r e c t l y p r e d i c t e d 16.5% of
t h e p e r s i s t e r S and 41.8% of t h e n o n - p e r s i s t e f s .
The Pearson r f o r
t h i s a n a l y s e s was .2324 and was s i g n i f i c a n t a t the .001 l e v e l ,
This
model was s u b j e c t e d to double c r o s s - v a l i d a t i o n .
The r e s u l t s o f t h e double c r o s s - v a l i d a t i o n a n a l y s i s o f th e b a s ic
r e s t r i c t e d model f o r e n g i n e e r i n g s t u d e n t s a r e as f o l l o w s .
The a p p l i c a
t i o n o f t h e eq u at i o n developed on sample A and a p p li e d t o B (Table 52)
c o r r e c t l y p r e d i c t e d 36.7% o f th e p o p u la ti o n as p e r s i s t e r s and 26.6%
o f th e p o p u la ti o n as n o n - p e r s i s t e r s .
t o t a l a cc u ra cy .
engineering
is
The sum o f t h e s e v a lu es i s 63.3%
p
A review o f Table 56 i n d i c a t e s t h a t t h e R f o r
.248 and i s not s i g n i f i c a n t a t the .05 l e v e l .
The.
a p p l i c a t i o n o f th e e q u a ti o n developed on sample B and a p p l i e d to A
131
Table 50
Accuracy o f Model P r e d i c t i n g P e r s i s t e n c e / N o n - P e r s i s t e n c e
Basic R e s t r i c t e d Model Applied t o Engineering Subpopulation
N=316
Actual S t a t u s
*
CO
3
•P
ro
-P
Persister
Persister
Non-Persister
C o rr e c t
E r ro r
52
(16.5%)
18
(5.7%)
</>
"O
OJ
•p
O
•r—
-
2
Q -
Non-Persister
E r ro r
C orre c t
114
(36.1%) .
132
(41.8%)
CGPA + .1303 x MATH4 - .2448 x SEX - .2202
*PNPENG(basic) " ' 2446 x
x MATH2 - .0012 x NCRED- .2721
This e q u a ti o n was developed on th e e n g in e e r in g s ubpopulat ion and
y i e l d e d an
o f .151 and was s i g n i f i c a n t a t th e .01 l e v e l . Tables
15-17 summarize t h i s model. The a p p l i c a t i o n o f t h i s model on the
e n g in e e r in g su bpo p u la t io n r e s u l t e d in a Pearson r .= .2324 which was
s i g n i f i c a n t a t p < .001. The sum o f th e 4 c e l l s equal 100% and a
t o t a l N o f 316. This model a c c u r a t e l y p r e d i c t e d 58.3% o f t h e c a s e s ,
using .5 as t h e p o i n t t o d i f f e r e n t i a t e p e r s i s t e r s from n o h - p e r s i s t e r s
(NOTE: The accura cy of t h i s model was improved to c o r r e c t l y p r e d i c t ­
ing 65.8% of th e c ase s by a d j u s t i n g t h e val ue to d i f f e r e n t i a t e p e r ­
s i s t e r s and n o n - p e r s i s t e r s . See Appendix B f o r f u r t h e r e x p l a n a t i o n
as well as a p p l i c a t i o n o f t h i s model t o t h e p e r s i s t e n c e / l o w GPA
non-persistence group.)
132
Table 51
Double C r o s s - V a l i d a t i o n
Accuracy o f Model P r e d i c t i n g P e r s i s t e n c e / N o n - P e r s i s t e n c e
Basic R e s t r i c t e d Model Applied t o Engineering St ude nts Sample A
N=I 58
Actual S t a t u s
*,
5
rd
+->
Persister
*o
CU
Non-Persister
z
Persister
Non-Persister
C o rr e c t
Error
46
(29.1%)
27
(17.1%)
E rr or
C o rr e c t
38
(24.1%)
47
(29.7%)
Cl .
*PNPENG(basic) = *2391 x CGPA - .4293 x SEX - .1312 x MATH2 + .0006
x NCRED - .0087 x MATH4 - .1361
This equation was developed on engineering sample B and yielded an
R2 of .12917 and was not s i g n i f ic a n t a t the .05 l e v e l . The applica­
tion of t h is model on sample A resulte d in a Pearson r = .1829 which
was s i g n i f i c a n t a t p < .011. The sum of the 4 c e l ls equal 100% and .
a t o ta l N of 158. This model accurately predicted 58.8% of the. cases
133
Table 52
Double C r o s s - V a l i d a t i o n
Accuracy of Model P r e d i c t i n g P e r s i s t e n c e / N o n - P e r s i s t e n c e
Basic R e s t r i c t e d Model Applied to Engineering Stu de nts Sample B
N=I 58
Actual S t a t u s
3
4- )
fO
+->
Persister •
Persister
Non-Persister
C orre c t
E r ro r
58
(36.7%)
34
(21.5%)
Er ro r
C o rr e c t
24
(15.2%)
42
(26.6%)
C/> ________________ . -
-o
CU
4- >
U
^
CU
5-
Non-Persister
O-
*PNPENG(basic) = - 2449 x m m + *2441 x CGPA - .4498 x MATH2 + .0016
x NCRED - .1189 x SEX - .3671
This equation was developed on engineering sample A and yielded an
R2 of .248 and was not s i g n i f ic a n t a t the .05 level. The application
of t h i s model on sample B resulte d in a Pearson r = .2634 which was
s i g n i f ic a n t a t p < .001. The sum of 4 c e l l s equal 100% and a total
N of 158. This model c o rre ctly predicted 63.3% of the cases.
134
(Table 51) r e s u l t e d in c o r r e c t l y p r e d i c t i n g 29.1% o f t h e p o p u la tio n
as p e r s i s t e r s and 29.7% of t h e p o p u la ti o n as n o n - p e r s i s t e r s , which
O
r e p r e s e n t s an o v e r a l l accura cy o f 58.8%. The R o f t h i s e q u a ti o n i s
.129 and a l s o i s not s i g n i f i c a n t a t th e .05 l e v e l .
