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Corresponding Authors:
David C. Vaidis, Laboratoire Cognition, Langues, Langage, Ergonomie, Université de Toulouse, Centre National de la Recherche Scientifique,
Toulouse, France
Email: david.vaidis@univ-tlse2.fr
Willem W. A. Sleegers, Social Psychology, Tilburg University, Tilburg, the Netherlands
Email: w.sleegers@me.com
A Multilab Replication of the
Induced-Compliance Paradigm
of Cognitive Dissonance
David C. Vaidis1, Willem W. A. Sleegers2, Florian van Leeuwen2,
Kenneth G. DeMarree3, Bjørn Sætrevik4, Robert M. Ross5,
Kathleen Schmidt6, John Protzko7, Coby Morvinski8,
Omid Ghasemi9, Andrew J. Roberts5, Jeff Stone10, Alexandre Bran11,
Amélie Gourdon-Kanhukamwe12 , Ceren Gunsoy13,
Lisa S. Moussaoui14 , Andrew R. Smith15 , Armelle Nugier16,
Marie-Pierre Fayant11, Ali H. Al-Hoorie17 , Obed K. Appiah18,
Spencer Arbige19, Benjamin Aubert-Teillaud11, Olga Bialobrzeska20,
Stéphanie Bordel21 , Valerian Boudjemadi22, Hilmar Brohmer23,
Quinn Cabooter24, Mehdi Chahir25, Ianis Chassang26 ,
Armand Chatard27 , Yu Yang Chou28 , Sungeun Chung29 ,
Mioara Cristea30, Joséphine Daga31, Gregory J. Depow32 ,
Olivier Desrichard33 , Dmitrii Dubrov34 , Thomas R. Evans35 ,
Séverine Falkowicz31, Sylvain Ferreira16 , Tim Figureau36 ,
Valérie Fointiat31, Théo Friedrich26, Anastasia Gashkova37,
Fabien Girandola31, Marine Granjon38, Dmitry Grigoryev39 ,
Gul Gunaydin40 , Şevval Güzel41, Mahsa Hazrati42, Mai Helmy43,
Ayumi Ikeda44 , Michael Inzlicht32, Sara Jaubert31, Dauren Kasanov45,
Mohammad Mohsen Khoddami46, Taenyun Kim47, Kiyoshi Kiyokawa48,
Rabia I. Kodapanakkal49, Alexandra Kosachenko50 ,
Kortney Maedge51, John H. Mahaney52, Marie-Amélie Martinie27,
Vitor N. Mascheretti53, Yoriko Matsuda48, Maxime Mauduy54,
Nicolas Mauny54, Armand Metzen11, Eva Moreno-Bella55,
Miguel Moya56, Kévin Nadarajah57 , Pegah Nejat58 ,
Elisabeth Norman59, Irmak Olcaysoy Okten60 , Asil A. Özdoğru61 ,
Ceyda Ozer62 , Elena Padial-Rojas63, Yuri G. Pavlov64 ,
Monica Perusquia-Hernandez65 , Dora Proost66,
Aleksandra Rabinovitch67 , Odile Rohmer38, Emre Selcuk40 ,
Cécile Sénémeaud54, Yaniv Shani68 , Elena A. Shmeleva69,
Emmelie Simoens70, Kaitlin A. Smith7, Alain Somat25 ,
Hayeon Song71, Fatih Sonmez72 , Lionel Souchet31 , John J. Taylor10,
2 Vaidis et al.
Ilja van Beest2, Nicolas Van der Linden73 , Steven Verheyen74,
Bruno Verschuere75 , Kevin Vezirian76 , Luc Vieira11,
Sera Wiechert18 , Guillermo B. Willis77, Robin Wollast16 , Ji Xia78,
Yuki Yamada79 , Naoto Yoshimura80, and Daniel Priolo36
1Laboratoire Cognition, Langues, Langage, Ergonomie, Université de Toulouse, Centre National
de la Recherche Scientifique, Toulouse, France; 2Social Psychology, Tilburg University, Tilburg,
the Netherlands; 3Department of Psychology, SUNY Buffalo State University, Buffalo, New York;
4Department of Psychosocial Science, University of Bergen, Bergen, Norway; 5Department of
Philosophy, Macquarie University, Sydney, Australia; 6Department of Psychology, Ashland University,
Ashland, Ohio; 7Psychological Science, Central Connecticut State University, New Britain, Connecticut;
8Department of Management, Ben-Gurion University of the Negev, Beersheba, Israel; 9School of
Psychological Sciences, University of New South Wales, Kensington, Australia; 10Department of
Psychology, University of Arizona, Tucson, Arizona; 11Laboratoire de Psychologie Sociale, Université
Paris Cité, Paris, France; 12Department of Neuroimaging, King’s College London, London, England;
13Department of Psychology, University of Rhode Island, South Kingstown, Rhode Island; 14Faculty
of Psychology and Education, Health Psychology Research Group, University of Geneva, Geneva,
Switzerland; 15Department of Psychology, Appalachian State University, Boone, North Carolina;
16Laboratoire Cognition, Langues, Langage, Ergonomie, Laboratoire de Psychologie Sociale et
Cognitive, Université Clermont Auvergne, Clermont-Ferrand, France; 17Royal Commission for Jubail
and Yanbu, Jubail English Language and Preparatory Year Institute, Jubail, Saudi Arabia; 18Department
of Psychology, University of Amsterdam, Amsterdam, the Netherlands; 19Department of Psychology,
Macquarie University, Sydney, Australia; 20Faculty of Psychology in Warsaw, Center for Research on
Social Relations, Szkoła Wyz
.sza Psychologii Społecznej University of Social Sciences and Humanities,
Warsaw, Poland; 21PsyCAP, Centre d’études et d’expertise sur les risques, l’environnement, la mobilité
et l’aménagement (CEREMA), Saint-Brieuc, France; 22Department of Psychology, Université de
Strasbourg, Strasbourg, France; 23Institute of Psychology, University of Graz, Graz, Austria; 24Department
of Psychology, Ghent University, Ghent, Belgium; 25Department of Psychology, Université de Rennes
2, Rennes, France; 26Department of Psychology, Université de Strasbourg, Strasbourg, France; 27Centre
de Recherches sur la Cognition et l’Apprentissage, Université de Poitiers, Centre National de la
Recherche Scientifique, Poitiers, France; 28Department of Neuroimaging, King’s College London,
London, England; 29Media and Communication, Sungkyunkwan University, Seoul, South Korea;
30Department of Psychology, Heriot Watt University, Edinburgh, Scotland; 31Laboratoire de Psychologie
Sociale, Aix-Marseille Université, Aix-en-Provence, France; 32Department of Psychology, University
of Toronto, Toronto, Ontario, Canada; 33Faculté de Psychologie et des Sciences de l’Éducation,
Université de Genève, Geneva, Switzerland; 34Center for Sociocultural Research, Higher School of
Economics University, Moscow, Russia; 35School of Human Science, University of Greenwich, London,
England; 36Laboratoire Epsylon, Université Paul-Valéry, Montpellier, France; 37Center of Population
Research, Ural Federal University, Yekaterinburg, Russia; 38Laboratoire de Psychologie des Cognitions,
Université de Strasbourg, Strasbourg, France; 39Center for Sociocultural Research, National Research
University Higher School of Economics, Moscow, Russia; 40Department of Psychology, Sabanci
University, Sabanci, Turkey; 41Department of Psychology, Üsküdar University, Istanbul, Turkey;
42Department of Psychology, Shahid Beheshti University, Tajrish, Iran; 43Psychology Department,
College of Education, Sultan Qaboos University, Seeb, Oman; 44Japan Society for the Promotion of
Science, Kyushu University, Fukuoka, Japan; 45Department of Psychology, Ural Federal University,
Yekaterinburg, Russia; 46Department of Psychology, Shahid Beheshti University, Tehran, Iran;
47Department of Interaction Science, Sungkyunkwan University, Seoul, South Kora; 48Cybernetics and
Reality Engineering Laboratory, Nara Institute of Science and Technology, Ikoma, Japan; 49Department
of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, the
Netherlands; 50Department of Psychology, Ural Federal University, Yekaterinburg, Russia; 51Department
of Psychology, Southern Illinois University, Carbondale, Illinois; 52Department of Psychology,
Appalachian State University, Boone, North Carolina; 53Department of Psychology, Université Paul-
Valéry Montpellier, Montpellier, France; 54Laboratoire de Psychologie de Caen Normandie, Université
de Caen Normandie, Caen, France; 55Social and Organizational Psychology, Education Faculty of
Psychology, National University of Distance, Madrid, Spain; 56Centro de Investigación Mente, Cerebro
y Comportamiento, University of Granada, Granada, Spain; 57Department of Psychology, Rennes
University, Rennes, France; 58Faculty of Education and Psychology, Shahid Beheshti University, Tajrish,
Iran; 59Department of Psychosocial Science, Det Psykologiske Fakultet, Universitetet i Bergen, Bergen,
Norway; 60Department of Psychology, Florida State University, Tallahassee, Florida; 61Department
of Psychology, Marmara University, TC Üsküdar Üniversitesi, Instabul, Turkey; 62Department of
Psychology, Sabanci Universitesi, Tuzla, Turkey; 63Department of Social Psychology, University of
Granada, Granada, Spain; 64Laboratory of Neurotechnology, Ural Federal University, Yekaterinburg,
Russia; 65Information Science, Nara Institute of Science and Technology, Ikoma, Japan; 66Psychology,
Ghent University, Ghent, Belgium; 67Psychology, Szkoła Wyz
.sza Psychologii Społecznej University
of Social Sciences and Humanities, Warsaw, Poland; 68Management, Tel Aviv University, Tel Aviv,
Israel; 69Department of Psychology, Ivanovo State University, Ivanovo, Russia; 70Faculteit Psychologie
Advances in Methods and Practices in Psychological Science 7(1) 3
Cognitive-dissonance theory (CDT; Festinger, 1957) is
one of the most influential theories in the psychological
sciences (Devine & Brodish, 2003; Gawronski & Strack,
2012; Haggbloom etal., 2002). “Cognitive dissonance”
refers to the aversive state caused by the co-occurrence
of two or more inconsistent cognitions that motivates
individuals to find ways to relieve the discomfort
(Harmon-Jones, 2019). It has mainly been studied through
observing attitude change following an inconsistency
between an attitude and a behavior. CDT has frequently
been the topic of review articles and books (e.g., Cooper,
2007; Harmon-Jones, 2019; McGrath, 2017), and a discus-
sion of CDT and its applications can be found in most
psychology textbooks (Aronson & Aronson, 2018; Griggs
& Christopher, 2016). CDT has been applied to a wide
variety of situations, including belief disconfirmation,
effort justification, hypocrisy, and decision-making (see
Freijy & Kothe, 2013).
The recent replication crisis in the psychological sci-
ences has raised several methodological issues,
including low statistical power (Maxwell etal., 2015)
and researcher degrees of freedom (Simmons etal.,
2011), that call into question the credibility of past find-
ings. These issues also apply to classical studies in the
CDT literature and raise concerns about the replicability
of prior findings (Vaidis & Bran, 2019). We therefore set
out to perform a test of CDT by conducting a pre registered
multilab replication of one of the most prevalent cogni-
tive-dissonance paradigms: the induced-compliance
paradigm.
The Induced-Compliance Paradigm
Although a large number of studies have been conducted
to evaluate CDT, one particular paradigm has become
the dominant choice for investigating CDT and its under-
lying mechanisms (Cooper, 2007; Harmon-Jones, 2019).
