Access to this full-text is provided by PLOS.
Content available from PLOS ONE
This content is subject to copyright.
RESEARCH ARTICLE
The effects of vocational interest on study
results: Student person – environment fit and
program interest diversity
Stijn SchelfhoutID
1
*, Bart Wille
2
, Lot Fonteyne
3
, Elisabeth Roels
1
, Filip De Fruyt
4
,
Wouter Duyck
1
1Department of Experimental Psychology, Faculty of Psychology and Educational Sciences, Ghent
University, Ghent, Belgium, 2Department of Personnel Management, Work and Organizational Psychology,
Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium, 3Student Counseling
Office, Department of Educational Policy, Campus UFO, Ghent University, Ghent, Belgium, 4Department of
Developmental, Personality and Social Psychology, Faculty of Psychology and Educational Sciences, Ghent
University, Ghent, Belgium
*stijn.schelfhout@ugent.be
Abstract
The extent to which a good person-environment (PE) interest fit between student and study
program leads to better study results in higher education is an ongoing debate wherein the
role of the study program environment has remained inadequately studied. Unanswered
questions include: how diverse study programs are in the interests of their student popula-
tions, and how this program interest diversity influences study results, in comparison to indi-
vidual PE fit? The present study addressed these questions in students (N = 4,635) enrolled
in open-access university education. In such an open access system, students are allowed
to make study choices without prior limitations based on previous achievement or high
stakes testing. Starting from the homogeneity assumption applied to this open access set-
ting, we propose several hypotheses regarding program interest diversity, motivation, stu-
dent-program interest fit, and study results. Furthermore, we applied a method of measuring
interest diversity based on an existing measure of correlational person-environment fit.
Results indicated that interest diversity in an open access study environment was low
across study programs. Results also showed the variance present in program interest diver-
sity was linked to autonomous and controlled motivation in the programs’ student popula-
tions. Finally, program interest diversity better explained study results than individual
student fit with their program of choice. Indeed, program interest diversity explained up to
44% of the variance in the average program’s study results while individual student-program
fit hardly predicted study success at all. Educational policy makers should therefore be
aware of the importance of both interest fit and interest diversity during the process of study
orientation.
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 1 / 26
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Schelfhout S, Wille B, Fonteyne L, Roels
E, De Fruyt F, Duyck W (2019) The effects of
vocational interest on study results: Student person
– environment fit and program interest diversity.
PLoS ONE 14(4): e0214618. https://doi.org/
10.1371/journal.pone.0214618
Editor: Emmanuel Manalo, Kyoto University,
JAPAN
Received: December 20, 2018
Accepted: March 16, 2019
Published: April 4, 2019
Copyright: ©2019 Schelfhout et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data cannot be
shared publicly because of privacy regulations on
student data. Data are available from the Ghent
University Department of Experimental Psychology
Ethics Commission (contact via the SIMON project,
simon@ugent.be) for researchers who meet the
criteria for access to confidential data.
Funding: The study is part of a larger project called
SIMON. This project is funded by Ghent University.
The funders had no role in study design, data
Introduction
Literature has shown that students who choose a study program that fits their vocational inter-
ests, arguably have better study results and have a better chance of finishing higher education
in a timely fashion [1]. Such study results are usually investigated in settings without much
emphasis on the educational access policy. However, these policies show large variety: they can
be open or restricted, based on past secondary school performance or tests like the SAT (scho-
lastic aptitude test). This person-environment fit (PE fit) research line focuses heavily on the
student side of the PE relationship, while leaving the environment of study programs underex-
plored [2]. As a consequence, it is still unknown how diverse study environments actually are
in terms of student vocational interest, and whether this varies as a function of access policy.
For instance, does an academic bachelor in psychology only attract students that are pro-
foundly interested in psychology or do students who enroll in psychology display quite some
diversity in their interest pattern? And how does interest diversity in a psychology program
compare to the diversity in other programs like mathematics or economics? As literature is
still oblivious of study program interest diversity, we are also unsure how this program diver-
sity directly influences study results. In an open access educational environment, the present
study uses homogeneity theory and the properties of vocational interest to pose and answer
three main research questions. How diverse are student interests within and between study
programs? Does this interest diversity directly influence average study results in study pro-
grams? And finally, how does this effect of program interest diversity on study results compare
to the effects of individual PE fit?
The properties of vocational interest
Vocational interest is typically defined as the liking or disliking of certain activities or environ-
ments, usually represented by a concise number of dimensions [3]. Vocational interest also
has a number of key characteristics which are important when exploring the relation between
student interest and study results [4,5].
First, vocational interest has predictive power towards study program choice [6]. Up to 70%
of the students (depending on the methodology used) chooses a study program that can be
predicted through vocational interest [7,8]. As such, a vocational interest model should be able
to compare student interest profile and study program environment on a commensurate scale
to explore how good students match their study choice [9]. For the present study, we have
used the RIASEC model by Holland [10].
Next, student interests always have an object or an environment [11]. As an example, a stu-
dent can be interested in solving equations or working in an engineering environment. As a
consequence, questionnaires targeting higher education students should focus on appropriate
items like activities or (future) occupations. For this specific study, we have used the SIMON-I
questionnaire [12], but our rationale could be easily applied to any other RIASEC-based
instrument.
Literature also reports that vocational interests are stable constructs [13]. This opens up
research possibilities towards prospective studies. As an example, in the present study we have
combined students’ interests and study results spanning an entire academic year.
Finally, interests are also linked to motivation [14]. Performing actions which the student is
highly interested in, like solving equations, can create a study environment that motivates the
student towards obtaining his or her degree of choice through facilitating study behavior.
Because motivation and vocational interest are linked together, we expect that average pro-
gram motivation scores of student populations have also been linked to the interest diversity
in student populations. As such, we have also assessed both controlled and autonomous
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 2 / 26
collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
motivation in the present study. Autonomous behavior is performed out of interest or personal
importance, while controlled behavior is driven by external demands [15]. For instance, a stu-
dent can get good grades because he likes studying the courses in his program of choice
(autonomous motivation) or because he wants to adhere to his parents’ expectations (con-
trolled motivation).
RIASEC theory
The Holland RIASEC model has a long standing tradition as one of the most influential mod-
els of vocational interest [16,2,17]. The RIASEC theory’s basic principle is very straightfor-
ward. Persons (students) and environments (study programs) are represented on the same
clockwise hexagon, containing six dimensions or scales, using the same RIASEC code: Realis-
tic,Investigative,Artistic,Social,Enterprising, and Conventional. By comparing the profiles of a
student and a study program, PE fit indicates how well a (future) student’s interests match a
specific study program. The current iteration of the RIASEC model still instigates a lot of
research and applications specifically targeting (higher) education. As an example, the current
study builds on the SIMON-I RIASEC questionnaire that was recently introduced as a study
orientation tool, tailored for the transition towards higher education [12]. SIMON-I measures
the interests of (future) higher education students in order to guide the student towards the
best fitting study programs. As such, SIMON-I builds on the object characteristic of vocational
interest by including items that describe professions (66) and activities (87), tied to one of the
six scales. As an example, Geneticist? (I) or Starting up an enterprise? (E) are two of a total of
153 very short items to which the (future) student had to answer with yes or no. The scores on
all dimensions can be recalibrated to a score on a 0–100 scale for each dimension, effectively
rendering a student RIASEC profile, e.g. R: 80, I: 70, A:60, S:50, E:55, C:59.
Beside these person profiles, there are a number of ways to construct RIASEC study pro-
gram environment profiles. One of these methods is built on a common principle that the
environment is determined through the people that are in it [18–20]. As vocational interest is a
stable construct, students having a good fit with their study programs in year one will likely
still have a good fit when they finish their study program. By using successful and persistent
students as representatives or incumbents of a study program, this incumbent method can
empirically generate environment profiles using the profiles of said students [21,10]. Due to
this empirical base, the incumbent method is immune to rater bias, in contrast to profile gen-
eration based on expert ratings. Practically, the RIASEC profile of the study program is con-
structed by averaging out the scores on the RIASEC dimensions using the incumbent student
RIASEC profiles of that program [21]. The relation or (dis)similarity between the individual
student and the study program environment is depicted through a measure of PE fit.
Measure of PE Fit: Pearson’s product moment correlation coefficient
Correlational fit is a pattern based similarity measure, that correlates commensurate student
and study program RIASEC dimension profile patterns by using the Pearson’s product-
moment correlation coefficient. As such, correlational fit indicates how well the student’s inter-
ests fit with his or her study program of choice. For instance, fitting a study program profile
(M) R:80, I:70, A:60, S:50, E:60, C:70 to a student profile (S) R:80, I:60, A:60, S:50, E:50, C:60
results in a correlation of .87 (or vice versa). A high correlation means a better PE fit. Literature
has shown that correlational fit also has predictive value towards study results, especially for
first year students [21,9].
Although the predictive validity of PE fit between students and their study programs on
study results has repeatedly been established, the positive influence of this PE fit on study
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 3 / 26
results remains somewhat limited, varying from a very small to medium effect at best [22–25].
Indeed, some studies report at best a very modest effect of PE fit on study results, while other
studies report correlations of up to .32 between study results and PE fit, implying an explained
variance of about 10% [26]. As a consequence, a debate is still ongoing whether or not the mag-
nitude of these results are in line with the high(er) expectations of theoretical vocational inter-
est models like the Holland RIASEC hexagon [10].