These two e quat io ns
comparing t h e p r e d i c t e d with th e a c t u a l y i e l d e d Pearson r ' s o f .182
and .263, both s i g n i f i c a n t a t the .01 l e v e l .
Thus, th e general f i n d ­
ings o f t h e double c r o s s - v a l i d a t i o n procedure o f th e b a s i c r e s t r i c t e d
model f o r e n g i n e e r s i s t h a t t h e model does minimally w i t h s t a n d the
procedures o f double c r o s s - v a l i d a t i o n . This i s supported by the
2
minimal R 1s and Pearson r ' s as well as th e p e r c e n t o f accura cy o f th e
model.
The b a s i c r e s t r i c t e d model to p r e d i c t p e r s i s t e n c e / n o n - p e r s i s t e n c e
f o r techn olo gy s t u d e n t s was a l s o an al yz e d.
The eq uat io n developed on
th e 78 techn olo gy t r a n s f e r s t u d e n t s was a p p l i e d to th e s ubpopula ti on
upon which i t was developed.
Table 53 shows t h a t th e model c o r r e c t l y
p r e d i c t e d 34.6% o f t h e p o p u la ti o n as p e r s i s t e r s and 42.4% as nonpersisters.
77.0%.
Thus, t h e o v e r a l l accuracy f o r c o r r e c t p r e d i c t i o n s was
The comparison o f t h e a c t u a l with th e p r e d i c t e d was .5357. and
was s i g n i f i c a n t a t th e .001 l e v e l .
This b a s i c r e s t r i c t e d model f o r te chnology s t u d e n t s was t e s t e d
using double c r o s s - v a l i d a t i o n .
The e q u a ti o n developed on sample A
was a p p l i e d to sample B (Table 55).
The o v e r a l l accura cy o f t h i s
eq u at i o n was 69.2%, c o r r e c t l y p r e d i c t i n g 25.6% o f th e p o p u la ti o n as
135
Table 53
Accuracy of Model P r e d i c t i n g P e r s i s t e n c e / N o n - P e r s i s t e n c e
Basic R e s t r i c t e d Model Applied t o Technology Subpopulation
N=78
Actual S t a t u s
*,
3
4->
fd
-P
CO
Persister
Persister
Non-Persister
C o rr e c t
Error
27
(34.6%)
9
(11.5%)
E r ro r
C o rr e c t
9
(11.5%)
33
(42.4%)
__________________________________
<D
4->
U
'•5 N o n - P e r s i s t e r
0)
5Ol
*PNPTECH(basic) = •0043 x NCRED " - 7666 x CSHP + .3502 x MATH3 - .5063
x Q2 + .1075 x CGPA - .003 x TBET + .0245
This e q u a ti o n was developed on t h e techn ol ogy sub po pulat io n and y i e l d e d
an R2 o f .310 and was s i g n i f i c a n t a t t h e .01 l e v e l . Tables 31-33
summarize t h i s model. The a p p l i c a t i o n o f t h i s model on th e technology
su bpo p u la t io n r e s u l t e d in a Pearson r = .5357 which was s i g n i f i c a n t ,
a t p < .001. The sum o f t h e 4 c e l l s equal 100% and a t o t a l N o f , 78.
This model a c c u r a t e l y p r e d i c t e d 77.0% o f t h e c a s e s .
136
Table 54
Double C r o s s - V a l i d a t i o n
Accuracy of Model P r e d i c t i n g P e r s i s t e n c e / N o n - P e r s i s t e n c e
Basic R e s t r i c t e d Model Applied to Technology St uden ts Sample A
N-39
Actual S t a t u s
Persister
-P
tX3
■P
(Z>
Persister
Noh-Persister
C o rr e c t
E r ro r
12
(30.8%)
-o
CD
-P
O
Er ro r
'-S N o n - P e r s i s t e r
£
CL
(15.4%)
6
8
.
(20.5%)
C or re c t
13
(33.3%)
*PNPTECH(basic) = *1535 x CGPA + • ° 048 x NCRED - .7828 x CSHP - .2445
x MATH3 - .0021 x TBET - .0835
This equation was developed on technology sample B and yielded an r2
of .326 and was s i g n i f ic a n t a t the .05 lev e l. The application of th is
model on sample A resulted in a Pearson r = .3850 which was s i g n i f i ­
cant a t p < .039. The sum of the 4 c e l l s equal 100% and a to ta l N
of 39. This model accurately predicted 64.1% of the cases.
137
Table 55
Double C r o s s - V a l i d a t i o n
Accuracy o f Model P r e d i c t i n g P e r s i s t e n c e / N o n - P e r s i s t e n c e
Basic R e s t r i c t e d Model Applied t o Technology Stu de nt s Sample B
N=39
Actual S t a t u s
*
(/)
n
+->
fO
+->
CO
Persister
Non-Persister
C o rr e c t
E r ro r
10
(25.6%)
4
(10.3%)
Er ro r
C o rr e c t
8
(20.5%)
17
(43.6%)
---------------------------------------------------
“O
CD
4—
)
Non-Persister
■s
Persister
CL
*PNpTECH(basic) = *5239 x MATH3 " •003 x TBET “ -4312 x Q2 + .0034
x NCRED - .6778 x CSHP + .04 x CGPA + .2001
This equation was developed on technology sample A and yielded an
r2 of .383 and was s i g n i f i c a n t a t the .05 l e v e l . The application
of t h i s model on sample B resulte d in a Pearson r = .3790 which was
s i g n i f ic a n t a t p < .009. The sum of the 4 c e l l s equal 100% and a
t o ta l N of 39. This model accurately predicted 69.2% of the cases.