A procedure borrowed from the persuasion field (Janis
& King, 1954), the induced-compliance paradigm con-
sists of inducing participants to perform a behavior that
en Pedagogische Wetenschappen, University of Ghent, Ghent, Belgium; 71Department of Psychology,
Sungkyunkwan University, Seoul, South Korea; 72Business Administration, Mus Alparslan University,
Muş, Turkey; 73Center for Social and Cultural Psychology, Université Libre de Bruxelles, Brussels,
Belgium; 74Department of Psychology, Education and Child Studies, Erasmus University, Rotterdam,
the Netherlands; 75Department of Clinical Psychology, University of Amsterdam, Amsterdam, the
Netherlands; 76Department of Psychology, Universite Grenoble Alpes, Grenoble, France; 77Psicologia
Social y Metodologia de las Ciencias del Comportamiento, University of Granada, Granada, Spain;
78Department of Psychology, SUNY Buffalo State University, Buffalo, New York; 79Faculty of Arts and
Science, Kyushu University, Fukuoka, Japan; and 80Research Organization of Open Innovation and
Collaboration, Ritsumeikan University, Kyoto, Japan
Abstract
According to cognitive-dissonance theory, performing counterattitudinal behavior produces a state of dissonance that
people are motivated to resolve, usually by changing their attitude to be in line with their behavior. One of the most
popular experimental paradigms used to produce such attitude change is the induced-compliance paradigm. Despite its
popularity, the replication crisis in social psychology and other fields, as well as methodological limitations associated
with the paradigm, raise concerns about the robustness of classic studies in this literature. We therefore conducted
a multilab constructive replication of the induced-compliance paradigm based on Croyle and Cooper (Experiment
1). In a total of 39 labs from 19 countries and 14 languages, participants (N = 4,898) were assigned to one of three
conditions: writing a counterattitudinal essay under high choice, writing a counterattitudinal essay under low choice,
or writing a neutral essay under high choice. The primary analyses failed to support the core hypothesis: No significant
difference in attitude was observed after writing a counterattitudinal essay under high choice compared with low choice.
However, we did observe a significant difference in attitude after writing a counterattitudinal essay compared with
writing a neutral essay. Secondary analyses revealed the pattern of results to be robust to data exclusions, lab variability,
and attitude assessment. Additional exploratory analyses were conducted to test predictions from cognitive-dissonance
theory. Overall, the results call into question whether the induced-compliance paradigm provides robust evidence for
cognitive dissonance.
Keywords
cognitive dissonance, induced compliance, counterattitudinal essay, attitude change, replication, multilabs
Received 6/5/20; Revision accepted 10/25/23
4 Vaidis et al.
is opposite to that implied by an existing attitude (i.e.,
counterattitudinal). According to CDT, this procedure
creates an aversive state (cognitive dissonance) that par-
ticipants are motivated to resolve. This resolution could
be achieved when the participants change their attitude
to be in line with their behavior. In the first studies using
this paradigm,1 participants were asked to perform a
counterattitudinal task in exchange for either a small or
sizable reward (e.g., Brehm & Cohen, 1962; Festinger &
Carlsmith, 1959). The size of the reward provides either
a sufficient or insufficient justification that prevents
(large reward) or favors (small reward) attitude change.
In later studies, researchers shifted from manipulating
reward size to manipulating the choice given to partici-
pants as a justification for their counterattitudinal behav-
ior (see Linder etal., 1967).
The most commonly used task in the induced-
compliance paradigm is the counterattitudinal-essay task
(Kim etal., 2014). In this task, participants are induced
to write a short essay consisting of several arguments in
favor of a position they themselves do not hold (e.g.,
Brehm & Cohen, 1962; A. R. Cohen etal., 1958). For
example, college students may be asked to argue in favor
of a tuition fee increase, usually under the guise that a
university committee is considering implementing the
change. Dissonance is manipulated by varying the way
the request stresses the choice given to the participants
to write the arguments: Participants are either reminded
that their participation is voluntary (high choice) or
merely instructed to perform the task (low choice).
According to CDT, participants in the high-choice condi-
tion are prevented from attributing their behavior to an
external justification, leading them to experience more
dissonance than the participants in the low-choice con-
dition. After writing the arguments, participants report
their attitude toward the essay topic. If participants expe-
rienced dissonance, this can be resolved by expressing
an attitude in line with their behavior. The typically
reported finding has been that participants show a more
behavior-consistent attitude after writing the essay in the
high-choice condition compared with the low-choice
condition (McGrath, 2017).
The induced-compliance paradigm along with the
counterattitudinal-essay task have served as the experi-
mental context to test some of the most fundamental CDT
premises. Relying on this paradigm, researchers have (a)
examined arousal attributions as an underlying process
for attitude change (Croyle & Cooper, 1983; Zanna &
Cooper, 1974); (b) revealed that self-affirmation reduces
attitude change (Steele & Liu, 1983); (c) shown that dis-
sonance is associated with aversive arousal, which is
alleviated following attitude change (Elliot & Devine,
1994); and (d) investigated alternative ways to regulate
dissonance, such as trivialization (Simon etal., 1995) and
the denial of responsibility (Gosling etal., 2006). In the
same vein, the paradigm has been used to test theoretical
moderators, such as public exposure (Baumeister & Tice,
1984), aversive consequences (Harmon-Jones et al.,
1996), and self-esteem (Stone & Cooper, 2003). The
induced-compliance paradigm has also been found to
produce the largest effect size (Cohen’s d = 0.81) com-
pared with several alternative cognitive-dissonance para-
digms (Kenworthy etal., 2011). Finally, the paradigm is
still used today and serves as a standard method for
testing CDT hypotheses (e.g., Cooper & Feldman, 2019;
Forstmann & Sagioglou, 2020; Randles etal., 2015).
Despite the popularity of the induced-compliance
paradigm, there are compelling reasons to doubt its rep-
licability. The credibility crisis has increased suspicion
about numerous established findings in social psychol-
ogy (Nelson etal., 2018; Pashler & Wagenmakers, 2012),
including CDT findings. Research involving the induced-
compliance paradigm has often relied on small sample
sizes (i.e., around 20 per cell; e.g., Elliot & Devine, 1994;
Linder etal., 1967; Simon etal., 1995; Steele & Liu, 1983;
Zanna & Cooper, 1974; but for an exception, see Murray
etal., 2012) and has produced large effect sizes in several
seminal studies (e.g., d > 1.5; Elliot & Devine, 1994;
Simon etal., 1995). Although none of these issues neces-
sarily undermine the paradigm or the theory the para-
digm seeks to support, they do question the credibility
of existing evidence and highlight the importance of
conducting a high-powered, preregistered, multilab rep-
lication study that addresses these issues.
The Replication Project
An important challenge in setting up a replication of
the induced-compliance paradigm is the lack of a
single canonical study. Studies in which the induced-
compliance paradigm has been used differ in many ways,
including the dissonance-inducing task, attitude assess-
ment, and control conditions. In addition, many of these
studies suffer from methodological limitations (Vaidis &
Bran, 2019). Consequently, we argue that instead of con-
ducting a direct replication, it is better to conduct a
constructive replication—a replication study that is close
to a seminal study but also includes new elements to
address important limitations (Hüffmeier et al., 2016;
Nosek & Errington, 2020). After careful consideration, we
selected a study performed by Croyle and Cooper (1983,
Experiment 1). This study consists of a typical implemen-
tation of the induced-compliance paradigm with the
addition of several methodological refinements not typi-
cally found in this paradigm, such as the inclusion of a
pre- and postessay attitude measure and multiple control
conditions. We therefore based our constructive replica-
tion of the induced-compliance paradigm on this study.
In Croyle and Cooper’s (1983) Experiment 1, partici-
pants were asked to write an essay on the topic of
Advances in Methods and Practices in Psychological Science 7(1) 5
implementing an alcohol ban on campus. Thirty partici-
pants were randomly assigned to one of three condi-
tions: a high-choice counterattitudinal condition, a
high-choice pro-attitudinal condition (i.e., consonant; in
which participants wrote arguments against implement-
ing the ban), and a low-choice counterattitudinal condi-
tion. The participants’ attitude toward the essay topic
was first assessed 1 to 3 weeks before the experiment,
and only participants who were against the alcohol ban
participated in the study. Participants’ attitudes were
assessed a second time immediately after the essay. Con-
sistent with CDT, participants in the high-choice counter-
attitudinal condition demonstrated greater attitude change
compared with the two remaining conditions. Note, how-
ever, that the study consisted of a small sample (n = 10
per analysis cell), and the analyses revealed a dispropor-
tionately large effect size (d = 2.40, 95% confidence inter-
val [CI] = [1.40, 3.37]2) compared with the typical effect
size in social psychology (d = 0.43, Richard etal., 2003;
d = 0.30, Schäfer & Schwarz, 2019) and effect sizes found
in meta-analyses on the induced-compliance paradigm
(d = 0.81, Kenworthy etal., 2011; d = 0.51,3 Kim etal.,
2014). Despite these results, Croyle and Cooper performed
a typical implementation of the induced-compliance para-
digm that matches those in seminal studies in the CDT
literature (e.g., Elliot & Devine, 1994; Gawronski & Strack,
2004; Zanna & Cooper, 1974). We therefore could expect
a successful replication but with an effect size smaller
than what was found in the original study.
One of the reasons we chose to replicate Experiment
1 of Croyle and Cooper (1983) is that they assessed
attitude change rather than relying solely on a postessay
attitude assessment. This feature is important because
a key hypothesis of Festinger’s (1957) theory is that
participants will demonstrate a change in attitude fol-
lowing an attitude-relevant discrepancy. As a conse-
quence, the proper assessment of a participant’s
response to dissonance requires a prior assessment of
the attitude in question. However, researchers in the
CDT literature have frequently used between-subjects
comparisons to infer attitude change from group differ-
ences rather than measuring attitude change intraper-
sonally. Although group differences are informative,
they preclude estimates of change within and across
groups. Although unlikely, group differences among
conditions in a counterattitudinal advocacy study may
arise from reactance in the control condition (i.e., even
more negative attitudes) rather than dissonance reduc-
tion in the experimental conditions. Consequently, a
pre-essay attitude assessment is needed to directly test
the core hypothesis of CDT. Having this measure pro-
vides several other advantages, including a likely
increase in statistical power and the opportunity to test
moderating factors, such as the size of attitude-behavior
discrepancy.
A second reason to replicate Experiment 1 of Croyle
and Cooper (1983) is that they included two control
conditions. Conceptually, CDT relies on inconsistency
to produce a change in the relevant attitude; however,
operationally, researchers have mainly used choice to
produce a state of dissonance (Vaidis & Bran, 2019). The
induced-compliance paradigm typically requires partici-
pants to write a dissonance-inducing counterattitudinal
essay under high- or low-choice conditions. Researchers
assume that participants in the high-choice conditions
attribute their inconsistent behavior to their own choice
and, therefore, must resolve the inconsistency by chang-
ing their attitudes. In contrast, participants in the low-
choice conditions can resolve their potential dissonance
by attributing the discrepant behavior to their lack of
choice (i.e., an external attribution). Therefore, this
design employs a control condition (the low-choice con-
dition) in which dissonance may be experienced but is
quickly resolved by participants externally justifying
their behavior. In other words, the control condition is
not likely to be entirely devoid of a dissonance state.
This questionable choice of control conditions raised
early critiques (e.g., Chapanis & Chapanis, 1964; Kiesler,
1971; Wicklund & Brehm, 1976) that encouraged control
conditions that separately control for both inconsistency
and choice. Croyle and Cooper followed these sugges-
tions and used three conditions. In the experimental
condition, participants wrote a counterattitudinal essay
under high choice. In the first control condition, partici-
pants wrote the counterattitudinal essay under low
choice, and in their second control condition, partici-
pants wrote a pro-attitudinal essay (under high choice).
The counterattitudinal high-choice condition was com-
pared with both a counterattitudinal low-choice condi-
tion and a pro-attitudinal high-choice condition. This
design enabled them to control for an effect of choice
(low vs. high) and essay position (counterattitudinal
vs. pro-attitudinal). Although we believe that the pro-
attitudinal control condition has certain limitations, the
inclusion of multiple control conditions further justifies
this choice of study for the replication.
Deviations From Croyle and Cooper
(1983, Experiment 1)
The main goal of this project was to conduct a compel-
ling test of the induced-compliance paradigm based on
Experiment 1 of Croyle and Cooper (1983). Rather than
performing a direct replication of this study, we con-
ducted a constructive replication that also addresses
previously raised limitations of the induced-compliance
paradigm (Vaidis & Bran, 2019). In this project, we
focused on the following issues: (a) attitude assessment,
(b) the operationalization of dissonance, and (c) threats
to internal validity.
6 Vaidis et al.
Attitude assessment
Induced-compliance studies vary substantially in their
attitude-assessment methods. These variations involve
participant selection, which essay topic is used, and how
the pre- and postessay attitudes are measured.