However, when starting a new study related to concepts like PE fit, researchers are usually
confronted with a selection bias problem. Access to study programs, especially in the United
States, is often already restricted and linked to performance criteria like passing a specific
exam or satisfying grade point average (GPA) requirements. As such, these access restrictions
yield a pre-selected sample, possibly not only biased in terms of intellectual competence, but
potentially also biased towards PE fit between students and programs. It is therefore a possibil-
ity that the variation in reported PE fit effects reflects–at least partly–the variation in educa-
tional access policies. The present study provides a unique opportunity to enhance our
knowledge on the range of PE fit effects by using a large student data set from an open access
higher education environment. It will be very interesting to observe whether the positive effect
of PE fit on study results generalizes to, or is even stronger under conditions of free study
choice, while also examining the influence of program interest diversity on average program
results.
Interest diversity theory
The age old homogeneity assumption states that people who display similarity in characteris-
tics such as vocational interests tend to lean towards similar environments, with literature
spanning more than half a century [27,28,20]. As a consequence, environments like profes-
sional occupations are inhabited with individuals that have similar patterns of vocational inter-
ests. This internal similarity in the population of an environment also seems to hold in higher
education when observing the vocational interest of students. As an example, students that
show a good fit between their RIASEC profile and a specific study program are more likely to
enroll for such a program than students who do not fit the program [11,6]. Starting from this
internal similarity in the population of study programs, we can make a number of predictions.
These predictions form the basis for the present study’s research questions.
To start, study programs should display a low interest diversity in their student population.
Indeed, individual students who have a high PE fit with a study program are more like to enroll
for this specific study program than students who lack a high PE fit. This mechanism will be
even more explicit if there are no further requirements (like GPA or exams) to enter a program
as is the case in our present study with open access. As a consequence, the average PE fit in our
open access study program population should be quite high when compared to programs with
a more restricted access. Due to this high individual PE fit across students, each study program
should display high internal similarity, or a low diversity, regarding the vocational interest of
its student population. In order to be able and test our predictions of open access versus
restricted access, the present study also features data from a small control group (same univer-
sity) that had to pass an entry exam to enter the Medicine or Dentistry programs.
Next, a high interest in a specific program is just one, autonomous motivation why students
enroll for that program. Students can also opt to act on exterior, more controlled motives to
make their choice, like pleasing their parents. As these students are less interested in the spe-
cific program, they will have a lower PE fit with the chosen program. Some study programs
might be more prone to such externally controlled study choice than others; these programs
will attract more students with a lower PE fit and thus show a higher interest diversity in their
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 4 / 26
student population. For the present study, we therefore predict that program interest diversity
will vary over programs. We also predict this variance will be linked to autonomous and con-
trolled motivation.
Finally, the internal similarity of the environment will exert an influence on the behavior of
the population, with different programs rewarding different interest patterns [29]. In case of
high internal similarity, the behavior of the students will be less determined by their interest
pattern but all the more by the study environment. For instance, Tracey and colleagues used
the profile deviation around the mean of the six RIASEC dimensions of a study program as a
measure of so called constraint [9]. Smaller deviations represent higher constraint and higher
internal similarity. Relevant for this study, results showed a cross-level interaction between
program constraint and student interest pattern. Indeed, high program constraint reduced the
effect of individual PE fit on study outcomes like GPA and persistence. However, their study
did not specifically focus on the direct influence of the environment on study results at the
program level, nor on the comparison between the effects of the environment (internal simi-
larity) and the effects of the individual (student PE fit). Our study aims to add to this knowl-
edge. Considering the already hypothesized low levels of program diversity (high internal
similarity), we predict a strong effect of the environment for the present study.
Measuring interest diversity
The debate regarding the best fitting (dichotomous) measure for studying homogeneity or
internal similarity of environments is still undecided. However, Bradley-Geist and Landis pro-
vide evidence that first and foremost, the measures used should depend on the study’s major
hypotheses [30]. For the present study, the emphasis lies on study program interest diversity
and its influence on study results in higher education. As such, interest diversity should be
assessable within study programs. Moreover, a program interest diversity assessment should
also allow for comparisons across programs so we can investigate the possible influence on
study results. For instance, the average deviation measure for testing environment homogene-
ity reflects the average difference within one group between the individual scores and the
mean or median group score [31,32]. Regarding vocational interest, we are faced with a con-
ceptual problem of measuring such an average deviation on a single scale. Indeed, the most
dominant theories use multiple scales to measure vocational interest. For example, our Hol-
land RIASEC theory uses a 6 dimensional hexagon to depict the vocational interest of individ-
uals and their vocational environments like study programs (see above). As it stands,
correlational fit already provides us with a one-scale, parsimonious and continuous PE fit mea-
sure that indicates the degree of fit of an individual’s interests with his or her environment.
Furthermore, a PE fit correlation is already known to be predictive of first year study results
[9]. Apart from indicating a degree of fit, the correlational fit measure also indicates how far a
student’s interests deviate from their study program interest profile. By averaging out these
deviance measurements over students of a specific program, a continuous measure of program
environment interest diversity can be obtained. Such a measure allows us to investigate
whether study program populations have a high internal similarity in vocational interest, while
still allowing for variance in this hypothesized low program interest diversity.
Present study
In the present prospective study, we have derived and investigated a number of research ques-
tions from the theoretical predictions made in the introduction regarding program interest
diversity in an open access environment. For our first question, we have investigated how
diverse study programs actually are in the vocational interest of their student population.
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 5 / 26
Students with similar interest patterns should be attracted to similar programs, especially in an
open access environment. Consequentially, the fit between students and their program of
choice should be high. As such, we hypothesize program interest diversity will be low. We also
expect that this general low interest diversity will still show variance over the range of pro-
grams, linked to the motivation of their student populations. Indeed, some students will
choose the program autonomously, because they are interested in the program itself, while
others will take into account exterior motives like parental approval and are less interested in
the program itself. We expect some programs could be more prone to such exterior motives of
student choice. Therefore, the student population of these programs should show more diverse
interest patterns. In sum, we hypothesize that a student population with high autonomous
motivation is related to a low program interest diversity, while a population with high con-
trolled motivation is linked to a higher program interest diversity.
For our second question, we have explored if and how program interest diversity has an
effect on (average) program study results. On an individual level, literature already showed us
that a higher PE fit will lead to better study results [9]. One could make the seemingly plausible
hypothesis that the program level interest diversity variable derived from this PE fit would
show a similar effect, i.e. a lower program interest diversity (high average PE fit of the students)
would lead to better results. However, we are wary of making that hypothesis, as we are aware
of the ecological fallacy phenomenon that warns researchers to not naively assume that indi-
vidual effects automatically generalize towards a higher (program) level [33]. Moreover, Smart
and colleagues already pointed out that different study programs could reward different inter-
est patterns [29]. As such, we have taken a conservative approach by pitting three hypotheses
against each other. We hypothesize rising program interest diversity could show a linear posi-
tive effect, a linear negative effect or a curvilinear mixed effect on average program study
results. Our findings will also serve as a baseline to integrate possible program interest diversity
effects into our third and final research question.
For our final research question, we have compared the found effects of program interest
diversity on study results to the effect of individual PE fit. As we are still unsure about the
nature of the program interest diversity effect, we can only make predictions regarding indi-
vidual PE fit. From theory discussed in the introduction, we hypothesize that the individual
effects of PE fit on study results will be small, as program environments with low interest
diversity (high similarity) are expected to limit the effect of individual PE fit.
Materials and methods
Data and procedure
All students (N
0
= 6,772, 55% female) starting an academic bachelor at a large Western Euro-
pean university (ranked in the Shanghai top 100, see also www.shanghairanking.com) across
eleven faculties and 41 study programs, with an open access policy (anyone who completed
secondary education) were invited to participate in a long term assessment to enhance study
choice and study results. Though the programs have an open access structure, they are also
strictly stratified. In other words, within one program, everyone has to take the same set of
study courses during the first year. The study programs are listed in Table 1. Students were
asked to participate in the present study during the starting week (end of September 2016) of
their curriculum via their lectors, email and the online learning platform [12]. Response rate
was 71% (N= 4,827, 57% female). Participating students immediately filled out online interest
and motivation questionnaires (about five to ten minutes long, see measures for a detailed
description). All students were also subject to periodic evaluation systems (once for each
course) split up into two sessions (January and May/June 2017) and a retry session (August/
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 6 / 26
September 2017) if they failed the test on the first attempt. At the end of the academic year
(September 2017), the results from the SIMON-I test were cross-referenced to the exam
results. A total of 4,422 students (92% of N) at least participated in some form of evaluation.
The remaining 8% dropped out, prior to any form of evaluation. As literature shows that PE fit
also has an influence on perseverance, we took a conservative approach and included the drop-
outs in our study [21]. Students from the study programs Medicine and Dentistry (n= 192,
Table 1. Study programs included in the present study.
