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Studies in Higher Education
ISSN: 0307-5079 (Print) 1470-174X (Online) Journal homepage: https://www.tandfonline.com/loi/cshe20
Is research-based learning effective? Evidence
from a pre–post analysis in the social sciences
Insa Wessels, Julia Rueß, Christopher Gess, Wolfgang Deicke & Matthias
To cite this article: Insa Wessels, Julia Rueß, Christopher Gess, Wolfgang Deicke & Matthias
Ziegler (2020): Is research-based learning effective? Evidence from a pre–post analysis in the
social sciences, Studies in Higher Education, DOI: 10.1080/03075079.2020.1739014
To link to this article: https://doi.org/10.1080/03075079.2020.1739014
© 2020 The Author(s). Published by Informa
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Is research-based learning eﬀective? Evidence from a pre–post
analysis in the social sciences
, Julia Rueß
, Christopher Gess
, Wolfgang Deicke
and Matthias Ziegler
bologna.lab, Humboldt-Universität zu Berlin, Berlin, Germany;
Department of Psychology, Humboldt-Universität zu
Berlin, Berlin, Germany
Research-based learning (RBL) is regarded as a panacea when it comes to
eﬀective instructional formats in higher education settings. It is said to
improve a wide set of research-related skills and is a recommended
learning experience for students. However, whether RBL in the social
sciences is indeed as eﬀective as has been postulated for other
disciplines has not yet been systematically examined. We thus
administered a pre–post-test study to N= 952 students enrolled in 70
RBL courses at 10 German universities and examined potential changes
in cognitive and aﬀective-motivational research dispositions. Latent
change score modelling indicated that students increased their
cognitive research dispositions, whereas most aﬀective-motivational
research dispositions decreased. The instructors’interest in the students’
work served as a signiﬁcant predictor of changes in research interest
and joy. Practical implications for designing RBL environments can be
inferred from the results.
research knowledge; research
dispositions; social sciences
Teaching and research can be linked through a variety of well-deﬁned instructional formats. One
of these is research-based learning (RBL), in which students conduct their own research with the
help of a supervisor. RBL is currently seen as a panacea for addressing a range of demands within
higher education, e.g. a lack of meaningful learning experiences and the need for stimulating
instructional formats. Accordingly, several authors and institutions claim that RBL should be
incorporated into the curriculum of many if not every academic study programme (e.g. Healey
and Jenkins 2009). Indeed, a growing number of programmes have attempted to implement
RBL in a range of disciplines and forms, e.g. the REU programme by the US National Science
Foundation. The main goal of these endeavours is to provide students with an opportunity to
experience participation in research. In science, technology, engineering, and mathematics
(STEM) disciplines, there is evidence that RBL does indeed live up to its promises and constitutes
an eﬀective learning experience (Linn et al. 2015). However, outside the STEM disciplines, it is still
unclear which research dispositions RBL fosters. Thus, this study aims to examine whether RBL’s
eﬀectiveness regarding the acquisition of various cognitive and aﬀective-motivational research
dispositions can be generalised to the social sciences.
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
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original work is properly cited, and is not altered, transformed, or built upon in any way.
CONTACT Insa Wessels email@example.com
Supplemental data for this article can be accessed at https://doi.org/10.1080/03075079.2020.1739014.
STUDIES IN HIGHER EDUCATION
Positioning research-based learning in relation to other forms of research-related
Teaching and research can be linked in diﬀerent ways. In a popular model, Healey and Jenkins (2009)
distinguish among diﬀerent instructional formats for engaging students in research along two axes.
The ﬁrst axis describes whether the research results or the research process is emphasised. The other
axis describes whether students take on an active role as participants or a passive role as audience.
These two axes can be combined into four diﬀerent formats: research-tutored, research-led, research-
oriented and research-based learning. In RBL, teaching focuses on the research process, and students
actively conduct research and inquiry. However, this description fails to describe the exact nature of
students’involvement in research. Huber (2014) further deﬁnes RBL as an instructional format in
which students work through the entire research process in a self-regulated manner, guided by
their own research questions. The instructor takes on a facilitating role. This theoretically derived
deﬁnition was replicated in an empirical classiﬁcation of research-related formats (Rueß, Gess, and
Deicke 2016) and serves as the underlying deﬁnition of RBL in the current study.
The eﬀectiveness of research-based learning
Conducting one’s own research project involves various cognitive, behavioural, and aﬀective experi-
ences (Lopatto, 2009, 29), which in turn lead to a wide range of beneﬁts associated with RBL.
RBL is associated with long-term societal beneﬁts because it can foster scientiﬁc careers: Students
participating in RBL reported a greater interest in pursuing postgraduate education or PhDs (Lopatto
2007; Russell, Hancock, and McCullough 2007) and were more likely to be engaged in scientiﬁc
careers six years after graduation (Hernandez et al. 2018).