In CDT studies, within-subjects designs are sometimes
implemented by assessing participants’ attitude several
weeks before the study (e.g., Elliot & Devine, 1994;
Steele & Liu, 1983), usually at the start of the student
semester. For instance, Croyle and Cooper (1983)
recruited participants who completed an attitude ques-
tionnaire 1 to 3 weeks before the experiment. This pre-
test enabled them to assess attitude change and to recruit
only participants who disagreed with the essay topic. In
the current project, we similarly strove to include an
attitude assessment at least 1 week before the main
study. The main reason for this attitude assessment is to
obtain a measure of attitude change. However, unlike
Croyle and Cooper (1983), we did not use the initial
attitude assessment to recruit only counterattitudinal
participants. This deviation was chosen for several rea-
sons. First, not all participating labs can include an atti-
tude assessment several weeks before the study, making
it difficult to compare the findings between labs if some
labs used a selection criterion and others did not. Sec-
ond, many induced-compliance studies do not include
an initial attitude assessment and cannot be directly
compared with studies in which it is included. A design
in which some labs have an initial attitude assessment
and others do not allows us to compare our findings
with a larger range of studies. Third, without a selection
criterion, there will be more variation in initial attitudes,
allowing tests of the size of the attitude discrepancy as
a moderating factor. Overall, our deviation from Croyle
and Cooper to not recruit only counterattitudinal par-
ticipants allows for easier implementation and valuable
comparison between studies.
There is considerable variation in which essay topics
are used in induced-compliance-paradigm studies. Exam-
ples include raising tuition fees, prohibiting alcohol, ban-
ning campus speakers, and instituting mandatory
comprehensive exams. The suitability of the essay topic
greatly depends on the context. For instance, the essay
topic used by Croyle and Cooper (1983) was the introduc-
tion of an alcohol ban on the university campus. This
essay topic is not suitable for all labs because some uni-
versities already have similar bans in place. Other topics,
such as introducing mandatory comprehensive exams,
may also not be applicable to all contexts (e.g., situations
in which such exams are already required). It is therefore
not surprising that there is much variation between stud-
ies regarding the essay topic. A related issue with topic
selection is the lack of information concerning additional
attitude components (i.e., importance, certainty), making
it more difficult to determine suitable essay topics.
To address the issue of which essay topic to use, we
conducted a pretest with a subset of the participating
labs to determine suitable essay topics. We assessed
attitude agreement and other attitude components, such
as attitude importance, of 15 different topics. This pretest
indicated that an increase in tuition fees seemed to be
the most suitable essay topic for many labs. We therefore
selected an increase in tuition fees as the default topic
to be used, although we also left the option open for
some labs to use a different topic if they deemed the
essay topic to not be applicable to their lab (for more
details, see the Method section).
Croyle and Cooper (1983) used a 31-point scale to
assess the participants’ attitude toward their essay topic.
The recommended number of Likert-scale options is still
a controversial issue (Eutsler & Lang, 2015; Krosnick &
Presser, 2009; Simms etal., 2019) and highly variable in
the cognitive-dissonance literature; the number of points
ranges from 5 (e.g., Gawronski & Strack, 2004) to 61
(e.g., Schaffer, 1974; also see Vaidis & Bran, 2019). In
the current study, we aimed to strike a balance between
validity and sensitivity. A small range of answers could
reduce the required variation of the expected effect, and
too large a range could reduce the validity of the pro-
vided answers (Matell & Jacoby, 1971). We ultimately
opted for a 9-point Likert scale to balance these require-
ments. This scale should permit enough room for varia-
tion to obtain differences between the conditions, and
each option can be meaningfully labeled to improve the
validity of the scale. A 9-point Likert scale has also been
used in multiple other induced-compliance studies (e.g.,
Harmon-Jones etal., 1996; Simon etal., 1995; Starzyk
etal., 2009). In addition, Croyle and Cooper assessed
the postessay attitude with a single item. To achieve
greater reliability in the attitude assessment, we used
one main item to assess the attitude but also added three
additional items (based on Stalder & Baron, 1998).
Improved control condition
A strength of Croyle and Cooper’s (1983) design is the
inclusion of two control conditions. Classically, the coun-
terattitudinal low-choice condition is used as a control
condition. As noted before, however, it cannot be con-
sidered as a condition in which no cognitive dissonance
is experienced. Writing a counterattitudinal essay under
low choice still involves an inconsistency; thus, it has to
be considered as a control for the effect of choice (high
vs. low) on attitude change. An additional control condi-
tion is warranted that does not involve an inconsistency
at all. Croyle and Cooper included a control condition
that indeed is not expected to result in any cognitive
dissonance—a consonant high-choice condition. How-
ever, because writing in favor of one’s attitude may
strengthen it (Briñol etal., 2012), we believe that this
control condition can be improved by asking participants
Advances in Methods and Practices in Psychological Science 7(1) 7
to write a neutral essay rather than a pro-attitudinal
essay. In this case, no inconsistency is involved, nor do
we expect it to change the attitude toward the essay
topic of the experimental condition.
Experimenter interaction
A final difference with Croyle and Cooper (1983) lies in
the social interaction between the experimenter and par-
ticipant. In the traditional induced-compliance paradigm,
the participant gives the experimenter a verbal agree-
ment to write the essay. This act is assumed to induce
the individual to self-attribute the responsibility of writ-
ing the essay (Beauvois & Joule, 1996; Kiesler, 1971;
McGrath, 2017; Zanna & Cooper, 1974), which may be
necessary for the individual to regulate the dissonance
state through attitude change (Vaidis & Bran, 2018).
An interaction between the participant and experi-
menter regarding the participant’s choice to write the
essay has the unwanted consequence of making the
experimenter aware of the participant’s experimental
condition (i.e., the design is “single blind”). From a
methodological point of view, this knowledge allows for
experimenter expectations to affect the results (Orne,
1962). It is preferable to use a double-blind procedure
instead (for an example in the case of a 2 × 2 factorial
design, see Linder etal., 1967, Experiment 2). One way
to achieve a double-blind design is to use a computer-
mediated procedure (e.g., Losch & Cacioppo, 1990).
However, a fully computer-mediated procedure could
result in high numbers of participants refusing to write
the essay (about 25% attrition in the case of Losch &
Cacioppo, 1990). Thus, including a social interaction
seems important to induce compliance in high-choice-
condition participants.
A constructive replication therefore requires a double-
blind procedure to avoid any influence of the experi-
menter but should also include a social interaction
between the participant and experimenter regarding the
participant’s decision to complete the essay task. In our
design, we aimed to respect both requirements. We
relied on a computer-mediated procedure that minimizes
the experimenter-participant interaction, thereby reduc-
ing possible demand effects, while still including an
interaction to promote compliance. In addition, the
experimenter was kept blind to a subset of the condi-
tions. The experimenter was inevitably aware of whether
the participant was in the low-choice or high-choice
conditions (because of the social interaction) but
remained unaware of whether the participant had to
write a neutral or counterattitudinal essay. This proce-
dure was a compromise between reducing the risk of
attrition in high-choice conditions and limiting experi-
menter demands.
Summary of the Registered
Replication Report
In the previous sections, we have shown that there is
considerable variation in methodological practices across
induced-compliance studies and that a proper assess-
ment of the attitude change in the most used cognitive-
dissonance paradigm is limited by methodological
shortcomings. As a result, a direct replication of any
individual previous study cannot provide a strong test
for the induced-compliance paradigm. We therefore
developed a constructive replication (Hüffmeier etal.,
2016) based on Experiment 1 of Croyle and Cooper
(1983) that addressed its methodological limitations.
With this study, we aimed to assess the replicability of
the induced-compliance paradigm and its findings.
Disclosures
The project was a Registered Report. The design, mea-
sures, manipulations, sample size, and data exclusions
were approved before data collection. Analyses indi-
cated as primary and secondary were preregistered and
can be found in the Stage 1 accepted manuscript. All
relevant files, including the Stage 1 manuscript, materi-
als, data, ethical approval documents, and code, are
publicly available on OSF at https://osf.io/9xsmj/.
Method
Design, sample size, and participating
laboratories
Design. The induced-compliance paradigm with an
essay-writing task was used to examine whether a state of
cognitive dissonance can produce a change in attitude
toward a new university policy. The experience of disso-
nance was manipulated with three conditions, two of
which serve as control conditions. These control groups
were set to separately assess the effect of choice (high
choice vs. low choice in writing the essay) and inconsis-
tency (writing a counterattitudinal essay vs. a neutral
essay). For most labs, the attitude of the participants
toward the university policy was assessed twice: several
weeks before the study and again during the main study.
Some labs were unable to recruit the same participants
twice for the study and included only a postessay attitude
assessment. The study therefore mainly consists of a 2
(within subjects: Pre-essay Attitude Assessment vs. Postes-
say Attitude Assessment) × 3 (between-subjects: High-
Choice Counterattitudinal Essay [HC-CE], Low-Choice
Counterattitudinal Essay [LC-CE], High-Choice Neutral
Essay [HC-NE]) mixed design; 37 labs included a pre-essay
attitude assessment, and two did not.
8 Vaidis et al.
Sample size. We determined the minimum required
sample size based on simulated data. Our goal was to
obtain an overall power of 95% for the primary analyses
(for details, see the Power Analysis section below). In
short, the power analysis revealed that a minimum of
1,760 participants was needed. Because no laboratory
would be able to reach this requirement alone and because
the goal of the registered replication report is not to deter-
mine whether each individual lab obtains a statistically sig-
nificant result, we did not set an identical required sample
size per lab. Instead, each lab was free to recruit as many
participants as possible based on available resources,
which resulted in a total of 4,898 participants (see Table 1).
Participating labs and lab recruitment. A total of 39
labs from 19 countries and 14 languages participated in
the data collection. Groups of labs joined the replication
effort in several phases. The first group of researchers was
composed of researchers experienced with CDT studies. A
second group was composed of interested researchers via
the Society for the Improvement of Psychological Science
conference (Sleegers & Vaidis, 2019). A third group was
composed following calls for contribution spread through
social networks and academic mailing lists. A final fourth
group of researchers joined the project following the Stage
1 acceptance of the current article. There were no exclu-
sion criteria for labs to join the project. Initially, 23 addi-
tional labs including seven other countries joined the
project but withdrew because of unforeseen challenges in
data collection (in particular, recruiting participants for in-
person data collection during the COVID-19 pandemic).
When translations were required, labs were asked to
translate the materials and back-translate them to ensure
accuracy of the translations. The lead authors (D. Vaidis
and W. Sleegers) coordinated this effort to ensure all
labs used similar materials. Labs were also instructed to
remain blind to the results until data collection was
completed. The expectations of lab members regarding
the replication results and effect sizes were measured
before data collection (see Additional Exploratory Analy-
ses Plan section). Each lab received a training video of
the procedure. They were also required to take photos
or record a video of their lab and to make their study
materials available on OSF.
The principal investigator from each lab signed a col-
laborator agreement indicating the ethical guidelines and
the preregistered procedure would be followed. For an
overview of the lab characteristics, see Table 2.
Population and participant recruitment. As in most
studies using the induced-compliance paradigm, partici-
pants were university students. Labs were encouraged to
recruit first-year students to reduce the likelihood that par-
ticipants were familiar with CDT. The study was advertised
to students as a series of tasks, some concerning students’
opinions. Varying incentives were used to recruit partici-
pants: They took part in the study in exchange for course
credits, monetary incentives, or no incentive. The labs
documented their use of incentives because this could
affect the attitude change (e.g., Festinger & Carlsmith,
1959; Kim etal., 2014). Lab characteristics (e.g., type of
room, incentive) are available in the supplemental mate-
rial on OSF for use in exploratory analyses.
Selection of the essay topic
Many different essay topics have been used in the CDT
literature, although a popular topic is an increase in
tuition fees (e.g., Elliot & Devine, 1994; Steele & Liu,
1983). We used a pretest to determine suitable essay
topics from a subset of data-collection sites.4 The pretest
assessed 15 hypothetical educational-policy changes
(e.g., [University name] should raise the tuition fee for
the upcoming academic year) on four dimensions: agree-
ment (1 = strongly disagree, 9 = strongly agree), impor-
tance (1 = not important at all, 9 = very important),
preference to write arguments against or in favor of the
topic (in favor/against/indifferent), and plausibility of
implementation (likely/unlikely).
The pretest provided participating labs with a set of
potential essay topics to choose from. On the basis of
the results of the pretest, we found that the increase in
tuition fees essay topic was a suitable topic in most of
the labs and also showed the least amount of variance
in terms of attitude agreement. We therefore set the
increase in tuition fees as the default topic to be used.