138
Table 56
R e s u lt s o f Double C r o s s - V a l i d a t i o n
Dependent V a r ia b le : P e r s i s!t e n c e / N o n - P e r s i s t e n c e
Model
2
Pearson
Correlation
( a c t u a l vs p r e d i c t e d )
N
Perc en t of
Accuracy
Engineering Models
Basic R e s t r i c t e d ,
316
.151**
.2324**
58.3
New. Version Deve­
loped on Sample A
and Applied to
Sample B
158
.248
.2634**
63.3
New Version Deve­
loped on Sample B
and Applied to
Sample A
158
.129
.1829*
58.8
Technology Models
Basic R e s t r i c t e d
78
.310**
.5357**
77.0
New Version Deve. loped on Sample A
and Applied to
Sample B
39
.383*.
.3790**
69.2
New Version Deve­
loped on Sample B
and Applied to
Sample A
39
.326*
.2850*
64.1
*p < .05
**p < .01
139
p e r s i s t e r s and 43.6% o f th e po p u la ti o n as n o n - p e r s i s t e f s .
son r was .379 and was s i g n i f i c a n t a t t h e .01 l e v e l .
developed on sample B was a p p l i e d to sample A.
eq u at i o n i s r e p o r t e d in Table 54.
eq u at i o n was 64.1%.
The Pear­
The eq uat io n
The a cc ura cy o f t h i s
The o v e r a l l e f f e c t i v e n e s s o f t h i s
The a p p l i c a t i o n o f t h i s eq u at i o n y i e l d e d a
Pearson c o r r e l a t i o n o f .285, s i g n i f i c a n t a t t h e .05 l e v e l .
This
model developed on te chnology s t u d e n t s a ppear s to be s t r o n g e r and more
a c c u r a t e than the b a s i c r e s t r i c t e d model f o r e n g in e e ri n g s t u d e n t s .
This model held up c o n s i s t e n t l y under double c r o s s - v a l i d a t i o n .
I n t e r n a t i o n a l St ude nts
Although t h e s i x major r e s e a r c h q u e s t i o n s did not s p e c i f i c a l l y
a d d re ss th e is u b g ro u p o f i n t e r n a t i o n a l s t u d e n t s , t h i s subgroup m e r i t s
special consid eratio n .
Canada o r Other.
To review, s t u d e n t s were c l a s s i f i e d as USA/
This v a r i a b l e was e n t e r e d i n t o th e m u l t i p l e r e g r e s ­
si on a n a l y s i s and was s i g n i f i c a n t in some models, e s p e c i a l l y those
in v o lv in g t h e te chnology su b p o p u la t io n .
The term i n t e r n a t i o n a l r e f e r s
here to a l l th o s e who were c l a s s i f i e d as " o th e r" and being n e i t h e r
American nor Canadian.
Table 57 compares i n t e r n a t i o n a l s t u d e n t s with
American/Canadian c i t i z e n s on s e l e c t e d v a r i a b l e s .
There were a t o t a l of 29 i n t e r n a t i o n a l t r a n s f e r s t u d e n t s in t h i s
s tu dy .
Al I e xc e pt one were male.
Twenty-seven were e n r o l l e d in
e n g i n e e r i n g c u r r i c u l a and two were e n r o l l e d in te chnology c u r r i c u l a .
With r e s p e c t to s t a n d a r d i z e d t e s t i n f o r m a t i o n , two had r e s u l t s from
140
Table 57
A Comparison o f I n t e r n a t i o n a l Stu de nts and USA/Canadian Students
On S e l e c t e d Demographic and Cog nit ive V a ri a ble s
(N=394)
■■
V a ri a b le
P e rc e n t o f Total Pop ul at io n
International.
USA/Canadian
Stu de nts
(N=29) .
Students
(N=365)
7.4
92.6
P e rc e n t P e r s i s t i n g R e l a t i v e to
Own Group (10/29; 191/365)
34.5
52.3
Mean Number o f Months Between
Transfer
5.5
11.9
Mean GPA from P r i o r I n s t i t u t i o n s
2.55
2.75
Mean MSUGPA,.at End o f Study
1.98
2.42
Mean F i r s t Q ua rt e r MSUGPA
1.85
2.42
Mean Number of C r e d i t s Accepted
for Transfer
87.7
59.8
141
t h e T e s t o f Engl ish as a Foreign Language (TOEFL) and f i v e had ACT
r e s u l t s r e p o r t e d p r i o r t o admission.
The ACT q u a n t i t a t i v e , v e r b a l ,
and composite r e s u l t s were lower f o r i n t e r n a t i o n a l s t u d e n t s than f o r
t h e USA/Canadian s t u d e n t s .
variables.
Table 57 summarizes d a ta f o r s e l e c t e d
The p e r s i s t e n c e r a t e f o r two y e a r s was 34.5% f o r the
i n t e r n a t i o n a l s t u d e n t s compared with 52.3% f o r USA/Canada.
The mean
number o f months between t r a n s f e r f o r USA/Canadian s t u d e n t s was roughly
twice t h a t f o r i n t e r n a t i o n a l s t u d e n t s .
A review o f th e mean number
o f c r e d i t s a cce pt ed f o r t r a n s f e r r e v e a l s t h a t i n t e r n a t i o n a l s t u d e n t s
have completed an a d d i t i o n a l 27.9 c r e d i t s than USA/Canadian s t u d e n t s
(5 9. 8 vs 8 7 . 7 ) .
However, t h e c o g n i t i v e v a r i a b l e s o f grade p o i n t
aver age from p r i o r i n s t i t u t i o n s , mean f i r s t q u a r t e r MSU grade p o in t
a v er ag e , and mean MSUGPA a t t h e end o f th e stu dy a r e lower than f o r
t h e USA/Canada group.
In summary, i t appea rs t h a t t h e i n t e r n a t i o n a l
s t u d e n t e x p e r ie n c e s a g r e a t e r l o s s in grade p o i n t average upon t r a n s ­
f e r r i n g to Montana S t a t e than USA/Canadian c i t i z e n s .