Number Programs
1 Psychology
2 Communication Sciences
3 Mathematics
4 Educational Sciences
5 Political Sciences
6 Law
7 Sociology
8 Criminological Sciences
9 Speech Language and Hearing Sciences
10 Physical Education and Movement Sciences
11 Philosophy
12 Linguistics and Literature
13 East European Languages and Cultures
14 History
15 Oriental Languages and Cultures
16 Moral Sciences
17 Art History
18 Archaeology
19 African Studies
20 Veterinary Medicine
21 Physical Therapy and Motor Rehabilitation
22 Pharmaceutical Sciences
23 Bioscience Engineering
24 Economics
25 Biomedical Sciences
26 Engineering–Architecture
27 Engineering
28 Business Economics
29 Bioscience Engineering Technology
30 Engineering Technology
31 Applied Language Studies
32 Biochemistry and Biotechnology
33 Biology
34 Chemistry
35 Physics and Astronomy
36 Geology
37 Geography and Geomatics
38 Computer Sciences
39 Public Administration and Management
Medicine and Dentistry (excluded)
https://doi.org/10.1371/journal.pone.0214618.t001
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 7 / 26
response rate 86%) already had to pass an entry exam to be allowed to start, in contrast to the
students who picked any of the other 39 study programs. As such a high stakes access mecha-
nism (possibly) influences the homogeneity inside a such a program, we have decided to again
act conservatively and exclude both programs from our study. However, we have pooled the
students from both programs (program RIASEC profiles correlate 0.99 and have about 50%
common courses) as a control group for our first question regarding program interest diversity.
The final pool of student participants (SP) was N= 4,635, spread out across 39 study programs.
Besides this first year student pool, we established the profiles of the study programs using
interest questionnaire responses of 6,572 senior 3
rd
and 4
th
year students spread out over the
same 39 study programs featuring in Table 1. These students all met the conditions of aca-
demic success and perseverance. The procedure of making the program E-profiles was identi-
cal to the procedure used by Allen and Robbins: the RIASEC scores of all students in each
study program were averaged out across all six dimensions to obtain the E-profile for each
study program [21].
Ethics statement
The Ethical Commission of the Faculty of Psychology and Educational Sciences at Ghent Univer-
sity has granted approval to "Constructing Simon:a tool for evaluating personal capacity to
choose a post-secondary major that maximally suits the potential." of which the present study is
an integral part, with reference 2016/82/Elisabeth Roels.
This study was carried out in accordance with the recommendations of the Ethical Com-
mission of the Faculty of Psychology and Educational Sciences at Ghent University. All sub-
jects gave online informed consent in accordance with the Declaration of Helsinki. The
protocol was approved by the Ethical Commission of the Faculty of Psychology and Educa-
tional Sciences at Ghent University.
Considering the nature and size of the online project and the highly restricted access to the
information of the participants, an explicit written informed consent was deemed unworkable
and unnecessary and was replaced with an explicit online informed consent of which a transla-
tion is provided below.
Prior to filling out the online surveys, students had to explicitly agree to the following state-
ments: (translation)
Processing of personal information. The provided personal information can be linked to
study results in higher education. This information can be used for scientific purposes and for
counseling in higher education. Personal information will be stored in a separate data file.
Under no condition will this personal information be communicated to third parties. Ghent
University is responsible for the processing of the information. Participants are not obliged to
provide this information, they always have access to this information if they so desire and have
the right to correct or adjust this information through SIMON@ugent.be.
Copyright. This test and the internet files attached to it are protected through copyright.
Test results are allowed to be printed for personal use. Copying, adjusting, translating, editing
or changing the whole or parts of this test or site, in any way, shape or form, mechanically or
other, are strictly forbidden, unless explicit written permission was obtained.
Liability. Though the content of this test was subject of extreme scrutiny, no liability can
be accepted for possible errors. (for the student) I agree to these terms: yes no
Measures
Vocational interest. The SIMON-I questionnaire (see also S1 Table in Supporting Infor-
mation) was presented to all students to measure the six RIASEC dimensions of the Holland
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 8 / 26
model as described in the introduction [12]. Derived from the specific item scores, all student
participants received a RIASEC profile, with each dimension scoring between 0 (low) and 100
(high). For the present study, the RIASEC scales showed a reliability of .92, .87, .91, .92, .93
and .90 respectively, measured through a Cronbach’s alpha [34]. To confirm the validity of the
RIASEC model for the present study’s data, we performed a confirmatory factor analysis on
the circular structure of the RIASEC dimensions using the CirCe package in R [35–36]. Fig 1
shows the resulting circular structure for the present data sample. Confirmatory factor analysis
(CFA) included several measures of model fit (Standardized Root Mean square Residual =
0.051; Normed Fit Index = 0.97; Comparative Fit Index = 0.97; Goodness of Fit Index = 0.99), all
pointing towards a good to excellent circular fit. For an overview on interpretation of these
indices, we refer to the exhaustive listings provided by Kenny [37]. Additionally, we also veri-
fied the circular RIASEC order and structure by conducting a randomization test of hypothe-
sized order relations (RTOR) using the RANDALL package [38–40]. Results of this RTOR
analysis revealed a correspondence index of .92, while a circular fit of the data also reached sig-
nificance, p= .02. For a full discussion on the RANDALL function and RTOR analyses, we
refer to [38–40]. In sum, the theorized circular fit for our RIASEC data was confirmed by both
CFA and RTOR analyses.
Student PE interest fit and study program interest diversity. Next, the student PE fit
between the vocational interest of the student and her/his study program was established using
the correlational fit measure. To calculate the correlational fit measure (shape resemblance
between the hexagonal pattern of student and study program profile) for each student, each
student’s RIASEC profile was correlated with his or her study program RIASEC profile based
on the profiles of successful and persistent students. As this correlational fit measure also rep-
resents a deviance, the measure (on a scale of -1 to 1) was then rescaled to an easy-to-interpret
interest deviance between 0 and 1, with D COR = 1 - (correlational fit + 1) / 2. As an example,
Fig 1. Verified study specific circumplex structure of the RIASEC model. R = realistic dimension, I = investigative dimension, A = artistic
dimension, S = social dimension, E = enterprising dimension, C = conventional dimension.
https://doi.org/10.1371/journal.pone.0214618.g001
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 9 / 26
a student with a correlational fit measurement of 0.76 would rescale to D COR = 0.12, indicat-
ing the student’s RIASEC profile deviates 12% in regard to the profile of his or her study
program.
A program interest diversity measure should indicate how deviant students’ interests are in
a given study program of choice. By averaging out D COR across students in a program, we
can obtain a measure of interest diversity AD COR for each study program, that represent
how much students diverge on average from the program profile. As an example, a study pro-
gram with an AD COR of 0.23 indicates that the RIASEC profiles of students within the pro-
gram deviate (on average) 23% in regard to the program profile.
Autonomous and controlled motivation. The Self-Regulation Questionnaire was pre-
sented to all students to measure their autonomous (8 items) and controlled (8 items) motiva-
tion [41]. For the present study, the autonomous and controlled motivation subscales showed
a reliability of .86 and .87 respectively, measured through a Cronbach’s alpha [34]. A factor
analyses on both the autonomous and controlled motivation subscales revealed the expected
two factor structure, explaining 52% of the variance. Items from the autonomous subscale
showed high loadings on the autonomous factor (M= 0.70, ranging from 0.50 to 0.83) but not
on the controlled factor. In contrast, items from the controlled subscale showed high loadings
on the controlled factor (M= 0.73, ranging from 0.70 to 0.77).
Study results. GPA is a widely known and used measure of study success [42]. However,
as Graham already pointed out, the validity and reliability of such a measure cannot be merely
assumed, which forms a widespread problem in literature concerning study success [43].
Indeed, validity and reliability are function of both sample and measure, rendering GPA
results from past samples insufficient [44]. To ensure reliability and validity of the GPA mea-
sure, not only towards research, but especially towards the eventual degree of students, the
present study’s featuring open access university has installed several precautions embedded in
the teaching and grading procedures for each study program. Considering the open access, it
is important to note that all programs are strictly stratified in the first year. Because all students
take the same courses, GPA is fully comparable within a program. For means of validity,
attainment levels for all programs are actively protected by national and regional law. In other
words, what students need to know in theory and practice is officially decreed and controlled
by the government. For means of reliability, each program consists of 60 study points, divided
over several courses (about ten courses for each program), taught by different professors and
lectors to avoid rater bias. The exams for each program’s course are spread across a number of
(non) periodical methods including but not limited to written exams, multiple choice exams
(usually corrected automatically by use of computer), oral exams and essay writing to avoid
methodical bias. In case of failing an exam, a student always has the right to resit the exam and
even an appeal if irregularities (like dubious questions) were established. Both the resit and the
appeal actively counter low reliability of exams. Importantly, we have included a PASS mea-
sure in the present study. In order to earn a PASS for his or her first year (and all other years),
a student has to pass all courses of a study program by obtaining a course score of at least 10/
20 for each course. This passing measure is an excellent countermeasure against possible
overly optimistic exam marking in some courses of a program, as a student has to pass all
courses of that program to obtain a PASS. Indeed, if a student earns a PASS, the results from
all courses unanimously indicate the student has mastered the learning material for the first
year. On the other hand, if there are courses in a program that are too difficult in comparison
to the other courses, to the extent that no one would succeed the program, the discrepancy
between the PASS rate (which would be zero) and the average GPA of the program should
alert us to problematic (too strict) grading for those specific courses. For these specific reasons,
we have included both PASS and GPA as measures of study results. GPA indicates the global
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 10 / 26
result of the student in his first year study program on a scale from 0 to 1,000. PASS was
assessed dichotomously (1/0) at the first year student level, and averaged out at the program
level as a PASS rate, across students. If these study result measures are reliable and valid, both
measures should correlate highly and should display similar results at the program level.