However, because the topic would not be suitable for
all participating labs (e.g., because of the university not
Table 1. Overview of the Study’s Design, Including Sample Size per Condition
No. Choice Essay Label Purpose N (pre-post) N (total)
1 High Counterattitudinal HC-CE Experimental condition 1,329 1,891
2 Low Counterattitudinal LC-CE Choice control 1,185 1,725
3 High Neutral HC-NE Inconsistency control 889 1,282
Note: The N (pre-post) column indicates the sample size of participants who completed both the pre-essay and postessay
attitude assessment. The N (total) column indicates the total sample size, including participants who were unable to
complete the pre-essay attitude assessment.
Advances in Methods and Practices in Psychological Science 7(1) 9
Table 2. Sample Size, Language, and Topic Used per Lab University
University Country, language Topic used Pre-post design
Université Paris Cité FR, French Tuition fee Yes
Tilburg University NL, Dutch, English Tuition fee Yes
Université Paul Valéry Montpellier 3 FR, French Tuition fee Yes
Appalachian State University USA, English Tuition fee No
University of Amsterdam NL, Dutch Tuition fee Yes
Université Caen Normandie FR, French Tuition fee Yes
Aix-Marseille Université FR, French Tuition fee Yes
University of Arizona USA, English Tuition fee Yes
University at Buffalo USA, English Tuition fee Yes
King’s College London UK, English Tuition fee No
University of Bergen NOR, Norwegian Harder exam Yes
Higher School of Economics University RU, Russian Tuition fee Yes
Central Connecticut State University USA, English Tuition fee Yes
University of Granada ES, Spanish Tuition fee Yes
Southern Illinois University USA, English Tuition fee Yes
University of Toronto CAN, English Tuition fee Yes
Université Grenoble Alpes FR, French Tuition fee Yes
Université de Poitiers FR, French Tuition fee Yes
Université de Genève CH, French Tuition fee Yes
Clemson University USA, English Tuition fee Yes
Université Clermont Auvergne FR, French Tuition fee Yes
Üsküdar University TUR, Turkish Tuition fee Yes
Université Rennes 2 FR, French Tuition fee Yes
SWPS University of Social Sciences and Humanities PL, Polish Tuition fee Yes
Université de Strasbourg FR, French Tuition fee Yes
Macquarie University AUS, English Tuition fee Yes
Sungkyunkwan University KOR, Korean Tuition fee Yes
Kyushu University JAP, Japanese Tuition fee Yes
Muş Alparslan University TUR, Turkish Tuition fee Yes
Université Libre de Bruxelles BE, French Tuition fee Yes
University of Graz AUT, German Tuition fee Yes
Ben-Gurion University ISR, Hebrew Tuition fee Yes
Ural Federal University RU, Russian Tuition fee Yes
Sultan Qaboos University OMN, Arabic Tuition fee Yes
Florida State University USA, English Tuition fee Yes
Sabanci University TUR, Turkish Tuition fee Yes
Shahid Beheshti University IRN, Persian 6 a.m. classes Yes
Erasmus University Rotterdam NL, English Tuition fee Yes
Nara Institute of Science and Technology JAP, Japanese Tuition fee Yes
Note: Labs are chronologically ordered based on when they joined the project.
having any tuition fees), two labs used a different topic
(see Table 2). We defined a topic as suitable when (a)
pretest participants reported a median attitude score
lower than 5 on the agreement dimension, (b) pretest
participants reported a median attitude score greater
than 5 on the importance dimension, and (c) at least
80% of the sample preferred writing against this topic.
The pretest showed that plausibility ratings were often
low, so rather than using a cutoff score, we used the
plausibility ratings to select the most suitable topic (i.e.,
with the highest plausibility rating). In the following
sections, we use the tuition-fees topic as an example to
describe the materials and refer to the OSF page for the
exact materials that each lab used.
For labs that included the pre-essay attitude assess-
ment (k = 37), participants completed a survey at least
1 week before the main study (M = 2.28 weeks, Mdn =
1.85).5 The pre-essay measure was chiefly performed
online, with an anonymous code to link the data between
the survey and the main study. Among other filler tasks,
participants responded to a short questionnaire about
student topics, including the essay topic from the main
study. For each topic, attitudes were assessed on both
an agreement and an importance dimension using a
10 Vaidis et al.
9-point-scale from 1 (strongly disagree or not important
at all) to 9 (strongly agree or very important).
Procedure
The main study was performed in lab rooms in isolated
or semi-isolated cubicles. The study was conducted on
a computer using the Qualtrics survey platform. The
study was presented as a set of smaller studies, starting
with a neutral filler task that was alleged to be the main
study. After the filler task, participants completed the
essay task.
Experimenters were trained to follow a predefined
script, including a business-casual dress code (for details,
see OSF). The experimenter guided each participant to
the computer but remained at a distance while partici-
pants completed the study, except for a specific moment
during the study during which participants contacted
the experimenter to start the essay task. All participants
within a particular session were randomly assigned to
one of the three conditions. In the low-choice condition,
the experimenter instructed participants to continue the
essay task. In the high-choice conditions, the experi-
menter asked participants to give their consent to write
the essay and proceed to the essay task. This interaction
was designed to keep the experimenter blind to whether
the participant had been assigned a counterattitudinal
or neutral topic. This procedure guaranteed that (a) par-
ticipants had to engage in a social interaction, (b) experi-
menters remained blind to the participant’s essay content
in the high-choice conditions, and (c) experimenters had
a limited opportunity to influence participants.
Essay instructions
All participants were introduced to the essay task with
a cover story that claimed this part of the study was
about a survey concerning student life that had been
requested by the university. The essay instructions were
based on Croyle and Cooper (1983) while also adapting
aspects from other CDT studies that have used a similar
procedure (e.g., Elliot & Devine, 1994; Gosling etal.,
2006; Losch & Cacioppo, 1990). The task started the
same way for all participants (here illustrated with the
increase in tuition topic):
[University name] is currently discussing making
changes to several university policies. Our depart-
ment has been asked to assist a committee by col-
lecting information on a number of issues. Because
the changes to the policies could impact students
currently studying at [University name], we would like
your participation in the discussion of these issues.
One of the evaluated policies concerns [University
name]’s tuition. More specifically, [University name]
has set up a committee on campus to investigate
the possibility of a substantial tuition increase. After
reviewing what they find, the committee will make
a recommendation to the administration regarding
the tuition increase that could be implemented at
[University name] in the next academic year.
In the HC-CE and LC-CE conditions, participants were
then instructed to write arguments counter to their atti-
tude (e.g., in favor of a tuition-fee increase). For them,
the complementary instruction was as follows:
Before making a recommendation, however, the
committee would like to gather arguments on both
sides of the issue. We have found that a good way
of doing this is simply to ask people to list all the
arguments they can think of that support a particu-
lar side of the issue. In other words, whatever one’s
personal opinion, we want them to write arguments
either only in favor or only against the increase of
tuition.
Because we have already finished gathering
arguments against a tuition increase, we are now
ready to gather arguments in favor of a tuition
increase. We thus want you to write ONLY argu-
ments IN FAVOR of the increase in tuition.
This was followed by a summary of the instructions
on the next page:
To summarize, in the next task, you will have to
write ONLY arguments IN FAVOR of the tuition
increase. Your arguments will be sent directly to
the committee, who will make a decision on this
issue based on the arguments they receive from
you and other students.
In the HC-NE control condition, participants were
instructed to suggest arguments about additional topics
that they think should be investigated by a future com-
mittee that is currently being set up:
Beside this topic, the committee would also like to
gather suggestions for additional educational pol-
icy changes that require the attention of future
committees. We would like you to write arguments
in favor of specific topics that should be investi-
gated by future committees.
This was followed by a summary of the instructions
on the next page:
Advances in Methods and Practices in Psychological Science 7(1) 11
To summarize, in the next task, you will have to
write arguments for additional topics that should
be investigated by future committees. Your argu-
ments will be sent directly to the current commit-
tee, who will make a decision on this issue based
on the arguments they receive from you and other
students.
After the essay instructions, participants were instructed
to contact the experimenter. When called, the experi-
menter approached the participant, keeping a neutral
face (no smiling), and asked the participant (with a neu-
tral tone), “Did you understand the instructions?” The
experimenter waited for the participant’s agreement
(verbal or nonverbal) before continuing. To ensure that
the experimenter remained blind to the essay content,
this interaction did not reveal what essay the participant
had been instructed to write.
In the low-choice condition, the experimenter simply
instructed the participant to begin the task. In the high-
choice conditions, the experimenter reminded the par-
ticipant about the voluntary nature of the task:
Because this next task is part of a research project,
we want to remind you that your participation is
completely voluntary. We would appreciate your
help, but we do want to let you know that it’s
completely up to you.6
The experimenter then asked participants whether they
were willing to perform the task. After the experimenter
received verbal agreement, the participant received a
form that served the purpose of reinforcing the choice
manipulation. The form stated,
I understand the nature of the task I am being
asked to perform. I am aware that my list of argu-
ments is intended for use by the University com-
mittee. I further understand that I will receive
[compensation/my participation credit, if applica-
ble] regardless of whether or not I write, and allow
release of, my list of arguments.
Participants were asked to sign and date the form and
check one of two boxes to indicate whether they allowed
their arguments to be released to the committee.
If the participant asked any clarification questions,
the experimenter answered them following a predefined
script. If participants questioned the cover story, experi-
menters explained that they are only in charge of the
session and are unable to give further details. Partici-
pants were then asked to follow the instructions as best
as they can. If the participant expressed doubts about
willingness to perform the task, the experimenter
restated the instructions once more (respectively, in
high-choice and low-choice conditions: “Your participa-
tion is voluntary. It’s completely up to you.” vs. “We
would like you to complete the task.”). Finally, if partici-
pants refused to perform the essay task, they were asked
to skip the essay task and continue the study. If they
refused this as well, the study was stopped.
Next, participants were presented with instructions
for writing their arguments. They were reminded of the
previous instruction to write in favor of the policy or to
provide additional topics to be investigated and were
instructed to write three short paragraphs including their
argumentation. They were informed they had a maxi-
mum of 5 min to complete the task. On the next page,
three different text areas required keyboard input from
the participant. A reminder of the remaining time was
displayed after 3 min. A second message was displayed
when 30 s remained, which informed participants that
their responses would soon be automatically submitted.
The submit button was active only after 3 min, and the
page was automatically submitted after 5 min.
Dependent measures
Postessay attitude assessment. The postessay attitude
was assessed directly after the completion of the essay
task.7 Regardless of the condition, participants indicated
their attitude toward the main policy on four items. The
first attitude measurement was identical to the pre-essay
attitude measure (e.g., “[University name] should raise the
tuition fee for the upcoming academic year”) and was
used to assess attitude change. Three additional attitude
items were adapted from Stalder and Baron (1998) and
were used to assess a broader attitude: “How would you
describe your overall opinion toward [raising tuition]?”
(1 = extremely unfavorable, 9 = extremely favorable); “To
what extent do you think there are disadvantages to [rais-
ing tuition]?” (1 = no disadvantages, 9 = a great many;
reverse-scored); and “To what extent do you think that
[raising tuition] is good for [University name]?” (1 = not at
all, 9 = a great deal).
At the end of the procedure, after the attitude assess-
ment, participants were asked to complete a measure
of perceived choice, a measure of affect, and a self-
construal scale. These questions were presented under
the cover story that the previously mentioned committee
was interested in these questions.
Perceived choice. Because perceived choice has often
been used as a manipulation check in dissonance studies,
we first included this perception of choice measure. Par-
ticipants were asked to indicate their perceived degree of
choice to perform the essay task on two 9-point scales
(1 = no choice at all, 9 = totally free to choose): “How free
12 Vaidis et al.
did you feel to decline to write the essay?” (Croyle &
Cooper, 1983), and “How much choice did you feel you
had to participate in this study?” (Cooper & Mackie, 1983).
The first item refers directly to the freedom to decline writ-
ing the essay and was therefore used as a manipulation
check. The second item was included for exploratory pur-
poses only and is not analyzed in this article.
Affect and self-construal. In addition to assessing per-
ceived choice, we also included additional measures for
exploratory purposes. We included an adapted version of
the Positive and Negative Affect Schedule (PANAS; Watson
etal., 1988) to assess the participant’s affective experience
while performing the essay task. We chose the PANAS
because it has already been translated into several lan-
guages that are used in this study. Participants were
instructed to indicate how they felt while writing the essay
task on a 5-point scale (1 = very slightly or not at all, 5 =
extremely). Two items, uncomfortable and conflicted,
were added to specifically assess a dissonance state (also
see Devine etal., 2019).