In addition, RBL fosters research skills that are also necessary for occupations outside academia
(British Academy 2012). RBL is said to facilitate the development of a ‘researcher’s mindset’–the
ability to objectively examine data or a situation and ﬁnding enjoyment in solving problems
(Wood 2003). A researcher’s mindset can be eﬀective in a wide range of professional activities. For
example, in the ﬁeld of psychotherapy, therapists could draw upon their research knowledge to
consult evidence on new therapeutic approaches (Levant et al. 2006). Hence, the acquisition of
research-related knowledge and skills is a prerequisite for successfully engaging in both scientiﬁc
and non-scientiﬁc careers –making it an appropriate focus for our article.
Successfully engaging in a task requires both cognitive dispositions, such as knowledge, and
aﬀective-motivational dispositions to put this knowledge into practice (Blömeke, Gustafsson, and
Shavelson 2015). Disposition serves as an umbrella term to denote a range of latent, personal
resources (e.g. attitudes, traits and abilities) that determine how an individual will normally act in a
certain situation (Schmidt-Atzert and Amelang 2012, 63). Accordingly, competent performance in
the research domain requires various cognitive (e.g. knowledge) and aﬀective-motivational (e.g.
interest) research dispositions. Whether RBL is eﬀective at facilitating the development of diﬀerent
cognitive and aﬀective-motivational research dispositions has been the focus of previous studies.
The existing evidence will be introduced in the following sections.
Most empirical studies on the eﬀectiveness of RBL focus on cognitive research dispositions. However,
the majority of these studies assessed STEM students (e.g. Linn et al. 2015; Seymour et al. 2004), with
only a few studies investigating the eﬀect of RBL in the social sciences. In a study from the ﬁeld of
social work, students gained domain-general research knowledge (Whipple, Hughes, and Bowden
2015). Taraban and Logue (2012) found evidence for a range of cognitive beneﬁts of psychology stu-
dents’participation in research, such as improved research methods skills. Participation in RBL can
2I. WESSELS ET AL.
also lead to increased understanding of the scientiﬁc process as a whole (Lloyd, Shanks, and Lopatto
Other researchers have examined speciﬁc skills pertaining to individual research steps, e.g. the
ability to use statistics software (Whipple, Hughes, and Bowden 2015) and communicating and pre-
senting one’s research (Stanford et al. 2017). RBL also seems to facilitate more general cognitive dis-
positions like critical thinking (Hunter, Laursen, and Seymour 2007; Kilgo, Ezell Sheets, and Pascarella
2015) and the ability to work independently (Stanford et al. 2017).
Thus, while RBL in the social sciences seems to be eﬀective at facilitating a range of diﬀerent cog-
nitive dispositions, these results can only serve as preliminary evidence. A problem concerning the
interpretability of these and other studies in the ﬁeld lies in their methodological designs: Most exist-
ing studies focus on subjective ex-post assessments and self-evaluated skill gains (e.g. Stanford et al.
2017). However, self-assessments are often distorted by personality (John and Robins 1994) or skill
levels themselves (unskilled students overestimate their abilities, see Kruger and Dunning 1999).
Large-scale investigations using objective measures provide more substantial conclusions, but
have so far only been completed for STEM students (e.g. Russell, Hancock, and McCullough 2007).
Linn et al. (2015) note that the underlying problem is a lack of valid measures to objectively investi-
gate the eﬀectiveness of RBL. To address this problem, the Social-scientiﬁc Research Competency Test,
an objective measure of cognitive research dispositions in the social sciences, was developed by Gess
and colleagues (Gess, Geiger, and Ziegler 2018; Gess, Wessels, and Blömeke 2017). The instrument is
based on a coherent model of diﬀerent areas of research knowledge necessary to conduct critical
steps in the research process (see Appendix 1, online supplemental data). In validation studies, the
instrument has been shown to be suitable for evaluating social-scientiﬁc research education and
could serve as an objective measure of the cognitive beneﬁts of RBL.
Higher education research is increasingly acknowledging the importance of aﬀective-motivational
aspects for learning (e.g. Postareﬀand Lindblom-Ylänne 2011). Reﬂecting this general trend,
aﬀective-motivational gains have also drawn increased attention in research on RBL.
Evidence on RBL’s potential to alter aﬀective-motivational research dispositions often stems from
studies with multidisciplinary samples. Demonstrated beneﬁts include higher research self-eﬃcacy
(Deicke, Gess, and Rueß 2014; Whipple, Hughes, and Bowden 2015), increased intellectual curiosity
(Bauer and Bennett 2003) and a higher tolerance for obstacles in the research process (Lloyd,
Shanks, and Lopatto 2019). Furthermore, a study with STEM students demonstrated a greater
desire to learn and an increased disposition towards working with ambiguity (Ward, Bennett, and
The few existing studies all examine individual aﬀective-motivational research dispositions, often
in an exploratory manner. However, conducting research is an especially demanding task that
requires students to handle uncertainties and manifold frustrations (John and Creighton 2011).
Thus, it can be assumed that successfully conducting research requires a range of diﬀerent
aﬀective-motivational dispositions to cope with the challenges of the research process. A coherent,
empirically grounded model of the aﬀective-motivational research dispositions necessary for student
research in the social sciences has been recently developed (Wessels et al. 2018). It encompasses dis-
positions that are necessary to begin and to sustain the research process: for example, research inter-
est is needed to initiate a research process, while sustaining it requires frustration tolerance to cope
with inevitable setbacks. It is unclear whether RBL is eﬀective in developing these research
Overall, studies on the nature and eﬀectiveness of RBL in the social sciences are generally scarce
and often based on weak methodological designs –in contrast to studies from other disciplines.