Despite these precautions, we still deem it possible (although unlikely) that a student’s GPA
could be biased due to specific program choice and subsequent deviant grading. As such, we
have taken a conservative approach by obtaining an estimate of the maximal hypothetical bias
for GPA by calculating the intra class correlation coefficient through a multilevel model. For
our purposes, this coefficient indicates how much of the variance in individual GPA can be
attributed to the program level. Practically, the coefficient provides us with an estimate of the
maximum bias due to possible different grading standards in different programs. However,
stronger students could also systematically choose more difficult programs. This confound
cannot be disentangled within the scope of the present study. As a results, we have included
the intra class correlation coefficient as an absolute maximum of hypothetical bias in a stu-
dent’s GPA due to the program of his/her choice.
Analyses
To avoid unwanted bias of (potentially) skewed distributions of the correlational data on
outcomes and subsequent conclusions, we performed all statistical testing on standardized
scores by taking the z—scores of the D COR measures (by subtracting the grand mean and
dividing by the grand standard deviation of the full data set) as the base measure. Averaging
out these
z—scores over programs renders the standardized equivalent of the AD COR measures.
To address our first research question, we inspected the variance of interest diversity over
programs. The interest diversity of open access programs was tested against our control group
that had to pass an entry exam. We also regress interest diversity (AD COR) on autonomous
and controlled motivation to test whether both types of motivation in the student population
are indeed linked to the variance in program interest diversity. To address our second research
question regarding the influence of interest diversity on study results, we have regressed the
average study program GPA and PASS on program interest diversity. As we are pitting three
hypotheses against each other, we have considered both linear and curvilinear relations. To
address our final research question regarding the comparison between the individual PE fit
effect and the environmental program interest diversity effect, we have constructed two hierar-
chical models, a linear one for GPA and a logistic one for PASS In these models, we have inves-
tigated the effect of individual PE fit on study results, the effect of program interest diversity
on study results and the cross-level interaction between individual PE fit and study program
diversity. All analyses were conducted using R(Studio), SPSS and HLM software.
Results
Table 2 shows the program summary containing the reference number, the scores on the R, I,
A, S, E and C dimensions, the average student GPA, the student PASS rates, the AD COR
interest diversity, the scores on autonomous (AMOT) and controlled (CMOT) motivation, the
response rate (RR) on the SIMON-I questionnaire and finally the number of students (N). The
correlation of GPA and PASS rates amounted to 0.80. Closer inspection revealed there was
one program, Geology (36) with 0% PASS rate. Because this find could prove problematic, we
considered the average GPA (370.29) of the program, which was very low (compared to the
other programs). The correlation between GPA and PASS did not change by excluding Geol-
ogy (36). There does not seem to be a huge discrepancy between the GPA and PASS results for
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 11 / 26
Table 2. Descriptive statistics for all study programs.
Number R I A S E C GPA PASS AD COR AMOT CMOT N RR
1 6.36 32.06 35.97 58.17 27.10 10.45 521.28 0.49 0.12 15.61 7.94 424 0.82
2 8.52 22.68 56.67 36.72 55.67 14.97 414.40 0.31 0.11 14.7 8.06 121 0.95
3 19.65 37.31 15.89 14.31 18.51 27.47 512.23 0.50 0.26 15.27 8.35 26 0.67
4 4.08 24.21 33.78 74.95 25.05 13.76 560.56 0.67 0.06 15.16 8.20 106 0.88
5 9.50 25.53 38.85 37.69 53.31 20.15 418.59 0.34 0.18 15.02 8.48 86 0.90
6 7.56 25.10 32.02 37.93 55.87 35.51 407.08 0.27 0.16 15.39 8.13 298 0.61
7 6.15 30.46 42.92 50.85 37.97 11.83 390.17 0.23 0.13 15.99 8.37 35 0.85
8 9.87 27.75 27.88 46.99 30.12 19.19 461.54 0.33 0.21 15.1 8.21 149 0.60
9 6.13 34.34 37.18 65.38 23.28 12.43 633.17 0.60 0.09 15.09 8.70 60 0.91
10 8.18 29.01 20.26 37.59 25.12 13.46 436.94 0.35 0.22 14.84 8.00 49 0.70
11 7.13 34.41 56.02 45.76 31.17 17.36 408.00 0.50 0.13 14.75 7.00 6 0.29
12 6.86 22.71 59.15 38.71 21.47 7.43 484.55 0.45 0.10 15.39 8.77 161 0.83
13 4.97 24.24 57.69 51.44 29.44 7.51 626.00 0.71 0.06 16.64 6.86 7 0.41
14 11.66 28.55 49.22 31.18 28.81 12.94 453.04 0.33 0.20 15.59 8.26 75 0.64
15 6.25 20.00 43.34 33.79 19.57 8.48 356.73 0.18 0.14 14.42 8.89 33 0.45
16 8.73 41.63 60.18 52.38 27.20 6.55 372.50 0.50 0.12 16.5 7.83 6 0.30
17 10.32 24.33 68.82 34.06 20.10 5.70 339.78 0.19 0.08 16.55 7.90 36 0.49
18 28.71 43.49 43.64 17.83 15.25 10.27 435.29 0.29 0.16 14.26 8.24 17 0.53
19 7.32 31.56 56.32 61.76 25.00 7.97 577.17 0.83 0.05 17.06 5.42 6 0.46
20 18.08 45.68 22.78 31.77 18.06 13.69 428.40 0.34 0.18 15.49 7.36 179 0.66
21 13.79 40.32 21.61 46.63 17.87 11.62 430.13 0.27 0.15 14.81 7.77 361 0.75
22 15.17 49.44 23.51 36.26 20.64 17.74 524.65 0.49 0.17 15.38 8.35 210 0.78
23 32.60 59.13 20.36 24.49 27.10 20.09 479.13 0.41 0.18 15.35 8.93 202 0.81
24 22.03 25.20 21.19 22.84 66.53 54.16 524.12 0.45 0.12 14.74 8.96 360 0.69
25 16.03 54.35 26.30 34.54 20.76 14.99 503.03 0.39 0.14 15.25 7.98 129 0.78
26 46.44 30.82 56.25 20.56 31.20 15.70 486.63 0.48 0.18 15.06 7.86 62 0.43
27 51.95 40.67 22.22 13.61 32.86 22.57 539.51 0.52 0.19 15.19 8.70 222 0.65
28 14.10 16.84 23.73 27.22 66.83 47.84 476.72 0.39 0.12 14.12 8.96 330 0.61
29 33.53 45.37 19.20 20.84 22.78 19.14 524.52 0.45 0.23 13.93 9.28 75 0.82
30 59.70 32.15 22.71 13.39 26.77 19.42 421.68 0.29 0.18 14.09 8.20 349 0.84
31 6.22 16.44 47.33 35.99 30.01 10.94 433.15 0.33 0.14 14.83 8.75 133 0.67
32 17.12 54.26 22.00 22.97 13.05 11.32 463.81 0.37 0.13 14.9 8.12 67 0.66
33 23.24 50.75 28.77 27.18 13.18 6.94 390.70 0.36 0.12 15.06 7.12 50 0.68
34 22.94 47.74 17.11 21.77 18.10 17.70 479.05 0.39 0.18 14.61 7.53 38 0.66
35 33.58 46.81 23.97 11.56 15.11 11.03 400.60 0.40 0.15 15.47 7.71 47 0.68
36 33.90 46.25 31.00 20.11 15.59 10.42 370.29 0.00 0.17 14.54 8.48 14 0.67
37 38.45 39.52 20.57 29.66 13.64 15.69 522.15 0.46 0.26 14.88 10.79 13 0.59
38 31.50 26.76 24.90 7.58 20.09 15.64 459.51 0.51 0.24 14.44 7.73 43 0.66
39 8.74 18.79 25.18 42.15 71.35 42.78 476.58 0.44 0.11 15.01 8.39 50 0.70
�17.51 47.68 23.41 44.68 26.40 14.81 702.37 0.93 0.17 16.38 8.36 192 0.86
Note. Number: see Table 1, R = average program scores (on 100) on realistic dimension, I = average program scores (on 100) on investigative dimension, A = average
program scores (on 100) on artistic dimension, S = average program scores (on 100) on social dimension, E = average program scores (on 100) on enterprising
dimension, C = average program scores (on 100) on conventional dimension, GPA = average program grade point average, PASS = average program pass-rate, AD
COR = program interest diversity, AMOT = average program autonomous motivation, CMOT = average controlled program motivation, N = number of students in the
program, RR = program response rate.
https://doi.org/10.1371/journal.pone.0214618.t002
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 12 / 26
Geology (36). Because 0% PASS rate is still a huge outlier, we decided to act conservatively and
pay special attention to the Geology (36) result in specific PASS analyses.
Important to note, the intra class correlation coefficient for GPA amounted to 6%. This
means that only 6% of the variance in GPA can be attributed to the program level. In other
words, the individual GPA is determined for 94% based on personal achievement. As such, the
maximum possible bias of GPA due to non-equivalent grading can be estimated at 6%.
Research question 1: Interest diversity of study programs
Fig 2 shows the spread of interest diversity (AD COR) across programs (M= .15 ; SD = .05).