Although the
i n t e r n a t i o n a l s t u d e n t may have completed a d d i t i o n a l t r a n s f e r a b l e
c o l l e g e coursework, i t d o e s n ' t appear t o help them perform a t th e same
l e v e l as n o n - i n t e r n a t i o n a l s t u d e n t s .
Summary
This c h a p t e r p r e s e n te d th e r e s u l t s and f i n d i n g s o f t h i s study.
Six major r e s e a r c h q u e s t i o n s were answered.
D e s c r i p t i v e a n a l y s i s of
142
th e e n g i n e e r i n g and techn ol ogy s u b -p o p u l a t i o n s were p r e s e n t e d .
A total
o f twelve m u l t i p l e r e g r e s s i o n models were developed; s i x p r e d i c t i n g
p e r s i s t e n c e / n o n - p e r s i s t e n c e and s i x p r e d i c t i n g MSU grade p o i n t average.
Four r e s t r i c t e d models were s e l e c t e d f o r double c r o s s - v a l i d a t i o n a n a l y ­
sis.
Although s t a t i s t i c a l s i g n i f i c a n c e was found th rou ghout the s tu d y ,
t h e p r a c t i c a l s i g n i f i c a n c e o f the models i s s t i l l q u e s t i o n a b l e .
The
l i m i t a t i o n s o f the p r a c t i c a l valu e o f t h e s e models i s d i s c u s s e d in
t h e fo ll o w i n g c h a p t e r .
CHAPTER 5
SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
This c h a p t e r summarizes the stud y " F a ct o rs Related to P e r s i s t e n c e
o f T r a n s f e r St u d e n ts En r o ll e d in Engineering and Technology Programs
a t t h e Montana S t a t e U n i v e r s i t y College o f En g i n e e ri n g ."
I t also
d i s c u s s e s th e c o n c l u s i o n s r e l a t i n g to t h e a n a l y s i s o f d a t a and makes
recommendations f o r f u r t h e r r e s e a r c h .
The c h a p t e r i s o r ga niz ed under
t h e fo ll o w i n g headings:
(a)
Stateme nt o f t h e Problem
(b)
Summary of t h e P o pula tio n and Procedures
(c)
Conclusion
(d)
Recommendations
Statem en t of t h e Problem
This study was designed to c o n t r i b u t e to th e Montana S t a t e Univer­
s i t y College o f E n g i n e e r i n g ' s e f f o r t s t o i n c r e a s e i t s unde rs tan din g
and r e t e n t i o n of t r a n s f e r s t u d e n t s in both th e e ng in e e ri n g and t e c h ­
nology programs.
This stud y i n v e s t i g a t e d demographic and c o g n i t i v e
v a r i a b l e s t h a t d i f f e r e n t i a t e p e r s i s t e r and. n o n - p e r s i s t e r t r a n s f e r
s t u d e n t s who i n i t i a l l y e n t e r e d the Montana S t a t e U n i v e r s i t y College
o f Engineering duri ng t h e 1977-78 and 1978-79 academic y e a r s and
Autumn Q ua rt e r 1979.
144
Summary, of th e Po pu la ti o n and Procedures
The t o t a l stu dy p o p u la ti o n comprised 394 t r a n s f e r s t u d e n t s .
Six
major r e s e a r c h q u e s t i o n s were examined pro vid in g demographic and cog­
n i t i v e in fo r m at io n as well as i d e n t i f y i n g f a c t o r s r e l a t i n g to p r e d i c t ­
ing p e r s i s t e n c e and Montana S t a t e U n i v e r s i t y grade p o i n t av er ag e .
The
t o t a l p o p u la ti o n was d i v id e d i n t o two s u b p opula ti ons f o r a n a l y s i s of
the data.
The e n g in e e r in g s u bpo pula ti on t o t a l e d 316 s t u d e n t s and the
technology s u bp op ula ti on inc lud ed 78 s t u d e n t s .
All da ta were obta ine d
from th e i n d i v i d u a l s t u d e n t s ' f i l e s i n . t h e College o f Engineering
Dean's O f f i c e and in th e Montana S t a t e U n i v e r s i t y O f f i c e o f the
Registrar.
S e l e c t e d demographic and c o g n i t i v e c h a r a c t e r i s t i c s o f the t r a n s ­
f e r s t u d e n t s were examined and summarized.
A t o t a l o f twelve m u l t i p l e
r e g r e s s i o n models were developed to p r e d i c t p e r s i s t e n c e / n o n - p e r s i s t e n c e
and MSU grade p o i n t av erage .
Eight o f t h e s e , c a l l e d r e s t r i c t e d models,
were developed by s e l e c t i n g only independent v a r i a b l e s which c o n t r i b u 2
te d a minimum i n c r e a s e o f 1% of th e t o t a l R val ues computed in the
ste pw is e r e g r e s s i o n .
Of t h e s e e i g h t , f o u r were chosen f o r f u r t h e r
t e s t i n g through double c r o s s - v a l i d a t i o n proced ures.
145
Conclusions
Conclusion One
Engineering t r a n s f e r s t u d e n t s ' appea r t o possess d i f f e r e n t cog­
n i t i v e c h a r a c t e r i s t i c s than technology t r a n s f e r s t u d e n t s .
s t u d e n t s had h i g h e r mean v a lu es on t h e fo ll ow in g v a r i a b l e s :
Engineering
high
school ra n k , ACT a p t i t u d e t e s t s c o r e s , cumulative grade p o i n t average
from p r i o r i n s t i t u t i o n ( s ) , MSU grade p o i n t average a t end o f s tu dy,
and number o f c r e d i t s acc e pt ed f o r t r a n s f e r .
The p e r s i s t e n c e r a t e
o f e n g i n e e r i n g s t u d e n t s was a l s o h ig h e r than t h a t of technology
students.
In a d d i t i o n , technology s t u d e n t s appear to drop out a t an
e a r l i e r p o i n t in t h e i r c o l l e g e program in comparison with e n gi ne e ri ng
students.