However, one cannot assume that the evidence gained in studies with STEM students easily trans-
lates to the social sciences. First, research seems more important to university programmes in the
natural sciences than in the social sciences (cf. Taraban and Logue 2012). Second, most research
STUDIES IN HIGHER EDUCATION 3
experiences within STEM disciplines occur in structured lab environments that might have a diﬀerent
pedagogical culture (Rand 2016). Third, if discipline-speciﬁc outcome variables are to be investigated,
a study needs to be conducted in that speciﬁc discipline.
Another open question pertains to the processes by which RBL in the social sciences aﬀects
changes in diﬀerent research dispositions. In studies with STEM students, the main predictors of
learning gains are the duration and intensity of the research experience: longer-lasting and more
intense research experiences lead to stronger increases in skill levels (Bauer and Bennett 2003).
Another study found that students with higher levels of autonomy in the research process, e.g.
the autonomy to make their own methodological decisions, showed stronger learning gains
(Gilmore et al. 2015). However, which characteristics of RBL courses in the social sciences aﬀect
changes in diﬀerent research dispositions has not been studied yet.
Research questions and hypotheses
The objective of this paper is to analyse the eﬀectiveness of RBL courses in the social sciences. Two
main research questions guided our work: (1) Does research-based learning have a positive eﬀect on
cognitive and aﬀective-motivational research dispositions? (2) How do diﬀerent course characteristics
relate to changes in these research dispositions?
Pertaining to the ﬁrst research question, the following hypotheses were tested:
Hypothesis 1a: As previous studies have found associations between student research experiences and self-eval-
uated knowledge gains (Taraban and Logue 2012), we predict that students will have signiﬁcantly higher post-
test scores than pre-test scores for research knowledge (knowledge of methods, knowledge of methodologies
and research process knowledge).
Hypothesis 1b: As previous studies have found associations between student research experiences and a higher
tolerance for obstacles in the research process (Lloyd, Shanks, and Lopatto 2019) as well as an increased ability to
work with ambiguity (Ward, Bennett, and Bauer 2003), we predict that students will have signiﬁcantly higher post-
test scores than pre-test scores for aﬀective-motivational research dispositions.
Pertaining to the second research question, the following hypotheses were tested:
Hypothesis 2a: Since studies in STEM disciplines have demonstrated that longer and more intense research experi-
ences (Bauer and Bennett 2003) have a positive inﬂuence on the eﬀect of participation in RBL, we predict that the
intensity of the research experience, i.e. the number of research steps performed, will inﬂuence changes in
Hypothesis 2b: Since studies in STEM disciplines have demonstrated that higher levels of autonomy in the research
process (Gilmore et al. 2015) positively impact the eﬀect of participation in RBL, we predict that students’auton-
omy, i.e. ability to freely choose a research question and a research method, will positively aﬀect changes in
aﬀective-motivational research dispositions.
Hypothesis 2c: We predict that diﬀerent motivating factors, e.g. students’self-eﬃcacy, the perception that they are
doing ‘real research’, perceived instructor interest in the students’work, and the perceived usefulness of RBL for
their later career will positively aﬀect changes in aﬀective-motivational dispositions.
To answer our research questions, paper-based measurements were conducted at the beginning and
the end of RBL courses oﬀered in diﬀerent social scientiﬁc disciplines at 10 diﬀerent universities.
As the objective was to study comparable RBL courses in the social sciences, only the curricula of
study programmes employing empirical social science research methods were considered. These
4I. WESSELS ET AL.
included sociology, political science, psychology, and education science (see also Gess, Wessels, and
Suitable RBL courses were identiﬁed via their course descriptions. Only courses that allowed stu-
dents to experience a full research cycle in a self-regulated manner were considered, in line with our
deﬁnition of RBL. The instructors of 146 courses were contacted via email and asked to participate in
the study; 65 agreed to participate, 50 did not wish to participate, mostly due to time constraints in
the course, and the remaining 31 instructors did not respond. Pre-tests were scheduled for the ﬁrst
two weeks of the course, and post-tests for the last two weeks of the course.
Altogether, pre- and post-measurements were conducted in N= 70 RBL courses at 10 universities
across Germany. All universities included were state-funded public universities with 10,000-50,000
students oﬀering degrees in a wide range of disciplines.
The testing itself was conducted during class time by one of the authors of this article, who
explained the procedure and general purpose of the study. The questionnaires were administered
in the form of printed booklets. A personal 6-digit code based on non-sensitive information, e.g. birth-
day month, was used to match pre- and post-test questionnaires while granting anonymity. Filling in
the questionnaire took approximately 25 min. The post-test followed the same procedure. Addition-
ally, a brief instructor survey on characteristics of the course instruction was administered.
The sample encompassed N= 952 students (74.1% female, 23.5% male), of which 881 participated in
the ﬁrst measurement and 539 participated in the second measurement. Higher participation rates at
the ﬁrst measurement point were due to higher course attendance at the beginning of the semester.