Finally, we added a questionnaire to assess self-con-
strual (20 items; Park & Kitayama, 2014) because some
perspectives on dissonance consider the self to be an
important construct related to the experience of disso-
nance (Aronson, 1992, 2019; Steele & Liu, 1983; Stone
& Cooper, 2003). The research on self-construal suggests
that the dissonance effect could be experienced differ-
ently depending on one’s culture (Markus & Kitayama,
1991). More specifically, in the free-choice paradigm
(Brehm, 1956), results suggest that interdependent self-
construal participants rationalize their choices less than
independent self-construal participants (e.g., Heine &
Lehman, 1997; Kitayama etal., 2004). To our knowledge,
no study has evaluated the role of self-construal in the
induced-compliance paradigm. Because this multilab
study involves participants from a large number of cul-
tures, we considered it an opportune situation to exam-
ine the impact of self-construal in this paradigm.
Demographics and funnel debriefing
After completing the steps of the experiment listed
above, participants answered several demographic ques-
tions: age, sex, academic degree and major, fluent lan-
guages, and native country. Two questions concerning
the participant’s university affiliation were used as exclu-
sion criteria: “What is your current university?” and “Do
you plan to study at the same university next year?”
At the end of the study, participants answered several
questions, presented one by one, that served as a funnel
debriefing: “Do you have any comments?”; “What do you
think is the purpose of the study?”; “Do you have any
psychological phenomena or theories in mind that you
think this study is about? If so, which one(s)?”; “If the
goal of the study was not the one we presented, would
you have an idea about the reason why we asked you
to write down arguments?”; “Have you ever heard about
cognitive dissonance theory? If so, please briefly describe
the theory.”; and “Have you ever participated in a similar
study where you were asked to provide arguments for
a particular position?” After answering these questions,
participants were fully debriefed by the experimenter
and thanked for their participation.
Exclusion criteria
Participant characteristics. Our registered plan was
to exclude participants that were not students at the time
of the study or that did not plan to be students at the same
university in the following year. Participants were also
required to be unaware of the study’s purpose. We there-
fore registered a plan for three raters, blind to the condi-
tions, to categorize each participant as being aware or
unaware of the study’s purpose based on their responses
to the funnel-debriefing questions. Participants were
excluded from the analysis if at least two of the raters cat-
egorized them as being aware of the study’s purpose.
Essay content. We had no registered plan to exclude
participants based on their essay response. Traditionally,
participants who did not comply with the essay instruc-
tions are excluded from the analyses; their data are
removed if they gave at least one pro-attitudinal or no
counterattitudinal argument. This attrition is strongly con-
ditional and can comprise around 20% of a counterattitu-
dinal condition (e.g., Azdia & Joule, 2001; Losch &
Cacioppo, 1990; Simon etal., 1995, Experiment 1; see also
Chapanis & Chapanis, 1964). Although we agree that this
exclusion is conceptually in accordance with the idea of
manipulating dissonance, this is a potential methodologi-
cal limitation that introduces an important bias into the
experimental design, resulting in an increased likelihood
of a false positive (Ranganathan et al., 2016; Zhou &
Fishbach, 2016). Participants with the strongest attitudes
are more likely to be the ones to defy the instructions and
also less likely to change their attitude. Excluding these
participants leaves participants who are more likely to dis-
play attitude change in the expected direction. We thus
decided to keep all the participants in the primary analy-
ses (i.e., an intention-to-treat analysis). However, to be
consistent with prior studies, we also performed second-
ary analyses in which participants who refused to write
the essay or who were rated to be nonconforming were
excluded (i.e., per protocol analysis). Each argument was
evaluated by three raters blind to the choice instruc-
tions. They categorized participants as not compliant
when they wrote at least one pro-attitude argument or no
Advances in Methods and Practices in Psychological Science 7(1) 13
counterattitudinal argument in the counterattitudinal con-
ditions or no arguments in the neutral-essay condition.
Participants were excluded when at least two raters cate-
gorized the participant as noncompliant. In addition, the
raters evaluated the written arguments in HC-CE and
LC-CE conditions and rated their quality (reported variable
persuasiveness; 1 = poorly persuasive, 4 = very persuasive).
These results were recorded to allow them to be used for
potential exploratory analyses not included here.
Session characteristics. Each lab kept a log with ano-
nymized participant numbers and reported any unusual
events that occurred during the sessions. Participants who
were disturbed during the data-collection session for any
specific reason (e.g., phone interruption, alarm, computer
crash) were excluded.
Lab characteristics. Out of concerns for data quality,
labs were excluded from data analysis if they experienced
25% or more attrition because of session characteristics or
participant noncompliance. The number of participants
tested and attrition rates are reported in Table 3.
Data analysis
The analyses were divided into a manipulation check,
primary analyses, and secondary analyses. The main
analyses were performed on the aggregated data across
all samples and involved four one-tailed Welch’s t tests
(Delacre etal., 2017) comparing the HC-CE condition
with the two control conditions in both postessay atti-
tude only and attitude change. The secondary analyses
repeated the primary analyses on a data set in which
participants who defied the essay instructions had been
excluded. We also inspected lab variability in terms of
the main analyses and on a multi-item assessment of the
postessay attitude. Additional exploratory analyses
regarding two dissonance-related affect items are also
provided in the present article to further interpret the
results.
Power analysis
Our goal was to obtain an overall power of 95% for the
four primary analyses (see Maxwell, 2004). To determine
the required sample size, we ran a simulation-based
power analysis in which we repeatedly simulated the
data necessary to conduct the primary (one-tailed) anal-
yses. We then counted how frequently we observed a
significant effect in all four analyses and divided it by
the total number of simulations to assess the overall
power. Despite large effect sizes in the CDT literature
(Kenworthy etal., 2011), we assumed two small effect
sizes (J. Cohen, 1988) to be conservative: an effect size
of d = 0.20 for the difference between the HC-CE and
LC-CE conditions and a slightly larger effect size of d =
0.25 for the difference between HC-CE and HC-NE. An
additional required parameter for the power analysis is
the correlation between the pre- and postessay attitudes.
Because no information on this correlation was avail-
able, we calculated the power assuming a correlation of
.9 in the HC-NE condition, .8 in the LC-CE condition,
and .7 in the HC-CE condition, based on the expectation
that an effect of the manipulation reduces the correlation
between the pre- and postessay attitudes. Using the
GenOrd package (Ferrari & Barbiero, 2012), we simu-
lated Likert responses for each condition with the speci-
fied correlations and effect sizes across a range of
different sample sizes per condition. The results showed
that a sample size of 1,760 provided 95% power, with
660 participants in the HC-CE and LC-CE conditions and
440 participants in the HC-NE condition.
Results
Sample size and exclusions
A total of 39 laboratories contributed to the project, with
a grand total of 4,898 recruited participants. Table 3
presents the sample size and exclusions for each labora-
tory. Following our exclusion criteria, 902 were excluded
based on participant characteristics, and 112 were
excluded based on session characteristics (with some
overlap). We also excluded the data from one lab
because its attrition rate was greater than the preregis-
tered attrition limit. One of the control conditions for
one lab was also discarded because of an error in the
study materials. This resulted in a total of 38 labs and
total sample size of 3,822 for the primary analyses
involving the postessay attitude and a total of 37 labs
and 2,724 participants for the primary analyses involving
attitude change. For one of the secondary analyses,
regarding essay noncompliance, we excluded an addi-
tional 28 participants who refused to complete the essay
task and 362 nonconforming participants who failed to
follow the essay-task instructions.
Manipulation check
To see whether participants indeed experienced greater
choice freedom in the high-choice conditions, we con-
ducted two one-tailed Welch’s t tests comparing the
HC-CE condition and the HC-NE condition with the
LC-CE condition. The two t tests showed that the choice
manipulation was successful. Compared with the LC-CE
condition (M = 4.44, SD = 2.78), participants felt more
free to decline writing the essay in both the HC-CE con-
dition (M = 6.50, SD = 2.62), t(2,740.40) = 20.18, p < .001,
14 Vaidis et al.
Table 3. Exclusions and Attrition Rate per Lab University
Participant
characteristic exclusions
University Recruited
Not a
student
next year
Aware
of study
purpose
N after
initial
exclusions
Session
characteristic
exclusions
Essay
refusals
Attrition
rate
Essay
content
exclusions
Université Paris Cité 237 16 23 198 8 1 5% 14
Tilburg University 176 3 24 149 2 3 3% 6
Université Paul Valéry
Montpellier 3
105 13 4 89 2 4 7% 13
Appalachian State University 213 20 6 187 1 5 3% 16
University of Amsterdam 117 4 29 88 13 1 15% 8
Université Caen Normandie 169 24 6 141 7 5 6% 26
Aix-Marseille University 211 61 6 144 7 3 6% 49
University of Arizona 225 20 25 182 8 1 5% 24
University of Buffalo 242 19 20 209 11 6 7% 34
King’s College London 29 7 1 22 0 0 0% 3
University of Bergen 101 16 5 81 0 0 0% 3
Higher School of Economics
University
86 5 2 79 0 0 0% 17
Central Connecticut State
University
27 3 2 22 0 0 0% 2
University of Granada 194 6 8 180 8 1 4% 8
Southern Illinois University 117 6 5 106 3 0 3% 13
University of Toronto 131 13 3 115 4 7 9% 19
Université Grenoble Alpes 157 14 9 135 3 2 4% 8
Université de Poitiers 106 12 9 85 2 0 2% 0
Université de Genève 211 9 48 157 4 5 5% 19
Clemson University 127 6 10 112 0 2 2% 4
Université Clermont Auvergne 177 11 20 146 1 1 1% 19
Üsküdar University 107 18 4 86 4 1 5% 15
Université Rennes 2 105 10 33 65 1 1 2% 11
SWPS University of Social
Sciences and Humanities
189 7 13 169 3 2 3% 19
Université de Strasbourg 81 3 19 60 1 0 2% 16
Macquarie University 63 2 9 52 0 0 0% 5
Sungkyunkwan University 100 14 4 82 1 7 10% 9
Kyushu University 70 3 2 65 2 0 3% 17
Mus¸ Alparslan University 89 18 0 71 0 0 0% 48
Université Libre de Bruxelles 93 1 4 88 2 1 2% 13
University of Graz 66 10 10 47 0 0 0% 10
Ben-Gurion University 146 31 11 108 0 0 0% 2
Ural Federal University 84 4 2 78 0 1 1% 17
Sultan Qaboos Universitya153 77 0 76 8 40 55% 58
Florida State University 76 4 4 69 0 0 0% 4
Sabanci University 117 29 4 85 2 2 5% 12
Shahid Beheshti Universityb 89 3 10 76 1 6 9% 37
Erasmus University Rotterdam 50 0 7 43 1 2 7% 8
Nara Institute of Science and
Technology
62 3 10 49 2 1 6% 11
4,898 525 411 3,996 112 111 5% 617
Note. The attrition rate is the percentage of participants who were excluded based on the session characteristics and the participants’ refusal to
continue the study after the essay task introduction, which may overlap.
aOne lab was excluded from the analyses because its attrition rate was greater than 25%.
bData from the high-choice neutral essay condition were not included because of an error in the study materials.
Advances in Methods and Practices in Psychological Science 7(1) 15
d = 0.77, 95% CI = [0.69, 0.84], and the HC-NE condition
(M = 6.91, SD = 2.36), t(2,349.50) = 23.45, p < .001, d =
0.95, 95% CI = [0.86, 1.04].
Primary analyses
Our main analyses for determining the replicability of
the induced-compliance paradigm consisted of four one-
tailed Welch’s t tests on the aggregated samples. We
examined whether the experimental condition (HC-CE)
differed significantly from each of both control condi-
tions (LC-CE and HC-NE). First, we analyzed the postes-
say attitude assessment. Second, we analyzed attitude
change by subtracting the pre-essay attitude from the
postessay attitude (see Table 4).
Postessay attitude. In our test of the classic cognitive-
dissonance effect, we did not find a significant differ-
ence in postessay attitude between the HC-CE condition
(M = 2.60, SD = 1.91) and the LC-CE condition (M = 2.66,
SD = 1.99), t(2,751.46) = −0.79, p = .79, d = −0.03, 95%
CI = [–0.10, 0.04]. However, we did find a significant dif-
ference comparing the postessay attitude between the
HC-CE condition and the HC-NE condition, t(2,408.92) =
6.51, p < .001, d = 0.26, 95% CI = [0.18, 0.34]. Participants
in the HC-CE condition reported a more positive attitude
than participants in HC-NE condition (M = 2.14,
SD = 1.61).