This interest diversity also corresponds to a rather impressive average PE fit correlation of .70
between the RIASEC profiles of students and their program of choice. We thus clearly observe
a concentration of programs at the lower end of the diversity continuum (due to the high aver-
age PE fit), ranging from .05 (African Studies) to .26 (Mathematics). This concentration at the
lower end also means that 74% of the interest diversity continuum at the higher end remains
unused. To formally test if an open access environment indeed results in more interest diver-
sity regarding study programs, we compared the interest diversity of 39 study programs with
open access to the control group with restricted access. A two-sided one sample t-test indeed
revealed a significant difference, t(38) = -4.27, p<.001, with a somewhat large effect size of
Cohen’s d= 0.69. We therefore conclude that the evidence shown confirms our hypothesis.
Study programs in an open access environment indeed display a low interest diversity regard-
ing their student population.
For the second part of our first research question, Fig 2 also shows interest diversity does
still display quite some variance across programs at the lower end of the continuum. For
instance, the interest diversity in Mathematics is about five times larger than the diversity in
African studies. To test whether this variance is linked to motivation, we regressed interest
diversity (AD COR) on (autonomous and controlled) motivation. The omnibus test proved to
be significant, F(2, 36) = 6.86, p= .003, with an explained variance of 28%. Standardized
regression coefficients (β
1
= -0.33, β
2
= 0.28, for autonomous and controlled motivation
respectively) indicate a negative relation between program interest diversity and average
autonomous motivation in the program student population, and a positive relation between
interest diversity and average controlled motivation in the program student population. Closer
graphical inspection (Figs 3and 4) of the individual effects also reveal the effects are especially
present at (relatively) very high and very low levels of controlled and autonomous motivation.
These findings confirm our hypothesis that program interest diversity is indeed related to
motivation in an open access environment. Programs with low interest diversity have a student
Fig 2. Interest diversity program spread across the 0–1 continuum. The X-axis displays the 39 study programs featuring
in this study. Y-axis displays the program interest diversity expressed through an AD COR value.
https://doi.org/10.1371/journal.pone.0214618.g002
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 13 / 26
population with high autonomous motivation and vice versa. Programs with high diversity are
linked to student populations with a higher controlled motivation while programs with low
diversity are linked to populations with a lower controlled motivation.
Research question 2: Effects of interest program diversity
To examine the possible direct effect of interest diversity in study programs on average pro-
gram study results, we conducted two (curvi-) linear regressions of both average program
GPA and PASS rates on interest diversity (AD COR). Figs 5and 6show the regressions of
average program study results on program interest diversity. We obtained similar results for
both measures of study results. Indeed, the linear regressions of study results on interest diver-
sity were not significant F(1,37) = 0.63, p= .43 and F(1,36) = 3.01, p= .09 for GPA and PASS
Fig 3. The linear regression of interest diversity on autonomous motivation. Interest Diversity is measured using
AD COR (based on D COR z–scores), with AMOT = autonomous motivation. The data points (dotted study
programs) are labeled analogous to Table 1.
https://doi.org/10.1371/journal.pone.0214618.g003
Fig 4. The linear regression of interest diversity on controlled motivation. Interest Diversity is measured using AD
COR (based on D COR z–scores), with CMOT = controlled motivation. The data points (dotted study programs) are
labeled analogous to Table 1.
https://doi.org/10.1371/journal.pone.0214618.g004
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 14 / 26
respectively. The curvilinear regressions of study results on interest diversity however, were
significant with high levels of explained variance, F(2,36) = 7.19, p= .002, R
2
= .29 and F(2,35)
= 13.84, p<.001, R
2
= .44 for GPA and PASS respectively. Adding the Geology (36) results to
the PASS regression rendered similar results. These curvilinear findings provide evidence that
rising program interest diversity has a mixed effect on the average study results of the pro-
gram’s student population. Different programs do seem to reward different interest patterns
(Smart, 2000). More specifically, a very low diversity in a small number of programs is associ-
ated with very high average study results. However, for the majority of the programs that had a
Fig 5. The (curvi-) linear regression of study program average results (GPA) on study program interest diversity.
Interest Diversity is measured using AD COR (based on D COR z–scores), with GPA = average grade point average for
each study program. The linear regression is depicted as a full line, the quadratic regression is depicted as an
interrupted line. The data points (dotted study programs) are labeled analogous to Table 1.
https://doi.org/10.1371/journal.pone.0214618.g005
Fig 6. The (curvi-) linear regression of average study program results (PASS) on study program interest diversity.
Interest Diversity is measured using AD COR (based on D COR z–scores), with PASS = average pass rate for each
study program. The linear regression is depicted as a full line, the quadratic regression is depicted as an interrupted
line. The data points (dotted study programs) are labeled analogous to Table 1.With a pass rate of 0%, Geology (36)
was considered an outlier and was removed from analyses.
https://doi.org/10.1371/journal.pone.0214618.g006
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 15 / 26
higher diversity to begin with, we observe that study results tend to improve as program inter-
est diversity rises.
As we were curious about the nature of these curvilinear effects, we ran further post hoc
analyses. Closer inspection of Figs 5and 6revealed that the left, descending part of the curvi-
linear relation is largely caused by the study results and interest diversity of four programs:
Educational Sciences,Speech Language and Hearing Sciences,East European Languages and
Cultures and African Studies. We decided to compare the six dimension RIASEC profiles of
these four programs to each other and to the other programs. Table 3 shows the correlation of
the four RIASEC program patterns. The high correlations indicate these programs have very
similar interest patterns. When comparing the RIASEC interest profile of these four programs
to the other programs in the present study, these specific programs have relatively very high
scores on the social S dimension (rankings 1, 2, 3 and 6 out of 39), and very low scores on the
realistic R dimension (rankings 30, 37, 38 and 39 out of 39). For these specific programs, a low
interest diversity is tied to better study results, as is shown by the correlation between their
interest diversity and their average study results: AD COR correlates -.47 with GPA and -.88
with PASS.
When considering the other programs of the present study, we repeated the regression of
study results (GPA and PASS) on interest diversity by excluding the results from these four
programs. Fig 7 now clearly shows a linear regression best explains the relation between pro-
gram interest diversity and average study results compared to a curvilinear one. Statistically,
Table 3. Comparison (correlations) RIASEC profiles of educational sciences,speech language and hearing sciences,
East European Languages and Cultures and African Studies.
Number Program 4 9 13 19
4 Educational Sciences — .97 .79 .87
9 Speech Language and Hearing Sciences — .83 .93
13 East European Languages and Cultures — .97
19 African Studies —
https://doi.org/10.1371/journal.pone.0214618.t003
Fig 7. Post hoc analysis: The (Curvi-) linear regression of average study program results (GPA) on study program
interest diversity. The data points (study programs) are labeled analogous to Table 1 and are identical to Fig 4, with
omission of points 4, 9, 13 and 19 that were discussed separately.
https://doi.org/10.1371/journal.pone.0214618.g007
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 16 / 26
the linear regression of GPA on interest diversity reached significance, F(1, 33) = 6.13, p= .02,
R
2
= .16, while the curvilinear one no longer did, F(2, 32) = 3.10, p= .06.
Fig 8 shows a somewhat similar pattern. Although the linear regression of PASS on interest
diversity did not reach significance, F(1, 32) = 2.84, p= .10, interest diversity still showed a
strong linear trend towards an effect by explaining 8% of the variance in program study results.
The curvilinear regression did not reach significance, F(2, 31) = 1.72, p= .20. The addition of
Geology (36) to the regression rendered similar results, although the explained linear variance
only amounted to 5%.
In sum, these results confirm our hypothesis that interest diversity has a direct, but mixed
effect on average study program results in an open access environment. Different programs
seem to reward different interest patterns. In general, a higher interest diversity of the student
population has a positive effect on average program study results. However, this effect seems
to reverse for very specific programs displaying a very high social dimension and a very low
realistic dimension. These programs reach high average study results if the student population
shows very low levels of interest diversity.
Research question 3: Individual student PE fit and program interest
diversity
To compare the effect of program interest diversity on study results to the effect of individual
PE fit, we have performed multilevel analyses of student (level 1) and program (level 2) effects
on study results. As GPA is a linear variable and PASS is a dichotomous variable, we con-
structed a linear and a logistic multilevel model. Effect sizes for the different effects at the indi-
vidual level, program level or both levels combined (full model) were calculated by combining
the variance components (GPA model) and model deviance statistics (PASS model) into a
pseudo R
2
.
Table 4 shows the final version of the GPA model (see also [45] for a discussion on multipli-
cative interaction analysis). Though significant, the fit of the individual student with this study
environment (measured through the D COR measure) only explained 0.6% of the variance in
Fig 8. Post hoc Analysis: The (curvi)linear regression of average study program results (PASS) on study program
interest diversity. The data points (study programs) are labeled analogous to Table 1 and are identical to Fig 5, with
omission of points 4, 9, 13 and 19 that were discussed separately. With a pass rate of 0%, Geology (36) was considered
an outlier and was removed from analyses.
https://doi.org/10.1371/journal.pone.0214618.g008
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 17 / 26
study results at the specific individual level and the general full GPA model. In contrast, pro-
gram interest diversity (AD COR) explained 17% of the variance in average study results at the
program level and 1% of the variance in the full model.