The mean age of the participating students was M= 24.38 years (SD = 4.79). 61.6% of the students
were enrolled in a bachelor’s programme, while 29.5% were enrolled in a master’s programme. Fifty
students were enrolled in other study programmes, such as the traditional German university
diploma, and were treated as either bachelor’s or master’s students depending on their study pro-
gress. Bachelor’s students were near the end of their second year of study on average; the mean
number of semesters completed was M= 3.33 (SD = 1.67). Master’s students were at the beginning
of their second year of master’s studies on average, with M= 2.57 (SD = 1.63) semesters of the
degree completed on average.
The students were enrolled in diﬀerent ﬁelds of study, namely educational science (31.4% of the
students), psychology (22.4%), sociology (10.3%), communication science (8.6%), and political science
(5.5%) The remaining students were studying other, more speciﬁc social scientiﬁc subjects (i.e. media
The students were enrolled in one of 70 RBL courses. Participation was often a mandatory part of
the students’study programmes: 41.8% of the students were required to enrol in this speciﬁc course;
an additional 35.7% could have chosen a diﬀerent RBL course, while only 17.6% could have chosen a
course not involving the instructional format of RBL. The average number of participants per course
was M= 13.54 (SD = 12.62). The majority of students were enrolled in one-semester courses (77.7%);
22.3% of the students were enrolled in two-semester courses. The courses were led by 65 diﬀerent
instructors or co-teaching teams. Fifty two of these instructors participated in the instructors’
survey at the end of the course.
A 9-item short version of the social-scientiﬁc research competence measure by Gess, Wessels, and
Blömeke (2017) was used to assess research knowledge in the social sciences. This test assesses
knowledge of research methods, knowledge of methodologies and research process knowledge
with items referring to both quantitative and qualitative research. The test uses short vignettes
STUDIES IN HIGHER EDUCATION 5
coupled with multiple choice questions on diﬀerent research problems (see sample item in Appen-
dix 1, online supplemental data). The instrument has gone through several validation studies and is
suitable for the evaluation of research courses in the social sciences in both bachelor’sandmaster’s
degree programmes (Gess, Geiger, and Ziegler 2018; Gess, Wessels, and Blömeke 2017). Since the
full 27-item measure takes 35 min to complete and in-class time was sparse, a 9-item short version
reﬂecting the full breadth of the original test in terms of content areas was developed based on
the discrimination parameters, item diﬃculty, reliability and correlation with the long version.
The correlation of the person scores for the short version and the person scores for the long
version is r= 0.86, which indicates that the two versions measure a similar construct. However, it
as the long version. The students’answers were coded as either correct (1) or incorrect (0), such that
the ﬁnal data consisted of 9 dichotomous items. The reliability was acceptable, with weighted
omega h=0.69(see Table 1).
Aﬀective-motivational research dispositions
The model of aﬀective-motivational research dispositions (Wessels et al. 2018) encompasses nine
necessary dispositions for pursuing research in the social sciences, of which four were selected to
be investigated in the present study. (1) Value-related interest in research subsumes beliefs about
the usefulness of research. (2) Finding joy in conducting research denotes the joy experienced with
respect to diﬀerent research activities. (3) Research-related uncertainty tolerance is the disposition
to handle uncertainties in the research process. (4) Research-related frustration tolerance is the dispo-
sition to endure setbacks in the research process.
Self-assessment scales (sample items and basic descriptive data can be found in Table 1) were
developed in a multistep process following deductive and inductive test construction procedures
(Burisch 1984). First, at least 20 items per disposition were constructed according to ﬁxed theory-
driven construction principles (Wilson 2005). The items were selected and reﬁned based on a pilot
study with N= 250 students from the social sciences. The ﬁnal instruments encompass 4 or 5
items per disposition and exhibit acceptable or good reliabilities (weighted omega h= 0.68–0.82).
The response format for all aﬀective-motivational measures was a ﬁve-point Likert scale ranging
from 1 (completely disagree) to 5 (completely agree).
Table 1. Sample items, means, standard deviation and weighted omega of the variables at T1 and T2.
Disposition with sample item
items Mean (SD)
1. Research knowledge –T1
Sample item see Appendix 1, online supplemental data
9 0.46 (0.16) .69
2. Research knowledge –T2 9 0.50 (0.16) .66
3. Value-related interest in research –T1
‘Compared to other topics, I assign a high value to research.’
5 3.96 (0.46) .80
4. Value-related interest in research –T2 5 3.86 (0.50) .80
5. Joy in working with scientiﬁc literature –T1
‘I enjoy reading the scientiﬁc literature on a topic.’
4 3.20 (0.80) .77
6. Joy in working with scientiﬁc literature –T2 4 3.03 (0.85) .82
7. Joy in working with empirical data –T1
‘I enjoy analyzing data.’
4 3.44 (0.47) .68
8. Joy in working with empirical data –T2 4 3.45 (0.57) .74
9. Uncertainty tolerance –T1
‘Iﬁnd it disturbing that before I start my research project, I don’t know
whether everything will work out as I imagine it will.’