Attitude change. Because of the inclusion of a pre-essay
attitude assessment in an earlier session, we could perform
an analysis testing whether the manipulation changed par-
ticipant attitudes from baseline. As in the postessay attitude
analysis, we did not observe a significant difference
between the HC-CE condition (M = 1.00, SD = 1.81) and
LC-CE condition (M = 0.97, SD = 1.80), t(1,969.56) = 0.38,
p = .35, d = 0.017, 95% CI = [−0.071, 0.11]. We did again
observe a significant difference between the HC-CE condi-
tion and HC-NE condition, t(1,725.20) = 6.72, p < .001, d =
0.31, 95% CI = [0.22, 0.41]. Participants in the HC-CE condi-
tion reported a more positive attitude change than partici-
pants in the HC-NE condition (M = 0.47, SD = 1.49).8
Secondary analyses
Essay-noncompliance exclusion. In the main analyses,
we included participants who did not comply with the
essay instructions (e.g., refused to write the essay). To
allow consistency with standard analysis in the cognitive-
dissonance literature, we repeated the main analyses while
excluding participants who did not comply with the essay
instructions. This led to the exclusion of 159 participants in
Table 4. Descriptive Statistics by Condition
High-Choice
Counterattitudinal Essay
Low-Choice
Counterattitudinal Essay
High-Choice
Neutral Essay
Primary analyses
Perceived choice 6.50 (2.62)
n = 1,448
4.44 (2.78)
n = 1,344
6.91 (2.36)
n = 1,032
Postessay attitude 2.60 (1.91)
n = 1,448
2.66 (1.99)
n = 1,343
2.14 (1.61)
n = 1,031
Pre-essay attitude 1.60 (1.23)
n = 1,062
1.66 (1.23)
n = 937
1.62 (1.28)
n = 727
Attitude change 1.00 (1.81)
n = 1,060
0.97 (1.80)
n = 937
0.47 (1.49)
n = 726
Secondary analyses
Postessay attitude 2.66 (1.89)
n = 1,290
2.63 (1.92)
n = 1,263
2.10 (1.56)
n = 882
Attitude change 1.05 (1.75)
n = 961
0.96 (1.77)
n = 892
0.50 (1.47)
n = 617
Four-item attitude 3.35 (1.54)
n = 1,447
3.40 (1.57)
n = 1,343
2.98 (1.35)
n = 1,031
Exploratory analyses
PANAS, uncomfortable 2.13 (1.22)
n = 1,449
2.16 (1.26)
n = 1,344
1.87 (1.12)
n = 1,032
PANAS, conflicted 2.61 (1.36)
n = 1,449
2.66 (1.38)
n = 1,344
1.89 (1.07)
n = 1,032
Note. Higher attitude numbers indicate an attitude more in favor of the policy. Attitude change was calculated by
subtracting the pre-essay attitude from the postessay attitude. PANAS = Positive and Negative Affect Schedule.
16 Vaidis et al.
HC-CE vs. LC-CE HC-CE vs. HC-NE
Overall
Université de Strasbourg
Cohen’s d (with 95% CI)
Higher School of Economics University
Erasmus University Rotterdam
King’s College London
Ural Federal University
Université Grenoble Alpes
Aix-Marseille Université
Shahid Beheshti University
Université Rennes 2
Mu¸s Alparslan University
University of Arizona
Kyushu University
Tilburg University
Université Paris Cité
Üsküdar University
University at Buffalo
Université de Poitiers
Université Paul Valéry Montpellier 3
Southern Illinois University
Université Clermont Auvergne
University of Toronto
University of Granada
Nara Institute of Science and Technology
Clemson University
Sungkyunkwan University
Central Connecticut State University
Université Caen Normandie
Florida State University
Université Libre de Bruxelles
University of Graz
Sabanci University
University of Amsterdam
Appalachian State University
SWPS University of Social Sciences and Humanities
University of Bergen
Université de Genève
Macquarie University
Ben-Gurion University
−1.0 1.0−0.50.50.0 −1.
01
.0−0.50.50.0
Fig. 1. Overall comparisons for each lab effect sizes (Cohen’s d) on postessay attitude between high choice and
low choice and between counterattitudinal essay and neutral essay. A positive Cohen’s d represents a more positive
attitude mean in the high-choice counterattitudinal essay condition, thus supporting the hypotheses.
Advances in Methods and Practices in Psychological Science 7(1) 17
the HC-CE condition (11%), 80 in the LC-CE condition
(6%), and 151 in the HC-NE condition (15%).
As in the primary analysis, the HC-CE condition
(M = 2.66, SD = 1.89) did not significantly differ from
the LC-CE condition (M = 2.63, SD = 1.92), t(2,547.56) =
0.38, p = .35, d = 0.015, 95% CI = [–0.063, 0.093]. The
HC-CE condition again differed significantly from the
HC-NE condition, t(2,095.19) = 7.43, p < .001, d =
0.31, 95% CI = [0.23, 0.40]. Participants in the HC-CE
condition reported a more positive attitude compared
with participants in the HC-NE condition (M = 2.10,
SD = 1.56).
Regarding attitude change, we replicated the same pat-
tern of results. The HC-CE condition (M = 1.05, SD = 1.75)
did not significantly differ from the LC-CE condition (M =
0.96, SD = 1.77), t(1,837.21) = 1.04, p = .15, d = 0.048,
95% CI = [−0.043, 0.14], but did differ significantly from
the HC-NE condition (M = 0.50, SD = 1.47), t(1,467.82) =
6.72, p < .001, d = 0.33, 95% CI = [0.23, 0.44].
Lab variability. Lab variability was investigated by
comparing a linear mixed model with by-lab random
intercepts to a linear mixed model with both by-lab ran-
dom intercepts and by-lab random slopes for the effects.
In each model, the first postessay attitude measure was
regressed on the experimental conditions, which were
included as dummy-coded fixed effects with the HC-CE
condition as the reference group. Using analysis of vari-
ance, we found no significant difference in model fit when
the two models were compared, χ2(5) = 10.19, p = .070.
Thus, we did not find statistically significant heterogeneity
of the effect of the experimental conditions across the dif-
ferent laboratories (also see Fig. 1).
Four-item attitude assessment. In addition to the
single-item measure of attitude analyzed above, attitude
was also assessed with three additional items after the
initial postessay attitude item for increased reliability. We
computed a new attitude score by averaging four items
(Cronbach’s a = .87, McDonald’s ω = .88) and repeated
the postessay attitude analyses. Again, these analyses
showed the same pattern of results. There was no signifi-
cant difference in attitude between participants in the
HC-CE condition (M = 3.35, SD = 1.54) and participants in
the LC-CE condition (M = 3.40, SD = 1.57), t(2,761.20) =
−0.86, p = .81, d = −0.033, 95% CI = [–0.11, 0.042]. There
was, again, a significant difference in attitude between
participants in the HC-CE condition and HC-NE condi-
tion, t(2,367.28) = 6.37, p < .001, d = 0.25, 95% CI = [0.17,
0.33]. Participants in the HC-CE condition reported a more
positive attitude compared with participants in the HC-NE
condition (M = 2.98, SD = 1.35). See the supplemental
material on OSF for additional analyses.
Exploratory analyses of affect
Neither the primary nor the secondary analyses showed
the predicted effect of choice on attitude (i.e., no sig-
nificant difference between HC-CE and LC-CE condi-
tions) but showed only an effect of writing an
attitude-inconsistent essay (i.e., HC-CE condition was
significantly greater than HC-NE condition). We therefore
decided to run additional analyses to help interpret these
results. One important question was whether we could
find evidence of subjective conflict or discomfort that
would suggest our manipulation successfully induced a
cognitive-dissonance state in the counterattitudinal-essay
conditions, particularly in the high-choice condition.
Participants completed a PANAS, which included two
additional items (i.e., uncomfortable and conflicted) to
specifically assess a cognitive-dissonance state. We per-
formed two separate Welch’s t tests to compare differ-
ences on these items between the conditions.
As in the primary analyses, we did not find a signifi-
cant difference on the uncomfortable item between the
HC-CE condition (M = 2.13, SD = 1.22) and the LC-CE
condition (M = 2.16, SD = 1.26), t(2,761.87) = −0.72, p =
.47, d = −0.027, 95% CI = [–0.10, 0.047]. Comparing the
uncomfortable feeling in the HC-CE condition and the
HC-NE condition, we found a significant effect,
t(2,325.89) = 5.44, p < .001, d = 0.22, 95% CI = [0.14,
0.30]. Participants in the HC-CE condition reported being
more uncomfortable than participants in HC-NE condi-
tion (M = 1.87, SD = 1.12). The difference between the
LC-CE condition and the HC-NE condition was also sig-
nificant, t(2,320) = 5.97, p < .001, d = 0.24, 95% CI =
[0.16, 0.32]. These results suggest that participants in the
conditions containing an inconsistency (HC-CE and
LC-CE) felt more uncomfortable while writing the essay
compared with the neutral-essay condition.
The same pattern was observed on the item about
feeling conflicted. There was no significant difference
between the HC-CE condition (M = 2.61, SD = 1.36) and
LC-CE condition (M = 2.66, SD = 1.38, t(2,765.24) = −1.01,
p = .31, d = 0.038, 95% CI = [0.11, 0.036]). However, there
was a significant difference between the HC-CE condi-
tion and the HC-NE condition, t(2,454.28) = 14.72, p <
.001, d = 0.58, 95% CI = [0.49, 0.66]. Participants in the
HC-CE condition reported feeling more conflicted than
participants in the HC-NE condition (M = 1.89, SD =
1.07). The difference between the LC-CE condition and
the HC-NE condition was also significant, t(2,373.96) =
15.29, p < .001, d = 0.61, 95% CI = [0.53, 0.71]. These
results suggest that the conditions with an inconsistency
(HC-CE and LC-CE) generated more conflict. Together,
these two analyses show that writing a counterattitudinal
essay elicits feelings akin to a dissonance state.
18 Vaidis et al.
We conducted additional analyses to test whether the
affective experience mediated the relationship between
writing a counterattitudinal essay versus a neutral essay
on the postessay attitude. We analyzed only the differ-
ence in attitude between the HC-CE condition and the
HC-NE condition to focus on the effect of inconsistency.
A crucial test of the mediation process is that the effect
of condition diminishes when the affective measure is
included in the model. Without including the affective
measure, we observed a significant difference between
the two conditions (b = −0.46, 95% CI = [–0.61, –0.32]).
Including the affective measures in separate models
increased the difference between conditions (uncomfort-
able: b = −0.51, 95% CI = [–0.65, –0.37]; conflicted: b =
−0.63, 95% CI = [–0.77, –0.48]). Relatedly, we observed
negative relationships between the affective state and
the postessay attitude in the HC-CE condition, uncom-
fortable: b = −0.28, SE = 0.040), t(1,445) = −6.80, p <
.001; conflicted: b = −0.32, SE = 0.036, t(1,445) = −8.95,
p < .001. In the HC-NE condition, the relationships
between affect and attitude were not significant, uncom-
fortable: b = −0.029, SE = 0.045, t(1,029) = −0.65, p = .52;
conflicted: b = −0.012, SE = 0.047, t(1,029) = −0.26, p =
.80. These results do not support a mediation process
consisting of inconsistency producing feelings of dis-
comfort and conflict that motivate positive attitude
change.
Additional exploratory analyses plan
In this article, we mainly presented the results of the
primary and secondary analyses. The data and the code
needed to reproduce the reported analyses are available
on OSF. One third of the full data set, including all
recorded measures from the study, is currently available
for researchers to explore the data. The full data set will
be made available within 4 years after article publication.
We encourage researchers to preregister an analysis plan
(possibly based on exploratory analysis of the first third
of the data set) before the full data set is made public
(for a similar procedure, see Klein etal., 2014; or Klein
et al., 2018).
