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Preference-Matching Worldwide 1
RUNNING HEAD: PREFERENCE-MATCHING WORLDWIDE
Date: July 3, 2024
A Worldwide Test of the Predictive Validity of
Ideal Partner Preference-Matching
Paul W. Eastwick1†, Jehan Sparks2, Eli J. Finkel3,4, Eva M. Meza1, Matúš Adamkovič5,6,7, Peter
Adu8, Ting Ai9, Aderonke A. Akintola10, Laith Al-Shawaf11,12, Denisa Apriliawati13, Patrícia
Arriaga14, Benjamin Aubert-Teillaud15, Gabriel Baník16, Krystian Barzykowski17, Carlota
Batres18, Katherine J. Baucom19, Elizabeth Z. Beaulieu19, Maciej Behnke20,21, Natalie Butcher22,
Deborah Y. Charles23, Jane M. Chen24, Jeong Eun Cheon25, Phakkanun Chittham26, Patrycja
Chwiłkowska20, Chin Wen Cong27, Lee T. Copping22, Nadia S. Corral-Frias28, Vera Ćubela
Adorić29, Mikaela Dizon30, Hongfei Du31, Michael I. Ehinmowo32, Daniela A. Escribano9,
Natalia M. Espinosa33, Francisca Expósito34, Gilad Feldman35, Raquel Freitag36, Martha Frias
Armenta28, Albina Gallyamova37, Omri Gillath9, Biljana Gjoneska38, Theofilos Gkinopoulos39,
Franca Grafe40, Dmitry Grigoryev37, Agata Groyecka-Bernard41, Gul Gunaydin42, Ruby
Ilustrisimo43, Emily Impett44, Pavol Kačmár45, Young-Hoon Kim25, Mirosław Kocur46, Marta
Kowal46, Maatangi Krishna47, Paul Danielle Labor48, Jackson G. Lu49, Marc Y. Lucas28,
Wojciech Małecki50, Klara Malinakova51, Sofia Meißner40, Zdeněk Meier51, Michal Misiak46,52,
Amy Muise53, Lukas Novak51, Jiaqing O54, Asil A. Özdoğru55,56, Haeyoung Gideon Park44,
Mariola Paruzel20, Zoran Pavlović57, Marcell Püski58, Gianni Ribeiro59,60, S. Craig Roberts61,46,
Jan P. Röer40, Ivan Ropovik62,63, Robert M. Ross64, Ezgi Sakman65, Cristina E. Salvador33, Emre
Selcuk42, Shayna Skakoon-Sparling66, Agnieszka Sorokowska41, Piotr Sorokowski46, Ognen
Spasovski67, Sarah C. E. Stanton30, Suzanne L. K. Stewart68, Viren Swami69,70, Barnabas
Szaszi58, Kaito Takashima71, Peter Tavel51, Julian Tejada36, Eric Tu53, Jarno Tuominen72, David
Vaidis15, Zahir Vally73, Leigh Ann Vaughn74, Laura Villanueva-Moya34, Dian Wisnuwardhani75,
Yuki Yamada71, Fumiya Yonemitsu76, Radka Žídková51, Kristýna Živná51, Nicholas A. Coles77
Psychological Science Accelerator Affiliated Study: PSA-AF-001
In Press, Journal of Personality and Social Psychology
†Correspondence should be addressed to Paul W. Eastwick; E-mail: eastwick@ucdavis.edu
Preference-Matching Worldwide 2
1University of California, Davis, Department of Psychology, USA, 2UCLA Anderson School of
Management, Behavioral Decision Making Group, USA, 3Northwestern University, Department of
Psychology and Morton O. Schapiro IPR Faculty Fellow, Institute for Policy Research, USA, 4Kellogg
School of Management, USA, 5Slovak Academy of Sciences, Slovakia, 6Charles University, Czech
Republic, 7University of Jyväskylä, Finland, 8School of Health, Wellington Faculty of Health, Victoria
University of Wellington, Wellington, New Zealand, 9University of Kansas, Department of Social
Psychology, USA, 10Redeemer's University, Department of Behavioural Studies (Psy),
Nigeria, 11University of Colorado Colorado Springs, Dept of Psychology, USA, 12Institute for Advanced
Study in Toulouse (IAST), France, 13UIN Sunan Kalijaga Yogyakarta, Indonesia, 14Iscte-University
Institute of Lisbon, CIS_Iscte, Portugal, 15Université Paris Cité, France, 16University of Presov,
Slovakia, 17Jagiellonian University, Faculty of Philosophy, Institute of Psychology, Kraków,
Poland, 18Franklin and Marshall College, Department of Psychology, USA, 19University of Utah,
Department of Psychology, USA, 20Adam Mickiewicz University, Department of Psychology and
Cognitive Science, Poland, 21Cognitive Neuroscience Center, Adam Mickiewicz University, Poznan,
Poland, 22Teesside University, Department of Psychology, UK, 23Christ University, Department of
Psychology, India, 24Wellesley College, USA, 25Yonsei University, Department of Psychology, South
Korea, 26Chulalongkorn University, Faculty of Psychology, Thailand, 27Department of Social Science,
Faculty of Social Science and Humanities, Tunku Abdul Rahman University of Management and
Technology, Malaysia, 28Universidad de Sonora, Mexico, 29University of Zadar, Department of
Psychology, Croatia, 30University of Edinburgh, UK, 31Beijing Normal University, Institute of Advanced
Studies in Humanities and Social Sciences, Zhuhai, China, 32University of Ibadan, Department of
Psychology, Nigeria, 33Duke Univeristy, Department of Psychology and Neuroscience, USA, 34University
of Granada, Departament of Social Psychology, Spain, 35University of Hong Kong, Hong
Kong, 36Universidade Federal de Sergipe, Brazil, 37HSE University, Russia, 38Macedonian Academy of
Sciences and Arts, North Macedonia, 39Institute of Psychology, Jagiellonian University in Krakow,