The uniqueness o f th e two groups i s f u r t h e r sup porte d by
t h e d i f f e r e n c e s in th e r e g r e s s i o n models f o r technology and e n g i n e e r ­
ing s t u d e n t s .
Although a small group o f independent v a r i a b l e s
accounted f o r most o f t h e v a r ia n c e in t h e p r e d i c t i o n o f p e r s i s t e n c e
and MSU grade p o i n t average f o r both groups; the o r d e r , magnitude, and
p o s i t i v e o r n e g a t i v e si g n f o r each v a r i a b l e in th e e q u a ti o n were
d i f f e r e n t f o r each model.
In summary, t h e r e i s s tr ong ev ide nc e to
s u p p o rt t h e f a c t t h a t e ng in e e ri n g and te chnology t r a n s f e r s t u d e n t s a re
each a unique group with d i f f e r e n t background c h a r a c t e r i s t i c s , r e t e n ­
t i o n r a t e s and MSU grade p o i n t av erage.
146
Conclusion Two
The stu dy s u p p o r ts two noteworthy g e n e r a l i z a t i o n s which appeared
in th e review of l i t e r a t u r e in Chapter Two.
F irst, aptitude te st
s c o r e s , high school ra n k , and o t h e r c o g n i t i v e v a r i a b l e s provid e s tr ong
i n d i c a t i o n s o f upper le v el s t u d e n t p e r s i s t e n c e and academic p e r f o r ­
mance.
Second, n o n - c o g n i t i v e measures o f t e n known as demographic
c h a r a c t e r i s t i c s a r e l e s s p o w e rf u l, l e s s c o n s i s t e n t p r e d i c t o r s of
p e r s i s t e n c e and academic performance.
An examination o f th e v a r i a b l e s
in cl ude d in th e r e s t r i c t e d r e g r e s s i o n models confirm t h e s e two
generalizations.
The fo ll o w i n g summarizes the importance o f s p e c i f i c
independent v a r i a b l e s .
Cumulative Grade Po in t Average from P r i o r I n s t i t u t i o n s .
This
v a r i a b l e was s e l e c t e d by t h e st ep w is e procedure in a l l r e s t r i c t e d
models f o r both t h e p r e d i c t i o n o f p e r s i s t e n c e and MSU grade p o in t
averag e.
In t h r e e of th e e i g h t r e s t r i c t e d models t h i s v a r i a b l e
accounted f o r the most v a r i a n c e in t h e dependent v a r i a b l e .
c r e a s e in R2 ranged from .02 to .19.
The i n ­
The l i t e r a t u r e s u p p o rt s t h i s
f i n d i n g t h a t grades from th e p r i o r i n s t i t u t i o n a r e th e s t r o n g e s t p re ­
d i c t o r o f academic su cc e ss a t a f o u r y e a r i n s t i t u t i o n .
(Nolan and
H a l l , 1978; Holahan and Kel ley , 1978; N i c k e l s , 1972; Wray and
Leischuck, 1971; Snyder and Blocker, 1970; Mince, 1968; Lee and
Suslow, 1966; Sims, 1966; Knoell and Medsker, 1964).
147
High School Rank a t G ra d u at io n .
th e complete r e g r e s s i o n models.
This v a r i a b l e was u t i l i z e d in
Since t h i s in fo r m at in was not a v a i l ­
a b le f o r a l l s t u d e n t s , i t was not in c lu de d in th e b a s i c models.
High
school rank appeared in t h r e e out of f o u r r e s t r i c t e d complete models.
Thus, i t was an im p o r ta n t v a r i a b l e in th e p r e d i c t i o n of both p e r s i s ­
te n c e and grade p o i n t av er ag e .
In one model i t accounted f o r .23 of
the v a r i a n c e in th e dependent v a r i a b l e .
This v a r i a b l e appea rs to be
more s i g n i f i c a n t in the p r e d i c t i o n of p e r s i s t e n c e and grade p o i n t
average f o r e n g in e e r in g s t u d e n t s than f o r technology s t u d e n t s .
As .
one would e x p e c t , th e h ig h e r th e high school rank, the h ig h e r the
va lu e in th e r e g r e s s i o n e q u a ti o n .
This f i n d i n g i s a l s o supported
by Elk ins and Luetkemeyer (1974), LeBold and Shell (1980), and Lamb
and Durio (1980).
ACT A p tit ud e T e s t S c o r e s .
This stu dy found t h a t ACT composite
and ACT q u a n t i t a t i v e s c o r e s a r e s tr o n g p r e d i c t o r s of p e r s i s t e n c e and
grade p o i n t av erage.
In a l l fo u r r e s t r i c t e d complete m od e ls , ACT
2
composite or ACT q u a n t i t a t i v e s co r es accounted f o r an i n c r e a s e in R
ranging from .01 to .14.
The ACT composite sc or es appeared to be
th e s t r o n g e r of th e two f o r p r e d i c t i n g p e r s i s t e n c e and grade p o in t
average f o r e n g in e e ri n g s t u d e n t s .
The ACT q u a n t i t a t i v e s c o r e was
th e s t r o n g e r f o r p r e d i c t i n g p e r s i s t e n c e and grade p o i n t average f o r
technology s t u d e n t s .
The s t r e n g t h o f a p t i t u d e t e s t s c o r e s in the
p r e d i c t i o n o f academic performance i s widely supported by previous
148
research.
(LeBold and S h e l l , 1980; Lamb and Durio, 1980; Holahan and
K e lle y, 1978; Co mp tro lle r General of t h e United S t a t e s , 1976; Pedrini
and P e d r i n i , 1974; Snyder and Blocker, 1970; Van Erdewyk, 1968; and
Sims, 1966).
A ss o c ia te d Degree and Number of C r e d i t s Accepted f o r T r a n s f e r .