Discussion
In the present study, we aimed to corroborate the
induced-compliance paradigm of CDT by replicating a
modified version of a carefully chosen instance of this
paradigm (Croyle & Cooper, 1983, Experiment 1). Almost
4,900 participants from 39 laboratories across 19 coun-
tries were recruited to participate in an in-person lab
study to perform a counterattitudinal-essay task, making
this the largest CDT study to date. In each lab, partici-
pants were informed that an unpopular policy change
was being considered at their university (in most labs,
raising the tuition). After being informed about this pol-
icy change, they were instructed to provide arguments
(i.e., an essay). One third of the participants were
directly instructed to write a counterattitudinal essay in
favor of the policy (LC-CE condition). One third of par-
ticipants received the same instructions but were
reminded that they were free to refuse to write the essay
(HC-CE condition). A final third was asked to provide
arguments for other policies to be examined, under high
choice (HC-NE condition). The attitude toward the essay
topic was assessed just after writing the essay, and for a
subset of the participants, the same attitude was also
assessed in a previous session. According to CDT, we
predicted that participants in the high-choice counterat-
titudinal condition would report a more positive attitude
than participants in the low-choice condition. The neu-
tral-essay condition constituted an additional control
condition with an improved operationalization of incon-
sistency. We predicted participants would likewise report
a more positive attitude in the high-choice counteratti-
tudinal condition compared with the neutral-essay
condition.
A vital component of the induced-compliance para-
digm is that participants in the high-choice conditions
experience more choice than participants in the low-
choice condition so that the counterattitudinal action
cannot readily be ascribed to the experimental context,
which would otherwise prevent, reduce, or eliminate
any cognitive dissonance. Our manipulation of choice
was successful—participants in the high-choice condi-
tions reported feeling more free to decline writing the
essay than participants in the low-choice condition.
Yet despite various checks that the experiment was
conducted successfully, we failed to replicate the classic
dissonance effect. Across multiple analyses, we found
that writing a counterattitudinal essay produced a more
favorable attitude toward the essay topic than writing a
neutral essay, but this occurred regardless of choice
freedom. In other words, the amount of choice that
participants experienced when writing the essay did not
affect their subsequent attitude to the essay topic.
Instead, differences in attitudes were observed only
between writing counterattitudinal arguments and writ-
ing arguments for other policies (i.e., noncounterattitu-
dinal). Participants became more in favor of the essay
topic after writing counterattitudinal arguments. This
pattern of results was robust across analyses involving
a single attitude item, an average across multiple attitude
items, and a change in attitude.
The lack of an effect of choice on attitude in our
multilab replication was unexpected. This result is incon-
sistent with the long-standing proposition in cognitive-
dissonance studies that choice is a key factor for attitude
Advances in Methods and Practices in Psychological Science 7(1) 19
change (see McGrath, 2017; Vaidis & Bran, 2018). The
current observations are contrary to the expectations
drawn from this paradigm and contradict prominent
theoretical perspectives on CDT (e.g., Beauvois & Joule,
1982, 1996; Cooper, 2007; Cooper & Fazio, 1984).
According to these perspectives, choice freedom was
considered as the essential component to produce atti-
tude change (e.g., Brehm & Cohen, 1962; Linder etal.,
1967). Our current replication results suggest that choice
may not be necessary—or might be necessary but not
sufficient—to produce attitude change when using the
induced-compliance paradigm.
We could also argue that the results should not be
entirely surprising. Several seminal induced-compliance
studies have produced effect sizes that could be argued
to be unrealistically large (e.g., ds > 1.5; Elliot & Devine,
1994; Simon etal., 1995) while having small samples
(i.e., around 20 participants per cell). In addition, classic
cognitive-dissonance designs were extensively criticized
in the 1960s (see Chapanis & Chapanis, 1964), in which
important methodological limitations and inadequacies
in the statistical analyses were pointed out. Finally, the
replication crisis in psychology has shown that even
established findings in the psychological literature can
fail to be reliably demonstrated. It is thus possible that
typical cognitive-dissonance findings, as they were and
are commonly studied, may fall in this category as well.
Despite failing to replicate the classic effect that cog-
nitive dissonance requires experiencing a freedom of
choice, we did find that participants who wrote a coun-
terattitudinal essay reported a more positive attitude
than participants who wrote a neutral essay. A straight-
forward interpretation could be that this is caused by
the inconsistency manipulation, which would support
Festinger’s (1957, 2019) original argument that cognitive
dissonance is first and foremost about inconsistency.
This also fits with the most recent perspective on CDT
(e.g., Gawronski, 2012; Harmon-Jones, 1999, 2019),
which gives a more central role to inconsistency than
to choice freedom. Our findings could be interpreted
to support this account of cognitive dissonance, although
there are several potential alternative explanations.
The finding that participants reported a more positive
attitude toward the topic after giving arguments in favor
could be due to a self-perception process (Bem, 1965,
1967). This explanation has, historically, been widely
debated in the literature on cognitive dissonance (e.g.,
Beauvois & Joule, 1982; Fazio etal., 1977; Greenwald,
1975) and has profoundly influenced the development
of CDT (Cooper, 2007; Vaidis & Bran, 2019). However,
self-perception theory does not involve negative affect
or cognitive conflict. We found that participants in
the counterattitudinal conditions experienced more dis-
comfort and more conflict than participants in the
neutral-essay condition. This finding contradicts an
explanation in terms of self-perception processes.
Another alternative explanation for the observed
effects is that they stem from self-persuasion processes.
Generating one’s own arguments, even when experimen-
tally directed (Killeya & Johnson, 1998), can produce
attitude change (e.g., Baldwin etal., 2013), particularly
if people have an easy time generating the arguments
(Xu & Wegener, 2023). It is plausible that participants in
the counterattitudinal-essay conditions simply persuaded
themselves after spending time generating reasons in
favor of the essay topic. This explanation has also been
a subject of long-standing debate in the dissonance lit-
erature (e.g., Cohen etal., 1959; Brehm & Cohen, 1962).
Although our experiment may not be able to definitively
rule out this alternative explanation, we note that self-
persuasion processes do not require the emotional
arousal or a sense of conflict posited in CDT. Therefore,
even though both CDT and self-persuasion could poten-
tially yield the same effect on attitude, CDT appears to
account for the motivational component.
Reconsidering the induced-compliance
paradigm
The induced-compliance paradigm of CDT posits that
providing individuals with the choice to produce a coun-
terattitudinal writing would create a state of cognitive
dissonance, motivating them to change their attitude to
align with their behavior (Cooper, 2007). According to
our manipulation check, the procedure was correctly
implemented: Participants did perceive more choice
freedom and wrote arguments about a counterfactual
topic. However, the results did not align with the
expected outcomes. This raises doubts about the validity
of the induced-compliance paradigm to elicit cognitive
dissonance, assuming there are no substantial critiques
of our study.
One critique of our study could be directed at the low
control over how each lab executed the procedure
(Ellefson & Oppenheimer, 2022). This possibility has only
limited merit given that we standardized many of the
materials, including procedural scripts of the experi-
menter-participant interaction, to reduce this possibility
and maximize the validity of the procedure. Nevertheless,
there could have been some variation in the exact word-
ing or expression of the experimenters while interacting
with the participant, although we note that multiple labs
involved in this project have a significant track record of
conducting dissonance studies and have successfully
shown the effect in the past. Note also that we did not
observe significant heterogeneity between labs in finding
the effect. The inclusion of experienced labs and lack of
heterogeneity speak against explaining the failure to
20 Vaidis et al.
replicate the classic dissonance effect in terms of low
control over how each lab executed the procedure.
One could also note that this study was performed in
the context of a global pandemic, during which most
laboratories performed the experiment with health-safety
precautions (e.g., wearing masks, increased distance). In
response, we point out that these precautions had already
become a norm for almost a year when the data collec-
tion began in most labs. Mask wearing specifically might
even have served to some extent in neutralizing facial-
expression variations during interactions. Thus, it is chal-
lenging to explain the results and the absence of effect
as due to any of the above circumstances.
Reexploring the role of inconsistency
Aside from results related to the effect of choice freedom
on attitude change in the induced-compliance paradigm,
there are several other results of this replication study
that are worth highlighting. Our study showed that coun-
terattitudinal essay writing had an effect on attitude—
participants who wrote a counterattitudinal essay
reported a more positive attitude compared with partici-
pants who wrote a neutral essay. Although the effect
size was small, it appeared to be robust despite various
methodological constraints we maintained in this repli-
cation procedure. For instance, our study had a double-
blind procedure regarding the high-choice conditions to
ensure that experimenters’ expectations could not
explain the difference between these two conditions.
The effect of counterattitudinal writing on attitude
change reaffirms the role of inconsistency in the dis-
sonance process.
We conducted exploratory analyses to further under-
stand the effects of counterattitudinal writing and found
that participants in the counterattitudinal conditions
reported feeling more discomfort and conflict while writ-
ing the essay. We also observed that the degree of dis-
comfort and conflict was negatively related to the
postessay attitude in the high-choice counterattitudinal
condition, and we did not find evidence of a mediating
influence of reported discomfort or conflict in the incon-
sistency-attitude relationship.
Some of the results of the exploratory analyses may
be seen as incompatible with CDT. We exercise caution
in drawing premature conclusions about whether these
results support or contradict predictions from CDT for
two reasons. First, our study was not specifically designed
to capture the role of affect in the dissonance process.
Second, the exact relationship between affect and atti-
tude change is subject to debate in the literature, ranging
from a positive relationship stemming from a mediation
process (e.g., Devine etal., 2019) to a potentially nega-
tive relationship because of emotional reappraisal (e.g.,
Cancino-Montecinos et al., 2018), or when affect is
assessed following attitude change (e.g., Elliot & Devine,
1994, Study 2), or even to no relationship (e.g., Proulx,
2018). Disentangling these different processes can prove
challenging, and even among dissonance theorists, con-
sensus may be elusive regarding the appropriate meth-
odology and ensuing interpretations.
Conclusion
The induced-compliance paradigm with a counterattitu-
dinal-essay task was used to examine whether a state of
cognitive dissonance can produce a change in attitude.
This paradigm has been fundamental for CDT. Despite a
large sample and an improved methodology, we did not
observe the expected effect of choice on attitude. We did
observe that performing counterattitudinal behavior,
regardless of perceived choice, affected attitudes.
These findings prompted us to draw several conclu-
sions. Freedom of choice, typically considered to be a
vital component of the induced-compliance paradigm,
does not reliably produce attitude change. Consequently,
this raises serious doubts about previous findings that
stem from this paradigm and certain interpretations of
CDT, specifically those that have emphasized the impor-
tance of choice (e.g., Beauvois & Joule, 1996; Cooper,
2007). Consistent with the core principles of CDT, writ-
ing a counterattitudinal essay did lead to attitude change.
Additional exploratory analyses revealed that writing a
counterattitudinal essay also produced more discomfort
and conflict. These results are consistent with CDT,
although our results regarding the relationship between
affect and attitude change were less clearly in support
of the theory. All in all, further theoretical and empirical
work is necessary to determine whether these findings
align with versions of CDT focused on inconsistency
(e.g., Gawronski, 2012; Harmon-Jones, 2019) or are bet-
ter explained by alternative theories.
Transparency
Action Editor: Alexa M. Tullet
Editor: David A. Sbarra
Author Contribution(s)
D. C. Vaidis and W. W. A. Sleegers shared first authorship.
David C. Vaidis: Conceptualization; Data curation; Formal
analysis; Investigation; Methodology; Project administration;
Supervision; Validation; Writing – original draft; Writing –
review & editing.
Willem W. A. Sleegers: Conceptualization; Data curation;
Formal analysis; Investigation; Methodology; Project admin-
istration; Software; Supervision; Validation; Visualization;
Writing – original draft; Writing – review & editing.
Florian van Leeuwen: Investigation; Methodology;
Writing – review & editing.
Kenneth G. DeMarree: Methodology; Supervision; Valida-
tion; Writing – review & editing.
Advances in Methods and Practices in Psychological Science 7(1) 21
Bjørn Sætrevik: Investigation; Methodology; Supervision;
Validation; Writing – review & editing.
Robert M. Ross: Investigation; Methodology; Supervision;
Validation; Writing – review & editing.
Kathleen Schmidt: Investigation; Methodology; Supervi-
sion; Validation; Writing – review & editing.
John Protzko: Investigation; Methodology; Supervision;
Validation; Writing – review & editing.
Coby Morvinski: Investigation; Supervision; Validation;
Writing – review & editing.
Omid Ghasemi: Investigation; Writing – review &
editing.
Andrew J. Roberts: Investigation; Methodology; Writing –
review & editing.
Jeff Stone: Investigation; Methodology; Supervision; Vali-
dation; Writing – review & editing.
Alexandre Bran: Methodology; Writing – review &
editing.