4 2.71 (0.73) .73
10. Uncertainty tolerance –T2 4 2.83 (0.78) .75
11. Frustration tolerance –T1
‘If my data analysis turns out to be incorrect and I have to start all over
again, I would probably despair.’
4 2.57 (0.54) .71
12. Frustration tolerance –T2 4 2.55 (0.56) .76
6I. WESSELS ET AL.
Instructor and course characteristics
Student survey. During the pre- and post-test, students were asked for additional information. At
pre-test, this included their self-assessed research self-eﬃcacy (6 items on a 5-point scale, e.g. ‘I
am sure I can ﬁnd suitable assessment tools for a quantitative study, even if the main variable is
diﬃcult to operationalize’). At post-test, students were asked about the research steps (e.g. searching
for relevant literature) they had completed so far, their perception of the instructor’s interest in their
research project, their perception of whether they were doing ‘real’research, and the perceived use-
fulness of the course for their later career (all measured with one item each on a 5-point scale).
Instructor survey. The post-test was also used to gather information about the course’s instructional
concept from the instructor’s perspective. A 5-minute questionnaire distributed to the instructors
asked about students’autonomy in choosing their own research question and method (two items
on a ﬁve-point scale).
In a ﬁrst step, students’pre- and post-test data were matched via their personal six-digit code. We
used SPSS 23 to conduct data checks and descriptive analyses of the manifest variables. To investi-
gate changes in the diﬀerent variables over time, we employed latent change score modelling
(McArdle 2009; LCM) and multiple regressions. LCM and all necessary preceding analyses were per-
formed with Mplus version 8 (Muthén and Muthén 2017). The following three steps were performed:
To conﬁrm the assumed factor structures and allow for a meaningful interpretation of the data, we
conducted conﬁrmatory factor analyses on all variables (see Appendix 2, online supplemental data).
For almost all variables, the unidimensional model exhibited better model ﬁt. The only exception was
the variable ‘Finding joy in conducting research’, which exhibited inadequate model ﬁts in both the
unidimensional and the three-dimensional solution. Hence, subsequent analyses were conducted
with two separate factors for this construct to ensure a meaningful interpretation of the data. The
ﬁrst factor describes ‘joy in working with scientiﬁc literature’, while the second describes ‘joy in
working with empirical data’.
Measurement invariance tests
A prerequisite for latent change score modelling is strong factorial invariance (McArdle 2009). Only if
strong factorial invariance is given can all factor loadings and intercepts be ﬁxed to the same values
for all measurement points. Following Meredith and Horn (2001), the CFI values of increasingly con-
strained models were compared (see Appendix 3, online supplemental data). For all variables, either
strong factorial invariance or partial measurement invariance was established, meaning that the sub-
sequent analyses can be meaningfully interpreted.
Latent change score modelling
We then employed LCMs to examine changes in our variables over time. In LCM, change is modelled
with latent diﬀerence variables that express the change across two or more measurement points (see
Figure 1). This approach enables us to observe interindividual diﬀerences in intraindividual change
free from measurement error (McArdle 2009).
LCM analyses were performed in two steps: In the ﬁrst step, we speciﬁed univariate LCMs (with
two measurement points, T1 and T2) for each variable. The latent change variable indicates intrain-
dividual changes from T1 to T2. Therefore, this variable was interpreted to test hypotheses H1a and
H1b (eﬀectiveness). The variance of the latent change variable indicates interindividual diﬀerences,
i.e. whether students’research dispositions develop in diﬀerent ways. When signiﬁcant
STUDIES IN HIGHER EDUCATION 7
interindividual diﬀerences were found, in a second step, the latent change variable was regressed on
six diﬀerent course characteristics. The regression coeﬃcients were then interpreted to test hypoth-
eses H2a, H2b and H2c (impact of course characteristics).
To account for the nested structure of the data (N= 952 students nested in 70 courses), we used
the course as a cluster variable with the Mplus command TYPE = COMPLEX. Additionally, auto-corre-
lated errors were included to account for method variance resulting from the use of the same items
over the two measurement points. Missing data were handled using full-information maximum like-
lihood estimation (FIML). The criteria suggested by Hu and Bentler (1999) were used as a reference
point for determining good model ﬁt: models with a CFI > 0.95 and a RMSEA < 0.06 were considered
to have adequate ﬁt.
Univariate latent change score models: changes in individual cognitive and aﬀective-
motivational research dispositions over time (hypotheses H1a and H1b)
The LCM for research knowledge exhibited good model ﬁt (see Table 2). The mean of the change
variable was small but signiﬁcant (ΔM= 0.04, p< .01), indicating a signiﬁcant change from T1 to
T2. This means that after taking the RBL course, students were able to correctly answer 0.45 questions
Figure 1. Illustrative latent change model for two measurement points and three items.
Table 2. Model ﬁts of all univariate latent change score models.
(df) pRMSEA CFI
1. Research knowledge 199.22 (141) .001 0.02 0.93
2. Value-related interest in research 132.61 (37) .001 0.05 0.95
3. Joy in working with scientiﬁc literature 62.18 (21) .001 0.05 0.98
4. Joy in working with empirical data 91.69 (20) .001 0.06 0.92
5. Uncertainty tolerance 39.29 (21) .01 0.03 0.98
6. Frustration tolerance 38.37 (20) .01 0.03 0.98
8I. WESSELS ET AL.
more on average (out of nine questions) than at T1. Thus, the data supported hypothesis H1a. The
variance of the change variable was very small and not signiﬁcant (σ
= 0.001, p= .8), indicating
that there were no interindividual diﬀerences.