As hypothesized regarding our third question, our GPA multilevel model thus indicates the
influence of the individual’s PE fit on individual study results can indeed be considered low.
However, at the same time, these GPA model results also reveal that interest diversity over pro-
grams explains much more variance in average study results in that program compared to the
individual level.
We also tested the addition of a cross-level interaction (through a random slope for PE fit)
between interest diversity (AD COR) at the program level and PE-fit (D COR) at the individ-
ual level. The chi-squared test returned a non-significant result, χ
2
(38) = 45.26, p= 0.20. This
Table 4. Multilevel GPA model.
A
Multilevel GPA Model
Level-1 Model
GPA
ij
=β
0j
+β
1j
�D COR
ij
+ r
ij
Level-2 Model
β
0j
=γ
00
+γ
01
�AD COR
j
+γ
02
�AD COR
j2
+ u
0j
β
1j
=γ
10
+ u
1j
Mixed Model
GPA
ij
=γ
00
+γ
01
�AD COR
j
+γ
02
�AD COR
j2
+γ
10
�D COR
ij
+ u
0j
+ r
ij
B
Final estimation of fixed effects Coefficient Standard Error t-ratio appr. d.f.p-value
Fixed Effect
For INTRCPT1, β
0
INTRCPT2, γ
00
446.22 10.12 44.07 36 <.001
AD COR,γ
01
-28.73 31.63 -0.91 36 0.37
AD COR
2
,γ
02
178.33 55.06 3.24 36 0.003
For D COR slope, β
1
INTRCPT2, γ
10
-18.03 3.54 -5.09 4595 <.001
C
Final estimation of variance components Standard Deviation Variance Component d.f.χ2p-value
Random Effect
INTRCPT1, u
0
48.66 2367.93 36 259.14 <.001
level-1, r213.16 45440.33 — — —
Note. GPA = grade point average, D COR = student PE interest fit, AD COR = program interest diversity, appr. d.f. = approximated degrees of freedom. Model
construction was conducted as follows. We first determined the amount of variance in study results (GPA) generated through both levels by using the intercept-only
model. Level 1 (individual level) generated 94%, while level 2 (program level) generated the remaining 6% of variance in study results measured through GPA. Explained
variance of the different effects was calculated using comparisons to the intercept-only model. Before making the actual models, we tested the possible influence of study
program SIMON-I response rate and population (number of students) by adding them to the intercept model. Both tests were not significant, t(36) = 1.14, p= .26 and t
(36) = 0.41, p= .69 respectively. Both group variables were thus removed from the intercept model. The D COR predictor was added to the model as a program centered
variable, removing the variance between programs. This variance between programs was then added through the curvilinear effect of AD COR from research question 2
completing the model. Note that the linear term AD COR renders a non-significant result, while the quadratic term did reach significance. Though the absence or
presence of the linear term does not change the curvilinear nature of the interest diversity effect, we have decided to keep the linear term as a part of the model due to
the multiplicative interaction between the student and the program level.
https://doi.org/10.1371/journal.pone.0214618.t004
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 18 / 26
result indicates the individual PE fit does not interact with the program interest diversity. In
other words, there is no different individual effect of PE fit on study results depending on the
study program environment.
Table 5 shows the final PASS model. The results are largely analogous to those from the
GPA model. The deviance statistic from the full models (containing predictors) compared to
the intercept only model revealed that the predictor models were significant. However, the
models showed very low pseudo R
2
(around 0.1%), for both individual PE fit and program
interest diversity when considering full model deviance (all levels combined). PE fit only
reached a pseudo R
2
of about 0.1% at the individual level, while the pseudo R
2
for interest
diversity did reach 37% at the program level.
As hypothesized regarding our third question, our PASS multilevel model thus indicates
the influence of the individual’s PE fit on individual study results can indeed be considered
low. In contrast to the GPA model, both individual PE fit and program interest diversity dis-
played very low levels of explained variance when considering full model deviance. Analogous
to the GPA model, PASS model results also reveal that interest diversity in the program
explains much more variance in typical passing rates for that program, across students.
Table 5. Multilevel PASS model.
A
Multilevel PASS model
Level-1 Model
Prob (PASS
ij
= 1|βj) = ϕ
ij
log[ϕ
ij
/(1 - ϕ
ij
)] = η
ij
η
ij
=β0
j
+β1
j
�(D COR
ij
)
Level-2 Model
β
0j
=γ
00
+γ
01
�AD COR
j
+γ
02
�AD COR
j2
+ u
0j
β
1j
=γ
10
+ u
1j
Mixed Model
η
ij
=γ
00
+γ
01
�AD COR
j
+γ
02
�AD COR
j2
+γ
10
�D COR
ij
+ u
0j
B
Final estimation of fixed effects Coefficient Standard Error t-ratio appr. d.f.p-value
Fixed Effect
For INTRCPT1, β
0
INTRCPT2, γ
00
-0.38 0.07 -5.44 36 <.001
AD COR,γ
01
-0.4 0.23 -1.715 36 0.1
AD COR
2
,γ
02
1.91 0.48 4.02 36 <.001
For D COR slope, β
1
INTRCPT2, γ
10
-0.15 0.04 -4.1 4595 <.001
C
Final estimation of variance components Standard Deviation Variance Component d.f.χ2p-value
Random Effect
INTRCPT1, u
0
0.33 0.11 36 157.01 <.001
Model Deviance = 14571–5 estimated parameters — — — — —
Note. GPA = grade point average, D COR = student PE interest fit, AD COR = program interest diversity, appr. d.f. = approximated degrees of freedom. Model
construction: analogous to the multilevel GPA model. Program SIMON-I response rate and population again tested non-significant, t(36) = 0.60, p= .55 and t(36) =
-0.46, p= .65 and were removed from the model.
https://doi.org/10.1371/journal.pone.0214618.t005
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 19 / 26
The addition of a random slope for the PE predictor again resulted in a non-significant
result, χ
2
(38) = 35.04, p>.50. Hence, we do not find any evidence for a cross-level interaction
between individual student PE fit and program interest diversity on study results.
In sum, these findings again confirm our hypothesis that the individual effect of PE fit on
study results is small to almost non-existent in an open access environment, while interest
diversity has a profound effect on results at the program level. For the full models, we have
found mixed evidence that program interest diversity is more explanative towards study results
than individual PE fit. Furthermore, we did not find any evidence for an interaction effect
between group level program interest diversity on individual study results.
Discussion
Vocational interest refers to the liking or disliking of certain activities or environments, repre-
sented by a number of base dimensions and characterized by the properties of prediction, con-
textualization, stability and motivation [7,15,8,3,4,11,5,13,6]. Literature has shown that
individual person-environment fit (PE fit) between students and study programs influences
higher education study results [21,1,24,22,26,23,9,25]. These studies however, mostly focus on
the student, while leaving the study environment underdeveloped [2]. As such, we are uncer-
tain how diverse study programs actually are in terms of vocational interest of their student
populations. Moreover, we also do not know whether and how program interest diversity
exerts an influence on study outcomes like grade point average (GPA). Finally, when studying
theoretical concepts like interest diversity or PE fit, admission restrictions to study programs
in higher education, like entry exams or GPA requirements, may imply a selection bias in stu-
dent intake that may influence the effects of PE fit.
The present prospective study set out to remedy these voids in literature. As such, the present
study was conducted in an open access environment, exploring the interactions between indi-
vidual interest PE fit and environment program diversity. Although we derived our hypotheses
from homogeneity theory, we do not consider interest diversity as a mere synonym for homoge-
neity [27,28,20]. In fact, our operationalization of the interest diversity construct is quite unique,
as it reuses measures of PE fit as an indication of how a student deviates from his study program
profile. By averaging out these deviances across a program, a continuous measure of program
interest diversity was obtained, through the use of a very large sample of students. Using this
interest diversity measure, we assessed three research questions. We investigated these questions
in a population of bachelor students starting their academic trajectory at a large Western-Euro-
pean university (Shanghai top 100) across eleven faculties with an open access policy.
During the present study, special care was given to the validity and reliability of predictors
and study result measurement. As indicated by Graham and Harris, reliability and validity are
function of both sample and measure [43,44]. The university where the study took place
already had a number of measures in place to guard the reliability and validity of study results.
Apart from the widely known GPA measure [42], we also added an extra measure of study suc-
cess through the PASS rate. A student only received a PASS if he or she succeeded for all
courses of the program. As explained in the method section, if our study result measures are
reliable, both measures should show a high correlation and both measures should show the
same result pattern. Our analyses indeed confirmed both predictions. Moreover, the intra class
correlation coefficient for GPA amounted to only 6%. This find indicates that the bias in GPA
due to non-equivalent quoting can only amount to a maximum of 6%. To which extent this
percentage is determined by stronger students systematically choosing certain programs or
rater-bias cannot be disentangled within the current study. Still, as a result, a student’s individ-
ual GPA is to a very large degree (at least 94%) determined by his personal achievement and
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 20 / 26
not through the specifics of program followed. As such, we are convinced that we have taken
the necessary precautions to ensure the reliability and validity of our study result measures and
the overall results of the present study.