The l i t e r a t u r e widely s u p p o rt s th e g e n e r a l i z a t i o n t h a t th os e who com­
p l e t e an A s s o c ia te Degree a n d / o r th o s e who accumulate a l a r g e number of
c r e d i t s w i l l perform, b e t t e r ac a de m ic a lly than th o s e who do not have a
degree o r th o s e who have not completed as many c o l l e g e c r e d i t s .
(Thomas, 1972; Wray and Leischuck, 1971; Snyder and Blo ck er, 1970;
Zimmerman, 1967; Sp a ng le r, 1966; Lee ans Suslow, 1966; McKenzie, 1965;
Knoell and Medsker, 1964b; and K l i t z k e , 1961).
The f i n d i n g s o f t h i s
study did not s t r o n g l y s u p p o rt the l i t e r a t u r e .
A s s o c ia te Degree, as
an independent v a r i a b l e , only appeared in one o f the r e s t r i c t e d models
f o r e n g in e e r in g s t u d e n t s , and in t h a t case only accounted f o r .01 of
t h e v a r i a n c e in th e dependent v a r i a b l e .
With r e s p e c t to th e number o f c r e d i t s acce pte d f o r t r a n s f e r , t h i s
v a r i a b l e did account f o r .01 o f th e v a r i a n c e in one o f th e f o u r r e ­
s t r i c t e d models f o r e n g i n e e r i n g s t u d e n t s .
However, the number of
c r e d i t s appeared to be a s t r o n g p r e d i c t o r f o r p e r s i s t e n c e and grade
p o i n t av erage f o r te chnology s t u d e n t s .
In t h r e e of the f o u r r e s t r i c ­
te d models f o r te chnology s t u d e n t s , NCRED was th e b e s t p r e d i c t o r and
2
in one model accounted f o r an R i n c r e a s e of .20.
149
Gender.
Whether females perform b e t t e r than men in v a ri ous c u r ­
r i c u l a i s a q u e s t i o n t h a t i s answered d i f f e r e n t l y by d i f f e r e n t s t u d i e s .
On th e lo c al l e v e l , Dulniak (.1981) in h is study of MSU freshmen drop­
o u t s , found t h a t women had a h ig h e r p e r s i s t e n c e r a t e than men.
This
f i n d i n g i s not su ppor te d by t h i s r e s e a r c h on e n g in ee ri ng t r a n s f e r s t u ­
dents.
Female t r a n s f e r s t u d e n t s in e n g in e e r in g programs had a higher
n o n - p e r s i s t e n c e r a t e than men (67% v s . 45%).
The independent v a r i a b l e
SEX appeared as a n e g a t i v e c o n t r i b u t i o n t o the eq uat io n in e n gi ne e ri ng
models p r e d i c t i n g p e r s i s t e n c e .
Sex was not an imp ort an t independent
v a r i a b l e itj) th e e n g in e e r in g models p r e d i c t i n g grade p o i n t average.
Since t h e r e were no females in th e te chnology s u b p o p u l a t i o n , the
importance o f sex as an independent v a r i a b l e f o r t h a t group was not
ascertained.
Other Independent V a r i a b l e s .
The fol lo wi ng independent v a r i a b l e s
were l e s s powerful and l e s s c o n s i s t e n t p r e d i c t o r s o f p e r s i s t e n c e and
grade p o i n t average:
a.
Mathematics - For technology s t u d e n t s , having no mathematics
was a marginal p r e d i c t o r o f n o n - p e r s i s t e n c e .
Engineering s tu d e n t s
who completed c a l c u l u s and technology s t u d e n t s who completed t r i g o ­
nometry appeared to have a s t r o n g e r p r o b a b i l i t y of being a p e r s i s t e r
and were p r e d i c t e d to o b t a i n a h ig h e r MSU grade p o i n t av erage.
b.
I n s t i t u t i o n a l Type - Communit y / j u n i o r c o l l e g e as an indepen­
150
de nt v a r i a b l e appeared in the r e s t r i c t e d technology r e g r e s s i o n models.
Stu de nts from a community/junior c o l l e g e were p r e d i c t e d to have lower
grades and a lower p r o b a b i l i t y o f p e r s i s t e n c e than th o s e from f o u r y e a r
c o l l e g e s and u n i v e r s i t i e s .
c.
C i t i z e n s h i p - This v a r i a b l e was found to be a s i g n i f i c a n t
p r e d i c t o r of p e r s i s t e n c e and grade p o i n t average f o r e n g in e e ri n g s t u ­
dents.
St ude nts from c o u n t r i e s o t h e r than th e USA or Canada were
found to have a hig h e r n o n - p e r s i s t e n c e r a t e and lower MSU grade p o i n t
averag e.
d.
Q u a rt e r of Entry - In th e o r i g i n a l r e g r e s s i o n models the
q u a r t e r of "entry appeared to account f o r a minimal amount o f v a r i a n c e .
In t h r e e of th e f o u r r e s t r i c t e d models f o r technology s t u d e n t s , Spring
q u a r t e r was a n e g a t i v e v a lu e .
Thus, th o s e technology s t u d e n t s e n t e r ­
ing during the Spring q u a r t e r had a s t r o n g e r p r o b a b i l i t y o f non­
p e r s i s t e n c e and lower grades than th os e e n t e r i n g during Winter and
Autumn q u a r t e r s .
Conclusion Three
The p e r s i s t e n c e r a t e f o r e n g in e e ri n g s t u d e n t s was h ig h e r than
th e p e r s i s t e n c e r a t e f o r technology s t u d e n t s .
P e r s i s t e n c e f o r a two
y e a r pe ri o d was 52.5% f o r e n g in e e ri n g s t u d e n t s and 46.2% f o r t e c h n o l ­
ogy s t u d e n t s .
P e r s i s t e n c e f o r a one y e a r pe rio d was 70% f o r e n g i n e e r ­
ing s t u d e n t s and 47.7% f o r te chnology s t u d e n t s .