The univariate LCMs for all aﬀective-motivational dispositions had very good model ﬁts (see Table
2). The dispositions diﬀered in their development from T1 to T2:
Value-related interest in research
The results revealed a signiﬁcant decrease from T1 to T2 (ΔM=−0.14, p< .01). The signiﬁcant variance
of the change variable (σ
= 0.33, p< .01) indicates the presence of interindividual diﬀerences in
changes in interest.
Joy with respect to research activities
As described above, this variable consisted of two distinct factors whose development was examined
individually. The results suggest a signiﬁcant decrease in ‘joy in working with scientiﬁc literature’from
T1 to T2 (ΔM=−0.17, p< .01). The signiﬁcant variance of the change variable (σ
= 0.36, p< .01) indi-
cates that there were diﬀerences in students’trajectories. No signiﬁcant change was observed for the
second factor, ‘joy in working with empirical data’(ΔM=−0.05, p= .25). The signiﬁcant variance indi-
cates the presence of interindividual diﬀerences in students’trajectories (σ
= 0.15, p< .01).
The results suggest a signiﬁcant increase from T1 to T2 (ΔM= 0.12, p< .01). The signiﬁcant variance
= 0.38, p< .01) indicates that there were substantial interindividual diﬀerences in students’
The results show that frustration tolerance did not change signiﬁcantly from T1 to T2 (ΔM= 0.03, p
= .24). The signiﬁcant variance was indicative of interindividual diﬀerences (σ
= 0.12, p< .01).
Therefore, the data supports hypothesis H1b only with respect to uncertainty tolerance. For value-
related interest in research and joy in working with scientiﬁc literature, signiﬁcant decreases were
Inﬂuence of other variables on changes in diﬀerent research dispositions over time
(hypotheses H2a, H2b and H2c)
Next, predictors of the change variable were analysed for the research dispositions for which the uni-
variate LCMs showed evidence of interindividual diﬀerences. This was the case for value-related inter-
est for research, joy in working with scientiﬁc literature and uncertainty tolerance.
Value-related interest for research
The multiple regression revealed two signiﬁcant and positive predictors of the latent change in value-
related interest in research: the perceived usefulness of the course for one’s later career and the
instructor’s perceived interest in the students’work. The overall variance explained by this regression
model was 10% (see Table 3).
Joy in working with scientiﬁc literature
The perceived usefulness of the course served as a signiﬁcant predictor of the latent change in joy
from T1 to T2. Students who perceived the course as useful for their later career experienced
greater increases in joy in working with scientiﬁc literature. The full regression model explained
5% of the variance in the change in joy (see Table 3).
STUDIES IN HIGHER EDUCATION 9
Uncertainty tolerance was signiﬁcantly predicted by research self-eﬃcacy at T1. Self-eﬃcacy served
as a negative predictor: the higher a student’s self-eﬃcacy, the more uncertainty tolerance decreased
or the less it increased. The overall variance explained by this regression model was 6% (see Table 3).
These ﬁndings are in line with hypothesis H2c, which examined the inﬂuence of additional motiv-
ating factors. Hypotheses H2a and H2b were not supported.
Discussion and implications
Our study examined the eﬀectiveness of RBL in the social sciences. By applying pre–post measure-
ments in 70 courses, we examined changes in diﬀerent cognitive and aﬀective-motivational research
dispositions through participation in RBL. Research knowledge increased signiﬁcantly, but no inter-
individual diﬀerences were observed that could be further investigated. Research-related uncertainty
tolerance increased, whereas research interest and joy in working with scientiﬁc literature decreased
over the course of RBL participation. Subsequent regression analyses showed that the change in
uncertainty tolerance was signiﬁcantly predicted by research self-eﬃcacy. The changes in interest
and joy were predicted by the perceived usefulness of the course for one’s later career, while the
change in interest was also predicted by the instructor’s perceived interest in the students’work.
Contrary to our expectations, the number of research steps performed and the autonomy students
were given during the RBL experience did not have an eﬀect on changes to any of the aﬀective-moti-
vational research dispositions.
Overall, research knowledge increased signiﬁcantly over the course of RBL participation (see hypoth-
esis 1a). Previous studies with students from individual social scientiﬁc disciplines have reported com-
parable results (e.g. Taraban and Logue 2012). We were able to conﬁrm these ﬁndings using an
objective test instrument assessing three sub-areas of research knowledge: knowledge of
methods, knowledge of methodologies and research process knowledge in the social sciences.
However, the students in our sample did not exhibit substantial interindividual diﬀerences in their
improvement and no further analyses could be conducted to explain diﬀerences in the observed
change with reference to other variables. This lack of interindividual diﬀerences might have been
Table 3. Multiple regression analysis for aﬀective-motivational research dispositions.