For our first research question, we investigated how diverse study programs actually are in
the vocational interest of their student population. We hypothesized program interest diversity
would be low, as predicted by the homogeneity assumption. We also expected that this general
low interest diversity would still show variance over the range of programs, linked to the moti-
vation of their student populations. Indeed, some programs are chosen through autonomous
motivation by students who are highly interested in their program of choice. As students are
highly interested in their program of choice, such programs should display a low interest
diversity. Other programs could be more attractive to students who have ulterior motives like
pleasing their parents and should display a higher interest diversity. As students are less intrin-
sically interested in their program of choice, these programs display more variance in student
vocational interest, resulting in a higher interest diversity. Results for our first question indeed
showed that program diversity was low across all study programs, leaving 74% of the higher
end diversity continuum unused. On an individual level this find indicates that in general, the
PE fit between students and their programs is quite high: RIASEC profiles between student
and program correlate .70, on average. Indeed, students predominantly seem to choose a
higher education study program that fits their interests quite well when given the opportunity,
as is the case in an open access environment. Results also showed that the variance in program
interest diversity is related to motivation. Study programs with low interest diversity were
linked to students with relatively higher autonomous motivation, while programs with a
higher interest diversity were linked to students with a higher controlled motivation. This rela-
tion between student motivation and interest diversity indicates that some programs do attract
more students with a higher controlled motivation.
For our second research question, we explored the direct effect of the program interest
diversity on average study results. As different programs could reward different interest pat-
terns, we took a conservative approach and pitted three hypotheses against each other on how
program interest diversity would influence average program study results. The curvilinear
relation between program interest diversity and average study results provided evidence for
our mixed effects hypothesis. Different programs indeed rewarded different interest patterns
[29]. To provide an explanation for this curvilinear effect, we performed a post-hoc analysis.
Results of this analysis showed that in general, larger program interest diversity was linked to
better average study results. In other words, programs with more interest diversity in their stu-
dent population showed better average results. However, some study programs with very spe-
cific interest patterns that scored high on the Social dimension and low on the Realistic
dimension showed an opposite relation: lower program interest diversity in student popula-
tions in such environments was associated with better study results. To improve general study
results, these findings suggest policy makers and institutions in (open access) higher education
should allow for interest diversity in the student population of study programs. At the same
time, policy should also ensure a sufficiently high individual student PE interest fit, as litera-
ture already suggested [21,1,24,22,26,23,9,25]. However, to ensure better study results for very
specific programs (high on the social dimension and low on the practical dimension) like Edu-
cational Sciences, the fit between student and program should indeed be as high as possible,
resulting in a (very) low program interest diversity and a very high individual PE interest fit.
Finally, results also revealed criterion validity for our continuous approach of program interest
diversity: up to 44% of the variance in average study program results can be explained by pro-
gram interest diversity in student populations. As such, our continuous approach of interest
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 21 / 26
diversity represents a valuable addition to the measurement of internal similarity of environ-
ments usually determined through dichotomous test statistics [30–32].
For our final research question we compared the effect of program interest diversity on
study results to the effect of individual PE fit. We hypothesized that due to the low interest pro-
gram diversity (or high internal similarity) the effect of PE fit would be low. We also tested the
cross-level interaction of individual program fit and environmental program diversity. Analy-
ses indicated that the effects at the individual level on study results were very modest at best:
student PE fit only explained up to 0.6% at the individual level. Hence, in an open access
higher education environment, the variance in PE fit between students and their program
barely has a meaningful impact on individual study results. In opposition to these results at the
individual level, program interest diversity explained up to 37% of the variance at program
level, which is a huge contrast to the observed explained variance at the individual level. More-
over, program interest diversity of different study programs did not only influence average
study results, we also obtained partial evidence that this diversity explained more study result
variance in the total multilevel models than the individual indicators of PE fit. As only a small
part of the total variance in study results (up to 6%) was situated at the program level to begin
with, this is no small feat indeed. These findings are analogous to those found in our second
question and provide additional evidence that higher education institutions should indeed
consider program interest diversity when making policy decisions towards student orientation
and admission. As a possible explanation, most students in this open system showed a high PE
fit with their program of choice. In systems were choice is restricted (on the basis of exams or
GPA requirements), students may have to choose for programs that match their interests less
well, and then this (larger variety in) PE fit has a bigger impact on individual study results.
Indeed, earlier research that examined PE fit effects on study results in constrained access sys-
tems typically observed more explained variance [22,10,26,23,24,9,25].
This discussion on the consequences of open access policy illustrates the importance of
studying PE fit effects in a variety of study contexts. As Nauta already indicated, (study) envi-
ronments remain understudied [2]. Entry exams or GPA requirements yield a selection bias in
student intake that will influence the internal similarity in student populations, and therefore
also the effects of the observed PE fit variance. Such contextual effect are likely partly responsi-
ble for the mixed results regarding the influence of PE fit on study results. For the first time in
literature, the present study thus aimed at addressing this problem directly by conducting a PE
fit/interest diversity study in a predefined open access environment, firmly rooted in existing
theory regarding the possible influence of the environment. As theory predicted, the influence
of the environment on outcomes in this open access set up becomes quite influential, while the
individual level almost has no explanative power at all regarding study results. In other words,
the open access environment causes study program interest diversity to have a profound influ-
ence on study results, while severely diminishing the influence of individual student PE fit.
To close the discussion on our third question, the variance in program interest diversity
and student PE fit was limited to the extent the cross level interaction between individual and
environment was not significant. In other words, program interest diversity did not influence
the student PE fit-study results relation: effects of individual PE fit remained low, regardless of
the interest diversity of study programs. These findings in our open access study environment
are at odds with results from internal similarity research from Tracey and colleagues [9]. They
showed that the effects of PE fit on individual study success was indeed constrained by the
study environment. As an explanation, we speculate the open access system leads to such low
interest variance that prevents a cross-level interaction between study program interest diver-
sity and student PE fit.
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 22 / 26
Limitations and future research
The present study is unique in its assessment of PE fit effects in an open access system. It
would be interesting in the future to directly compare study environments with more con-
strained entry restrictions on the exact same measurements, using the exact same analyses. We
speculate that such an approach would show enlarged PE fit effects in more restricted study
programs, while the influence of interest diversity will diminish. The access restrictions could
thus be a crucial factor in explaining the mixed findings in literature regarding PE fit, while
elaborating literature with interest diversity research.
Our conceptualization of interest diversity and its motivational connection could also be used
in organizational and occupational research. We predict that not all work environments will show
the same amount of interest diversity. As access to the work environment works quite differently
in comparison to access to higher education, we can also expect different effects. For instance,
open access to certain jobs (low degree requirements) could result in higher interest diversity.
Indeed, a student in an open access environment picks a certain program because that program is
of particular interest to him or because his parents wants him to study that specific program. An
employee could have other motives to pick a job. Employees who have no (or a low) degree can
decide to work out of financial motives exclusively. Though highly speculative, we think that the
different motivation in work and study contexts will lead to different patterns of interest diversity
for both contexts and could ultimately end up explaining why the strength of the PE fit–study
results relation is so underwhelming in comparison to the theoretical predictions.
Conclusion
In the present study, we have assessed program interest diversity of student populations in
study programs. In an open access environment, interest diversity of student populations in
study programs is low. RIASEC profiles of students and programs correlate .70 on average.
Interestingly, study programs with low interest diversity attract students with relatively higher
autonomous motivation, while programs with a higher interest diversity show higher con-
trolled motivation. Despite overall low diversity, the interest diversity of the program environ-
ment still had a profound effect on the program’s typical study results, while the influence of
individual PE fit seems to be nonexistent or very limited at best. In order to enhance student
study success, the present study has shown policy makers and educational institutions should
focus on a sufficiently high PE fit amongst their student populations, while still allowing for
some study program interest diversity.
Supporting information
S1 Table. SIMON-I questionnaire.
(DOCX)
Acknowledgments
The current manuscript is a direct result of a larger longitudinal project called SIMON (Study
success and Interest MONitor), which aims at dispensing study advice to prospective students
at Ghent University.
Author Contributions
Conceptualization: Stijn Schelfhout, Bart Wille, Lot Fonteyne, Filip De Fruyt, Wouter Duyck.
Data curation: Stijn Schelfhout, Lot Fonteyne.
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 23 / 26
Formal analysis: Stijn Schelfhout.
Funding acquisition: Wouter Duyck.
Investigation: Stijn Schelfhout.
Methodology: Stijn Schelfhout.
Project administration: Stijn Schelfhout, Lot Fonteyne, Wouter Duyck.
Software: Lot Fonteyne.
Supervision: Wouter Duyck.
Validation: Stijn Schelfhout.
Visualization: Stijn Schelfhout.
Writing – original draft: Stijn Schelfhout.
Writing – review & editing: Stijn Schelfhout, Bart Wille, Lot Fonteyne, Elisabeth Roels, Filip
De Fruyt, Wouter Duyck.
References
1. Tracey TJG, Robbins SB. The interest-major congruence and college success relation: A longitudinal
study. J Vocat Behav. 2006; 69(1): 64–89. https://doi.org/10.1016/j.jvb.2005.11.003
2. Nauta MM. The development, evolution, and status of Holland’s theory of vocational personalities:
reflections and future directions for counseling psychology. J Couns Psychol. 2010; 57(1): 11–22.
https://doi.org/10.1037/a0018213 PMID: 21133557
3. Lounsbury JW, Studham RS, Steel RP, Gibson LW, Drost AW. Holland’s vocational theory and person-
ality traits of information technology professionals. In Dwivedi Y, Lal B, Williams M, Schneberger S,
Wade M, editors. Handbook of research on contemporary theoretical models in information systems.