151
. In comparison, th e College of Engineering Dean's O f f i c e has
determined t h a t th e one y e a r p e r s i s t e n c e r a t e f o r e n g i n e e r i n g and
te chnology freshmen s t u d e n t s combined has v a r i e d from 71% to 75%
dur ing t h e p a s t f o u r y e a r s .
Dulniak (1981) found th e MSU freshmen
p e r s i s t e n c e r a t e to be 68.6% f o r t h e i r f i r s t y e a r in c o l l e g e .
Conclusion Four
This study found t h a t t h e r e g r e s s i o n models p r e d i c t i n g MSU grade
2
p o i n t average r e s u l t e d in hig h e r R ' s than models p r e d i c t i n g p e r s i s ­
tence/non-persistence.
In every ca se where both MSU grade p o i n t
aver age and p e r s i s t e n c e were p r e d i c t e d using th e same independent
2
v a r i a b l e s and th e same s u b p o p u la t io n , t h e R ' s of the o r i g i n a l and
r e s t r i c t e d models were h ig h e r f o r t h e p r e d i c t i o n o f MSU grade p o in t
av erage than f o r t h e p r e d i c t i o n of p e r s i s t e n c e / n o n - p e r s i s t e n c e .
Conclusion Five
In every i n s t a n c e , r e g a r d l e s s o f t h e dependent v a r i a b l e , the
complete r e g r e s s i o n model accounted f o r a g r e a t e r p e r c e n t o f the
v a r i a n c e in comparison with th e b a s i c r e g r e s s i o n model.
t h e R2 improvement was in excess o f .10.
In most cases
The a d d i t i o n o f ACT a p t i t u d e
t e s t s c o r e s and high school rank i n c r e a s e d the e f f e c t i v e n e s s o f the
r e g r e s s i o n model.
Although t h i s s t a t e m e n t i s widely su pported by th e
l i t e r a t u r e , th e r e s e a r c h e r p r e s e n t s t h e fo ll ow in g c a v e a t .
/
152
The b a s i c models were developed on e n g in e e r in g and technology sub
p o p u l a t i o n s number 316 and 78 r e s p e c t i v e l y .
The complete models were
developed u t i l i z i n g 123 e n g in e e r in g s t u d e n t s and 33 technology s t u ­
dents.
The d i f f e r e n c e in t h e samples upon which th e r e g r e s s i o n mod2
e l s were developed may, in p a r t , account f o r th e improvement in R
o f t h e complete model over th e b a s i c model.
For example, th e group
o f 33 techn olo gy t r a n s f e r s t u d e n t s who su bmitted ACT s c o r e s may simply
r e p r e s e n t a d i f f e r e n t type o f s t u d e n t than th o s e who did not submit
scores.
I f so t h e s e 33 s t u d e n t s may be i n h e r e n t l y more p r e d i c t a b l e ,
2
and t h e .IQ R . improvement may be caused p a r t l y by t h i s non-random
s e l e c t i o n o f t h i s s ubp opu la ti on r a t h e r than being caused e n t i r e l y by
th e use o f a d d i t i o n a l d a t a .
Conclusion Six
In s p i t e of th e a d d i t i o n a l number o f c r e d i t s o b ta in e d from p r i o r
i n s t i t u t i o n ( s ) , i n t e r n a t i o n a l s t u d e n t s (Canadian excluded) had a
lower p e r s i s t e n c e r a t e and lower academic performance than American
and Canadian t r a n s f e r s t u d e n t s .
The t o t a l e ng in e e ri n g and technology
p e r s i s t e n c e r a t e f o r i n t e r n a t i o n a l s t u d e n t s was 34.5% in comparison
to USA/Canadian s t u d e n t s p e r s i s t e n c e r a t e of 52.3%.
International
s t u d e n t s ex pe ri en c e d a c o n s i d e r a b l e drop in grade p o i n t average a f t e r
t r a n s f e r r i n g i n t o e n g in e e r in g and techn olo gy programs.
The drop in
GPA, as measured by comparing mean v a l u e s , was .57 f o r i n t e r n a t i o n a l
153
s t u d e n t s and .23 f o r USA/Canadian s t u d e n t s .
Recommendations
Recommendation One
The College should e s t a b l i s h a r e t e n t i o n committee to examine
th e q u a l i f i c a t i o n s and performance o f s t u d e n t s applying f o r e ntr a nce
to th e Montana S t a t e U n i v e r s i t y College o f Engineering and to e v a l u a t e
t h e a b i l i t y of th e College to s u c c e s s f u l l y e duca te th e t r a n s f e r s t u d e n t
population.
This committee should examine p o l i c i e s and procedures a s ­
s o c i a t e d with the s t u d e n t s ' admission and th e e v a l u a t i o n o f t r a n s f e r
t r a n s c r i p t s , and should e s t a b l i s h e f f e c t i v e s t r a t e g i e s to improve the
le v el of p e r s i s t e n c e in e n g in e e ri n g .
Recommendation Two
With th e approximate lo s s of 50% of the t r a n s f e r s t u d e n t s over
two y e a r s , the College should c o n s i d e r u t i l i z i n g a p r e d i c t i v e measure
such as one of th ose developed by t h i s r e s e a r c h .
This p r e d i c t i v e
measure, whether p r e d i c t i n g p e r s i s t e n c e or grade p o i n t a v e r a g e , should
be used t o g e t h e r with c u r r e n t admission in fo rm at io n to i d e n t i f y or
" f l a g " s t u d e n t s who have marginal academic p o t e n t i a l but who could
succeed with s p e c i a l a d v i s i n g or o t h e r a s s i s t a n c e .
154
Recommendation Three
This stu dy g a th e r e d d a ta on ap pro xi ma te ly 400 s t u d e n t s .
Since
Autumn q u a r t e r 1979 a t l e a s t 300 a d d i t i o n a l t r a n s f e r s t u d e n t s have
e n t e r e d th e College o f Eng in eer ing .