Change of value-related
interest in research
Change of joy in working
with scientiﬁc literature
Change of uncertainty
Predictor variables (and time point of
measurement) B(SE) βB(SE) βB(SE) β
Research self-eﬃcacy (T1) −0.01 (0.01) −0.08 −0.01
−0.01 −0.03 (0.01) ** −0.22 **
Number of research steps performed (T2) −0.02 (0.03) −0.04 <−0.01
<−0.01 0.02 (0.03) 0.04
Usefulness of the course for a later
0.10 (0.03) ** 0.24 ** 0.10 (0.04)
0.22 ** −0.01 (0.04) −0.03
Student autonomy (T2 –lecturer survey) 0.02 (0.02) 0.04 0.01 (0.04) 0.03 −0.04 (0.03) −0.09
Lecturers interest in students’work (T2) 0.09 (0.04) * 0.16 * <−0.01
−0.01 0.03 (0.04) 0.05
Perception of conducting ‘real’research
−0.04 (0.04) −0.10 0.00 (0.04) 0.01 −0.01 (0.03) −0.02
AIC 31,375 28,945 29,746
(SE) 0.10 (0.04) 0.05 (0.03) 0.06 (0.04)
Note: B= unstandardised coeﬃcients, SE = standard error; β= standardised coeﬃcients.
10 I. WESSELS ET AL.
due to similar answering patterns on the knowledge items. We used a 9-item short version of a longer
test, which might not have been suﬃcient to identify substantial diﬀerences between students. In
future projects, we would recommend using the 27-item test form or another objective measurement
that yields more variance in students’answers.
Aﬀective-motivational research dispositions
A signiﬁcant change from the ﬁrst to the second measurement point was found for three out of the
four aﬀective-motivational research dispositions examined.
In line with our expectations (see hypothesis 1b), uncertainty tolerance increased over the course
of RBL participation. This change in uncertainty tolerance was signiﬁcantly predicted by research self-
eﬃcacy (see hypothesis 2c). However, self-eﬃcacy served as a negative predictor: the higher a stu-
dent’s self-eﬃcacy, the smaller the positive change in uncertainty tolerance. Students with low
levels of research self-eﬃcacy might exhibit stronger increases in uncertainty tolerance because
these students have less research experience and thus beneﬁt more strongly from participation in
RBL. A high level of uncertainty tolerance is important for coping with the unpredictable nature of
the research process. Some claim that uncertainty tolerance is vital not only for conducting research
but also for facing an increasingly complex world in general (Brew 2010). In this sense, uncertainty
tolerance not only assists students in pursuing scientiﬁc careers but also prepares students for
other professions. How students’uncertainty tolerance can be changed is currently a subject of
debate in several ﬁelds. In the health sciences, it has been suggested that medical students’uncer-
tainty tolerance can be enhanced by monitoring and controlling emotional processes related to
uncertainty (Iannello et al. 2017). Translating this recommendation to research in the social sciences,
we suggest integrating guided reﬂections on experienced emotions related to uncertainty in the
research process. One way of doing so would be to use reﬂective learning diaries (Nevalainen, Man-
tyranta, and Pitkala 2010). However, we did not test for reﬂective processes related to uncertainty in
our sample. We can only assume that some instructors reﬂected on and discussed research-related
uncertainties. Further research investigating the inﬂuence of guided reﬂection processes on the
development of uncertainty tolerance in RBL courses would be necessary to come to a more compre-
Interest and joy in research exhibited high mean values during both the pre- and post-test, indi-
cating that the participating students are generally very fond of research and related activities.
However, unlike uncertainty tolerance, interest and joy decreased over the course of RBL partici-
pation (see hypothesis 1b). There are several possible explanations for this. Perhaps students gain
a more realistic idea of what research is during the course. At the beginning of their studies, students’
conceptions of research might be inﬂuenced by the predominant view of research in their society: in
Germany, the public perceives research as interesting and trustworthy (Wissenschaft im Dialog 2018).
Thus, realising how small the explanatory power of a single research project is might be frustrating or
disillusioning. Gaining a more realistic understanding of the nature and practice of research might
lead to decreased interest or joy in research, while simultaneously serves as an indication of what
others have termed ‘becoming a scientist’(Hunter, Laursen, and Seymour 2007).
The regression analyses showed that certain course variables served as signiﬁcant predictors (see
hypothesis 2c): changes in students’interest in research were signiﬁcantly predicted by the instruc-
tor’s perceived interest in the students’research and the perceived usefulness of the course for their
later career (both rated by the students). Perceiving that the instructor is interested in their work
might be motivating for students and increase their own interest in research. As a practical impli-
cation, this does not mean that instructors should pretend to be interested in students’work. It
could suﬃce for instructors to choose topics for RBL courses that are of genuine interest to them
–for example, their own research topics. Bringing one’s own research topics into the classroom,
thereby combining one’s teaching and research, has often been recommended as a useful practice
for instructors (Vicens and Bourne 2009). One of the main arguments for this is that it saves valuable
STUDIES IN HIGHER EDUCATION 11
time for instructors involved in both teaching and research. Our results additionally suggest that com-
bining teaching and research comes with beneﬁts for students, who feel more motivated by their
instructors’interest in the topic.