Hershey, PA: IGI Global; 2009. pp. 529–543. https://doi.org/10.4018/978-1-60566-659-4.ch03
4. Rounds JB. Vocational interests: evaluation of structural hypotheses. In Lubinski D, Dawis RV, editors.
Assessing individual differences in human behavior: new concepts, methods, and findings. Palo Alto,
CA: Consulting Psychologists Press; 1995. pp 177–232.
5. Su R, Rounds J, Armstrong PI. Men and things, women and people: A meta-analysis of sex differences
in interests. Psychol Bull. 2009; 135(6): 859–884. https://doi.org/10.1037/a0017364 PMID: 19883140
6. Whitney DR, Predicting from expressed vocational choice: a review. Pers Guid J. 1969; 48(4): 279–
286. https://doi.org/10.1002/j.2164-4918.1969.tb03318.x
7. Burns ST. Validity of person matching in vocational interest inventories. Career Dev Q. 2014; 62(2):
114–127. https://doi.org/10.1002/j.2161-0045.2014.00074.x
8. Donnay DEK. Strong’s legacy and beyond: 70 years of the Strong Interest Inventory. Career Dev Q.
1997; 46(1): 2–22. https://doi.org/10.1002/j.2161-0045.1997.tb00688.x
9. Tracey TJG, Allen J, Robbins SB. Moderation of the relation between person-environment congruence
and academic success: environmental constraint, personal flexibility and method. J Vocat Behav. 2012;
80(1): 38–49. https://doi.org/10.1016/j.jvb.2011.03.005
10. Holland JL. Making vocational choices: A theory of vocational personalities and work environment. 3
rd
ed. Odessa FL: Psychological Assessment Resources; 1997
11. Rounds J, Su R. The Nature and power of interests. Curr Dir Psychol Sci. 2014; 23(2): 98–103. https://
doi.org/10.1177/0963721414522812
12. Fonteyne L, Wille B, Duyck W, De Fruyt F. Exploring vocational and academic fields of study: develop-
ment and validation of the Flemish SIMON Interest Inventory (SIMON-I). Int J Educ Vocat Guid. 2017;
17(2): 233–262. https://doi.org/10.1007/s10775-016-9327-9
13. Swanson JL, Hansen JIC. Stability of vocational interests over 4-year, 8-year and 12-year intervals. J
Vocat Behav. 1988; 33(2): 185–202. https://doi.org/10.1016/0001-8791(88)90055-3
14. Lent RW, Brown SD, Hackett G. Toward a unifying social cognitive theory of career and academic inter-
est, choice, and performance. J Vocat Behav. 1994; 45(1): 79–122. https://doi.org/10.1006/jvbe.1994.
1027
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 24 / 26
15. Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social develop-
ment, and well-being. Am Psychol. 2000; 55(1): 68–78. https://doi.org/10.1037/0003-066X.55.1.68
PMID: 11392867
16. Holland JL. A theory of vocational choice. J Couns Psychol. 1959; 6: 35–45. https://doi.org/10.1037/
h0040767
17. Toomey KD, Levinson EM, Palmer EJ. A test of Holland’s theory of vocational personalities and work
environments. J Employ Couns. 2009; 46(2): 82–93. https://doi.org/10.1002/j.2161-1920.2009.
tb00070.x2
18. Astin AW, Holland JL. The environmental assessment technique–a way to measure college environ-
ments. J Educ Psychol. 1961; 52(6): 308–316. https://doi.org/10.1037/h0040137
19. Linton R. The cultural background of personality. New York: Century; 1945.
20. Schneider B. The people make the place. Pers Psychol. 1987; 40(3): 437–453. https://doi.org/10.1111/
j.1744-6570.1987.tb00609.x
21. Allen J, Robbins S. Effects of interest–major congruence, motivation, and academic performance on
timely degree attainment. J Couns Psychol. 2010; 57(1): 23–35. https://doi.org/10.1037/a0017267
PMID: 21133558
22. Assouline M, Meir EI. Meta-analysis of the relationship between congruence and well-being measures.
J Vocat Behav. 1987; 31(3): 319–332. https://doi.org/10.1016/0001-8791(87)90046-7
23. Spokane AR, Meir EL, Catalano M. Person-environment congruence and Holland’s theory: a review
and reconsideration. J Vocat Behav. 2000; 57(2): 137–187. https://doi.org/10.1006/jvbe.2000.1771
24. Tinsley HEA. The congruence myth: An analysis of the efficacy of the person-environment fit model. J
Vocat Behav. 2000; 56(2): 147–179. https://doi.org/10.1006/jvbe.1999.1727
25. Tsabari O, Tziner A, Meir EI. Updated meta-analysis on the relationship between congruence and satis-
faction. J Career Assess. 2005; 13(2): 216–232. https://doi.org/10.1177/1069072704273165
26. Nye CD, Su R, Rounds J, Drasgow F. Vocational interests and performance. A quantitative summary of
over 60 years of research. Perspect Psychol Sci. 2012; 7(4): 384–403. https://doi.org/10.1177/
1745691612449021 PMID: 26168474
27. Holland JL. The psychology of vocational choice. Waltham, MA: Blaisdell; 1966.
28. King DD, Ott-Holland CJ, Ryan AM, Huang JL, Wadlington PL, Elizondo F. Personality homogeneity in
organizations and occupations: considering similarity sources. J Bus Psychol. 2017; 32(6): 641–653.
https://doi.org/10.1007/s10869-016-9459-4
29. Smart JC, Feldman KA, Ethington CA. Academic disciplines: Holland’s theory and the study of college
students and faculty. Nashville, TN: Vanderbilt University Press; 2000.
30. Bradley-Geist JC, Landis RS. Occupations and organizations: a comparison of alternative statistical
tests. J Bus Psychol. 2012; 27(2): 149–159. https://doi.org/10.1007/s10869-011-9233-6
31. Burke MJ, Dunlap WP. Estimating interrater agreement with the average deviation index: a user’s
guide. Organ Res Methods. 2002; 5(2): 159–172. https://doi.org/10.1177/1094428102005002002
32. Burke MJ, Finkelstein LM, Dusig MS. On average deviation indices for estimating interrater agreement.
Organ Res Methods. 1999; 2(1): 49–68. https://doi.org/10.1177/109442819921004
33. Lubinski D, Humphreys LG. Seeing the forest from the trees: When predicting the behavior or status of
groups, correlate means. Psychol Public Policy Law. 1996; 2(2): 363–376. https://doi.org/10.1037/
1076-8971.2.2.363
34. Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951; 16(3): 297–
334. https://doi.org/10.1007/BF02310555
35. Browne MW. Circumplex models for correlation matrices. Psychometrika. 1992; 57: 469–497. https://
doi.org/10.1007/BF02294416
36. Grassi M, Lucio R, Di Blas L. CircE: an R implementation of Browne’s circular stochastic process
model. Behav Res Methods. 2010; 42(1): 55–73. https://doi.org/10.3758/BRM.42.1.55 PMID:
20160286
37. Kenny DA. 2015 Nov 21. Available from http://davidakenny.net/cm/fit.htm
38. Hubert L, Arabie P. Evaluating order hypotheses within proximity matrices. Psychol Bull. 1987; 102:
172–178.
39. Tracey TJG. RANDALL: a Microsoft FORTRAN program for a randomization test of hypothesized order
relations. Educ Psychol Meas. 1997; 57: 164–168. https://doi.org/10.1177/0013164497057001012
40. Tracey TJG, Rounds J. The spherical representation of vocational interests. J Vocat Behav. 1996; 48
(1): 3–41. https://doi.org/10.1006/jvbe.1996.0002
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 25 / 26
41. Vansteenkiste M, Sierens E, Soenens B, Luyckx K, Lens W. Motivational profiles from a self-determina-
tion perspective: The quality of motivation matters. J Educ Psychol. 2009; 101(3): 671–688. https://doi.
org/10.1037/a0015083
42. Richardson M, Abraham C, Bond R. Psychological correlates of university students’ academic perfor-
mance: A systematic review and meta-analysis. Psychol Bull. 2012; 138(2): 353–387. https://doi.org/
10.1037/a0026838 PMID: 22352812
43. Graham S. Inaugural Editorial for the Journal of Educational Psychology. J Educ Psychol. 2015; 107(1):
1–2. https://doi.org/10.1037/edu0000007
44. Harris KR. Editorial: Is the work as good as it could be? J Educ Psychol. 2003; 95: 451–452. https://doi.
org/10.1037/0022-0663.95.3.451
45. Brambor T, Clark WR, Golder M. Understanding interaction models: improving empirical analyses.
Political Anal. 2006; 14: 63–82. https://doi.org/10.1093/pan/mpi014
Student person – environment fit and program interest diversity
PLOS ONE | https://doi.org/10.1371/journal.pone.0214618 April 4, 2019 26 / 26
Content uploaded by Bart Wille
Author content
All content in this area was uploaded by Bart Wille on Apr 09, 2019
Content may be subject to copyright.
Content uploaded by Stijn Schelfhout
Author content
All content in this area was uploaded by Stijn Schelfhout on Apr 04, 2019
Content may be subject to copyright.