The r e s e a r c h e r s u g g e s ts t h a t d a ta
on new t r a n s f e r s t u d e n t s should be added to th e p r e s e n t e n g in e e ri n g
t r a n s f e r s t u d e n t base in o r d e r to improve th e p r e d i c t i v e models which
have been form ulated he re .
The i n c r e a s e in th e t o t a l number in each
o f t h e s u b p o p u la t io n s should improve t h e s t a t i s t i c a l and p r a c t i c a l
s i g n i f i c a n c e o f t h e r e g r e s s i o n models.
Recommendation Four
The p r e s e n t admission s ta n d a r d s o f th e U n i v e r s i t y allow f o r the
a c c e pt an c e o f s t u d e n t s who show l i t t l e academic p o t e n t i a l f o r e n g i ­
n e e r in g .
The College should fo rm ul at e s t r i c t admission s ta n d a r d s
to enhance th e academic q u a l i t y of th e e n g in e e r in g and technology
programs.
An a c c e p t a b l e r e t e n t i o n r a t e i s s tr e n g th e n e d by denying
admission to th o s e who show l i t t l e academic promise.
Recommendation Five
In th e f o r m u la t io n of p o l i c i e s f o r admission of s t u d e n t s who
show academic p o t e n t i a l , th e College should not s o l e l y r e l y upon the
cu mulative grade p o i n t av er ag e .fr om p r i o r i n s t i t u t i o n ( s ) .
The d i v e r ­
s i t y o f i n d i v i d u a l s t u d e n t s and the d i v e r s i t y of t h e i r previo us
academic programs a t any o f s ev e ra l ty pe s o f i n s t i t u t i o n s make i t
.
155
d i f f i c u l t to base admission to a program s o l e l y on grade p o i n t a v e r ­
age.
The r e s e a r c h e r encourages th e College to o b t a i n high school
ra nk, a p t i t u d e t e s t s c o r e s , and measures o f i n t e r e s t in th e e n g i n e e r ­
ing p r o f e s s i o n s from each s t u d e n t p r i o r to accep tanc e i n t o a program.
These da ta coupled with th e a l r e a d y e x i s t i n g da ta base should enhance
th e a c c u r a t e s e l e c t i o n o f s t u d e n t s who w i l l gra dua te from th e Uni­
versity.
Recommendation Six
The College should ta ke an a c t i v e r o l e in th e ed u ca ti o n o f e n g i ­
ne er in g f a c u l t y as to th e s i m i l a r i t i e s and d i f f e r e n c e s o f t r a n s f e r
and freshmen s t u d e n t s .
Most f a c u l t y a l r e a d y have i n t u i t i v e f e e l i n g s
about th e performance and c h a r a c t e r i s t i c s of freshmen and t r a n s f e r
students.
The d a ta provided by t h i s s t u d y , in a d d i t i o n to the data
on f i l e in the Engineering Dean's O f f i c e , could enhance the under­
s ta n d i n g o f t h e s e two s t u d e n t groups.
This has s p e c i a l re le v a n c e to
th e pr oper advisement o f s t u d e n t s .
Recommendation Seven
The College should c o n s i d e r th e r e v i s i o n of admission r e q u i r e ­
ments f o r i n t e r n a t i o n a l s t u d e n t s .
In t h e event t h a t i t i s too
d i f f i c u l t to determine th e academic le v e l of fo r e i g n t r a n s f e r s t u ­
d e n t s , because o f v a r io u s f o r e i g n r e p o r t i n g systems and i n s t i t u t i o n a l
t y p e s , th e r e s e a r c h e r encourages t h e use. o f a p t i t u d e t e s t s .
Pre­
156
s e n t l y , the TOEFL exam, which only a s s e s s e s English competency, does
not provide a c c u r a t e s c r e e n in g of f o r e i g n s t u d e n t s .
Recommendation Eight
The College should e s t a b l i s h a method to i d e n t i f y s t u d e n t s who
t r a n s f e r i n t o the U n i v e r s i t y .
Presently, a tr a n s f e r student is
c l a s s i f i e d as such upon i n i t i a l e n t r y i n t o th e U n i v e r s i t y .
However,
once a t r a n s f e r s t u d e n t has completed one q u a r t e r a t th e U n i v e r s i t y ,
t h e i n d i v i d u a l i s c l a s s i f i e d as a c o n ti n u in g s t u d e n t .
uniqueness o f being a t r a n s f e r s t u d e n t i s l o s t .
Hence, the
Since t r a n s f e r s t u ­
de nts r e p r e s e n t a s i z a b l e p e r c e n t o f th e s t u d e n t body and s i n c e t h e i r
academic performance may be q u e s t i o n a b l e , the College should e s t a b l i s h
a mechanism to i d e n t i f y t r a n s f e r s t u d e n t s and study t h e i r progr ess
th rou gho ut t h e i r academic program.
LITERATURE CITED
158
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Z
APPENDICES
APPENDIX A
CONVERSION OF SAT TEST SCORES TO ACT EQUIVALENTS
DEVELOPED BY
LANGSTON AND WATKINS
APPENDIX A-I
ACT Math Score E q uiv al e nt s f o r SAT Q u a n t i t a t i v e Scores
(Pearson r = .849)
ACT:M Scores
E q u i v a le n t
SAT Q u a n t i t a t i v e Scores
—
ACT:M Scores
I
2
3
4
5
220 - 230
240
250
260
270
21
22
23
24
25
6
7
8
9
10
280
290 - 300
310
320
330
26
27
. 28
29
30
11
12
13
14
15
340
350
360
370
380
31
32
33
34.
35
16
17
18
19
20
390
400 - 410
420
430 - 440
450
36
•
E qui va le nt
SAT Q u a n t i t a t i v e Scores
460 - 470
480
490
510
500
530
520
-
-
540
550
580
610
640
'
(Langston and Watkins, U n i v e r s i t y o f I l l i n o i s , N - 12,014, 1980)
-
660
670
690
720
740
-
760
-
-
-
570
600
630
650
680
710
730
750
800