Amélie Gourdon-Kanhukamwe: Investigation; Method-
ology; Supervision; Validation; Writing – review &
editing.
Ceren Gunsoy: Investigation; Writing – review & editing.
Lisa S. Moussaoui: Investigation; Writing – review &
editing.
Andrew R. Smith: Investigation; Supervision; Validation;
Writing – review & editing.
Armelle Nugier: Investigation; Supervision; Validation;
Writing – review & editing.
Marie-Pierre Fayant: Methodology; Writing – review &
editing.
Ali H. Al-Hoorie: Investigation; Writing – review &
editing.
Obed K. Appiah: Investigation.
Spencer Arbige: Investigation.
Benjamin Aubert-Teillaud: Investigation.
Olga Bialobrzeska: Investigation; Supervision; Validation;
Writing – review & editing.
Stéphanie Bordel: Investigation.
Valerian Boudjemadi: Investigation.
Hilmar Brohmer: Investigation.
Quinn Cabooter: Investigation.
Mehdi Chahir: Investigation.
Ianis Chassang: Investigation.
Armand Chatard: Investigation; Writing – review &
editing.
Yu Yang Chou: Investigation; Supervision; Validation.
Sungeun Chung: Investigation; Supervision; Validation.
Mioara Cristea: Investigation; Supervision; Validation;
Writing – review & editing.
Joséphine Daga: Investigation.
Gregory J. Depow: Investigation; Supervision; Validation;
Writing – review & editing.
Olivier Desrichard: Supervision.
Dmitrii Dubrov: Investigation; Supervision; Validation;
Writing – review & editing.
Thomas R. Evans: Data curation; Writing – review &
editing.
Séverine Falkowicz: Investigation.
Sylvain Ferreira: Investigation.
Tim Figureau: Investigation.
Valérie Fointiat: Investigation.
Théo Friedrich: Investigation.
Anastasia Gashkova: Investigation.
Fabien Girandola: Investigation; Supervision; Validation.
Marine Granjon: Investigation; Supervision; Validation.
Dmitry Grigoryev: Investigation; Supervision; Validation;
Writing – review & editing.
Gul Gunaydin: Investigation; Supervision; Validation;
Writing – review & editing.
Şevval Güzel: Investigation.
Mahsa Hazrati: Investigation; Supervision; Validation.
Mai Helmy: Investigation; Writing – review & editing.
Ayumi Ikeda: Investigation.
Michael Inzlicht: Investigation; Supervision; Validation.
Sara Jaubert: Investigation; Supervision; Validation; Writ-
ing – review & editing.
Dauren Kasanov: Investigation.
Mohammad Mohsen Khoddami: Investigation; Supervi-
sion; Validation.
Taenyun Kim: Investigation.
Kiyoshi Kiyokawa: Investigation.
Rabia I. Kodapanakkal: Investigation.
Alexandra Kosachenko: Investigation; Validation;
Visualization.
Kortney Maedge: Investigation.
John H. Mahaney: Investigation.
Marie-Amélie Martinie: Investigation; Writing – review &
editing.
Vitor N. Mascheretti: Investigation.
Yoriko Matsuda: Investigation.
Maxime Mauduy: Investigation; Supervision; Validation;
Writing – review & editing.
Nicolas Mauny: Investigation; Supervision; Validation.
Armand Metzen: Investigation.
Eva Moreno-Bella: Investigation.
Miguel Moya: Investigation; Supervision; Validation.
Kévin Nadarajah: Investigation; Supervision; Validation.
Pegah Nejat: Investigation; Supervision; Validation.
Elisabeth Norman: Investigation; Supervision; Validation;
Writing – review & editing.
Irmak Olcaysoy Okten: Investigation; Supervision; Vali-
dation; Writing – review & editing.
Asil A. Özdoğru: Investigation; Supervision; Validation;
Writing – review & editing.
Ceyda Ozer: Investigation; Supervision; Validation.
Elena Padial-Rojas: Investigation.
Yuri G. Pavlov: Investigation; Software; Supervision; Writ-
ing – review & editing.
Monica Perusquia-Hernandez: Investigation; Supervision;
Validation; Writing – review & editing.
Dora Proost: Investigation.
Aleksandra Rabinovitch: Investigation.
Odile Rohmer: Investigation.
Emre Selcuk: Investigation; Supervision; Validation.
Cécile Sénémeaud: Investigation.
Yaniv Shani: Investigation.
Elena A. Shmeleva: Investigation.
Emmelie Simoens: Investigation.
22 Vaidis et al.
Kaitlin A. Smith: Investigation; Supervision; Validation.
Alain Somat: Investigation.
Hayeon Song: Investigation; Supervision; Validation.
Fatih Sonmez: Investigation.
Lionel Souchet: Investigation.
John J. Taylor: Investigation; Supervision; Validation; Writ-
ing – review & editing.
Ilja van Beest: Investigation; Supervision; Validation; Writ-
ing – review & editing.
Nicolas Van der Linden: Investigation; Writing – review
& editing.
Steven Verheyen: Investigation; Supervision; Validation;
Writing – review & editing.
Bruno Verschuere: Supervision; Validation; Writing –
review & editing.
Kevin Vezirian: Investigation; Supervision; Validation;
Writing – review & editing.
Luc Vieira: Investigation; Supervision; Validation; Writing –
review & editing.
Sera Wiechert: Investigation; Supervision; Validation.
Guillermo B. Willis: Investigation; Supervision; Validation.
Robin Wollast: Investigation.
Ji Xia: Investigation; Supervision; Validation; Writing –
review & editing.
Yuki Yamada: Investigation; Supervision; Validation; Writ-
ing – review & editing.
Naoto Yoshimura: Investigation.
Daniel Priolo: Investigation; Methodology; Project admin-
istration; Supervision; Validation; Writing – review &
editing.
Declaration of Conflicting Interests
The author(s) declare that there were no conflicts of interest
with respect to the authorship or the publication of this
article.
Funding
R. M. Ross was supported by a Macquarie University
Research Fellowship and the John Templeton Foundation
(Grant ID: 62631). E. A. Shmeleva was supported by a grant
from the Russian Science Foundation (Grant ID: 22-18-
00678, IvSU). D. Dubrov and D. Grigoryev were supported
within the Basic Research Program at the National Research
University Higher School of Economics. Y. Yamada was
supported by JSPS KAKENHI (JP18K12015, JP20H04581,
JP21H03784, and JP22K18263).
Open Practices
This article has received the badges for Open Data, Open
Materials, and Preregistration. More information about the
Open Practices badges can be found at http://www.psy
chologicalscience.org/publications/badges.
ORCID iDs
David C. Vaidis https://orcid.org/0000-0002-1954-2219
Florian van Leeuwen https://orcid.org/0000-0002-9694-
8300
Kenneth G. DeMarree https://orcid.org/0000-0001-5815-
2646
Bjørn Sætrevik https://orcid.org/0000-0002-9367-6987
Robert M. Ross https://orcid.org/0000-0001-8711-1675
Kathleen Schmidt https://orcid.org/0000-0002-9946-5953
John Protzko https://orcid.org/0000-0001-5710-8635
Coby Morvinski https://orcid.org/0000-0001-8081-7085
Omid Ghasemi https://orcid.org/0000-0001-7511-5580
Amélie Gourdon-Kanhukamwe https://orcid.org/0000-
0002-3060-1320
Lisa S. Moussaoui https://orcid.org/0000-0003-0392-7402
Andrew R. Smith https://orcid.org/0000-0001-5302-3343
Ali H. Al-Hoorie https://orcid.org/0000-0003-3810-5978
Stéphanie Bordel https://orcid.org/0000-0001-7784-0217
Ianis Chassang https://orcid.org/0000-0002-1094-3331
Armand Chatard https://orcid.org/0000-0003-4823-2903
Yu Yang Chou https://orcid.org/0009-0002-3988-0917
Sungeun Chung https://orcid.org/0000-0002-9337-134X
Gregory J. Depow https://orcid.org/0000-0001-9995-4143
Olivier Desrichard https://orcid.org/0000-0003-3269-8813
Dmitrii Dubrov https://orcid.org/0000-0001-8146-4197
Thomas R. Evans https://orcid.org/0000-0002-6670-0718
Sylvain Ferreira https://orcid.org/0009-0007-3999-016X
Tim Figureau https://orcid.org/0009-0001-7535-284X
Dmitry Grigoryev https://orcid.org/0000-0003-4511-7942
Gul Gunaydin https://orcid.org/0000-0003-0490-4528
Ayumi Ikeda https://orcid.org/0000-0002-1688-2875
Alexandra Kosachenko https://orcid.org/0000-0001-8896-
3837
Kévin Nadarajah https://orcid.org/0000-0003-0338-3732
Pegah Nejat https://orcid.org/0000-0003-1410-9720
Irmak Olcaysoy Okten https://orcid.org/0000-0001-6369-
0879
Asil A. Özdoğru https://orcid.org/0000-0002-4273-9394
Ceyda Ozer https://orcid.org/0009-0009-5846-0492
Yuri G. Pavlov https://orcid.org/0000-0002-3896-5145
Monica Perusquia-Hernandez https://orcid.org/0000-
0002-0486-1743
Aleksandra Rabinovitch https://orcid.org/0000-0002-4773-
7814
Emre Selcuk https://orcid.org/0000-0002-2955-4221
Yaniv Shani https://orcid.org/0000-0001-8777-8162
Kaitlin A. Smith https://orcid.org/0000-0002-2449-2009
Alain Somat https://orcid.org/0000-0003-1415-1777
Fatih Sonmez https://orcid.org/0000-0002-4054-0269
Lionel Souchet https://orcid.org/0000-0002-5620-2790
Nicolas Van der Linden https://orcid.org/0000-0002-1288-
948X
Bruno Verschuere https://orcid.org/0000-0002-6161-4415
Kevin Vezirian https://orcid.org/0000-0003-4013-7725
Sera Wiechert https://orcid.org/0000-0002-0260-1702
Robin Wollast ] https://orcid.org/0000-0001-5395-9969
Yuki Yamada https://orcid.org/0000-0003-1431-568X
Daniel Priolo https://orcid.org/0000-0003-3430-3459
Advances in Methods and Practices in Psychological Science 7(1) 23
Acknowledgments
We would like to thank Jasmin Koglek for their assistance in
correcting an error in the data analysis script.
Notes
1. The procedure called “forced compliance” was initially used to
challenge the reinforcement studies (Kelman, 1953) and to show
that the degree of reward for counterattitudinal advocacy has an
inverse effect on attitude change. However, because of alternative
explanations, the paradigm shifted to the use of choice instead
of rewards to manipulate dissonance (e.g., Linder etal., 1967).
2. Converted from a reported F value of 38.86 with 1 and 27
degrees of freedom using the effectsize R package (Ben-Shachar
etal., 2020).
3. Converted from a reported effect size of r = .248 using the
effectsize R package (Ben-Shachar etal., 2020).
4. The topics, procedure, and results of the pretest are available
on OSF (https://osf.io/mgjh8/).
5. This calculation is based on the Qualtrics time-stamp function
and could be inaccurate depending on the final validation of the
survey by the participant (with an automatic closure of a survey
after 1 week). All the data are available for exploratory purposes.
6. From Croyle and Cooper (1983). Adapted phrasings were
also proposed to fit with sessions with multiple participants. For
details of the material in each language, see OSF.
7. A part of the literature (e.g., Elliot & Devine, 1994, Experiment
1; Jonas etal., 2014; Proulx & Inzlicht, 2012) suggests that a delay
could have an impact on the regulation process of CDT. Most of
the dissonance literature has not included a delay. The time for
option selection, page submission, and the subsequent measures
were recorded for exploratory purposes.
8. All three conditions displayed a significant positive attitude
change from the first session to the second session: HC-CE:
t(1,060) = 17.97, p < .001; LC-CE: t(936) = 16.50, p < .001; HC-NE:
t(725) = 8.53, p < .001. We also reanalyzed the primary analy-
ses and included an interaction test between condition and
whether the participant completed the premeasure. A significant
interaction would indicate that the presence or absence of the
premeasure affected the difference in attitudes between condi-
tions. No significant interactions were found (HC-CE vs. LC-CE:
b = −0.098, SE = 0.16, t(2,787) = −0.60, p = .55; HC-CE vs. HC-NE:
b = −0.13, SE = 0.16, t(2,475) = −0.79, p = .43), meaning there is
no evidence to suggest that the premeasure affected the impact
of the manipulation.
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