Changes in joy were signiﬁcantly predicted by the perceived usefulness of the course for students’
later careers: those students who perceived the course as useful for their future career gained more
joy in research. For students who do not aspire to academic careers, it might be beneﬁcial to empha-
sise or enhance the course’s usefulness for outside academia, e.g. by choosing research topics that
are of interest in non-academic careers or applying service learning (Potter, Caﬀrey, and Plante
2003). In this way, more students might perceive conducting their own research projects as useful
for careers outside academia and therefore ﬁnd greater joy in doing research.
Contrary to our expectations, the number of research steps performed and the autonomy students
were given during the RBL experience did not have an eﬀect on changes to any of the aﬀective-moti-
vational research dispositions (see hypothesis 2a and 2b). This indicates that even working on pre-
deﬁned research problems or completing only a limited amount of research steps has a positive
eﬀect on students.
Overall, the regression models used to predict changes in diﬀerent aﬀective-motivational variables
accounted for 5% (joy in working with scientiﬁc literature), 6% (uncertainty tolerance) and 10% (research
interest) of the latent change variable’s variance. While these eﬀect sizes can be classiﬁed as small
(Cohen 1988), it is important to put these values into perspective: given that answering the question-
naires on the predictor variables took students only 1–2 min, the cost-value ratio of these regression
analyses can be considered very positive. From a more fundamental perspective, it must be noted
that aﬀective-motivational dispositions are complex, multidimensional phenomena that are inﬂuenced
by a range of external variables, such as current mood or personal life events. The variables examined in
this study (e.g. student autonomy, instructors’interest) are not suﬃcient to accurately predict changes in
diﬀerent aﬀective-motivational research dispositions over an entire course. However, they did partially
serve as signiﬁcant predictors and thus provide practical new ideas for designing RBL courses.
Limitations and implications for future research
A problem with our and other studies in the ﬁeld is the lack of a control group (cf. Lopatto 2004).
Without an adequate comparison group, it remains unclear whether the research experience itself
is eﬀective or whether it is the type of student who participates in RBL courses (Linn et al. 2015).
Some authors claim that students who seek out RBL courses have higher academic abilities and
are more motivated than other students in the ﬁrst place (Carter et al. 2016). In our sample, partici-
pation in the RBL course was often a mandatory part of the students’study programme; thus, a strong
self-selection bias in our sample can be ruled out. Nevertheless, a meaningful, matched control group
is still necessary to draw ﬁnal conclusions on the eﬀectiveness of RBL, e.g. by examining study pro-
grammes with a waiting list for RBL courses.
Another limitation concerns the testing time point. Since the post-measurement was conducted in
the classroom towards the end of the course, our results do not reﬂect the eﬀect of writing ﬁnal
papers or presenting research results. However, giving a public presentation on one’s research has
been described as particularly motivating by students (Cuthbert, Arunachalam, and Licina 2012)
and thus might inﬂuence the learning outcomes associated with RBL. Future research should incor-
porate the eﬀects of ﬁnal assignments by using later or follow-up measurements.
Our study’s quantitative set-up meant that the students’personal perspectives on their research
projects, individual reactions to challenges in the research process and additional thoughts on their
instructors’behaviour could not be addressed. A future project could further explore and validate the
preliminary ﬁndings of this study and the resulting implications by incorporating students’perspec-
tives via in-depth interviews.
The aim of this study was to examine the eﬀectiveness of RBL courses in the social sciences for
enhancing cognitive and aﬀective-motivational research dispositions. Based on the results, we can
12 I. WESSELS ET AL.
conclude that RBL is an eﬀective instructional format for enhancing research knowledge and
research-related uncertainty tolerance. RBL courses proved especially eﬀective when students
thought the RBL experience was useful for their later career.
The question of whether RBL is an eﬀective instructional format has so far been dominated by
studies from the ﬁeld of STEM, while evidence from the social sciences remains scarce. Our study
sought to provide a systematic account of the eﬀectiveness of RBL among students from diﬀerent
social scientiﬁc disciplines for enhancing discipline-speciﬁc measures using a pre–post design.
While the chosen procedure was suitable for extending existing evidence in the ﬁeld, a range of
open questions remain that should be addressed in further research endeavours.
1. What we call RBL has diﬀerent names elsewhere, e.g. ‘undergraduate research experiences’(URE), ‘summer under-
graduate research experiences’(SURE) or ‘course-based undergraduate research experiences’(CURE). Most of
these terms describe the context (‘during the summer’) or the type of students (‘undergraduates’) rather than
the instructional set-up per se. We chose the term ‘RBL’to denote a speciﬁc instructional approach independent
of the exact duration or the participating students. We do, however, use evidence from studies examining ‘CURE’
or ‘URE’. We carefully checked that the students’research experiences aligned with our notion of RBL.
We would like to thank Christoph Geiger, Frederic Lenz and Luise Behm for their valuable help in conducting this study.
We acknowledge support by the Open Access Publication Fund of Humboldt-Universität zu Berlin.
No potential conﬂict of interest was reported by the author(s).
This research was supported by the German Federal Ministry of Education and Research [grant number 01PB14004B].
Insa Wessels http://orcid.org/0000-0002-7799-8409
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