Poland, 40Witten/Herdecke University, Department of Psychology and Psychotherapy,
Germany, 41University of Wroclaw, Institute of Psychology, Poland, 42Sabanci University, Faculty of Arts
and Social Sciences, Turkey, 43University of San Carlos, Philippines, 44University of Toronto, Department
of Psychology, Canada, 45Pavol Jozef Šafárik University in Košice, Department of Psychology, Faculty of
Arts, Slovakia, 46University of Wroclaw, Institute of Psychology, IDN Being Human,
Poland, 47Independent Researcher, Bangalore, India, 48University of the Philippines, Department of
Psychology, Philippines, 49Massachusetts Institute of Technology, MIT Sloan School of Management,
USA, 50University of Wroclaw, Institute of Polish Studies, IDN Being Human, Poland, 51Palacký
University Olomouc, Olomouc University Social Health Institute, Olomouc, Czech Republic, 52University
of Oxford, School of Antrhropology & Museum Ethnography, UK, 53York University, Toronto, ON,
Canada, 54Singapore Institute of Technology , Singapore, 55Marmara University, Department of
Psychology, Turkey, 56Üsküdar University, Department of Psychology, Turkey, 57University of Belgrade,
Faculty of Philosophy, Department of Pscyhology, Serbia, 58ELTE Eötvös Loránd University, Institue of
Psychology, Budapest, Hungary, 59The University of Queensland, School of Psychology,
Australia, 60University of Southern Queensland, School of Law and Justice, Australia, 61University of
Stirling, UK, 62Charles University, Institute for Research and Development of Education, Czech
Republic, 63University of Presov, Faculty of Education, Slovakia, 64Macquarie University, Department of
Philosophy, Australia, 65Bilkent University, Department of Psychology, Turkey, 66Toronto Metropolitan
University, Canada, 67Ss. Ciryl and Methodius University in Skopje, Faculty of Philosophy, North
Macedonia, 68University of Chester, School of Psychology, UK, 69Anglia Ruskin University, School of
Psychology, Sport, and Sensory Sciences, UK, 70Perdana University, Centre for Psychological Medicine,
Malaysia, 71Kyushu University, Japan, 72University of Turku, Department of Psychology and Speech-
Language Pathology, Finland, 73United Arab Emirates University, United Arab Emirates, 74Ithaca
College, Psychology Department, USA, 75University of Indonesia, Indonesia, 76Chuo University,
Japan, 77Stanford University, Center for the Study of Language and Information, USA
Preference-Matching Worldwide 3
Abstract
Ideal partner preferences (i.e., ratings of the desirability of attributes like attractiveness or
intelligence) are the source of numerous foundational findings in the interdisciplinary literature
on human mating. Recently, research on the predictive validity of ideal partner preference-
matching (i.e., do people positively evaluate partners who match versus mismatch their ideals?)
has become mired in several problems. First, articles exhibit discrepant analytic and reporting
practices. Second, different findings emerge across laboratories worldwide, perhaps because they
sample different relationship contexts and/or populations. This registered report—partnered with
the Psychological Science Accelerator—uses a highly powered design (N=10,358) across 43
countries and 22 languages to estimate preference-matching effect sizes. The most rigorous tests
revealed significant preference-matching effects in the whole sample and for partnered and
single participants separately. The “corrected pattern metric” that collapses across 35 traits
revealed a zero-order effect of β=.19 and an effect of β=.11 when included alongside a normative
preference-matching metric. Specific traits in the “level metric” (interaction) tests revealed very
small (average β=.04) effects. Effect sizes were similar for partnered participants who reported
ideals before entering a relationship, and there was no consistent evidence that individual
differences moderated any effects. Comparisons between stated and revealed preferences shed
light on gender differences and similarities: For attractiveness, men’s and (especially) women’s
stated preferences underestimated revealed preferences (i.e., they thought attractiveness was less
important than it actually was). For earning potential, men’s stated preferences underestimated—
and women’s stated preferences overestimated—revealed preferences. Implications for the
literature on human mating are discussed.
Keywords: attraction, close relationships, human mating, ideals, matching hypothesis
Preference-Matching Worldwide 4
A Worldwide Test of the Predictive Validity of
Ideal Partner Preference-Matching
The study of human mating is vast and interdisciplinary, spanning fields as diverse as
economics (Hitsch et al., 2010), evolutionary psychology (Buss & Schmitt, 2019), family studies
(Boxer et al., 2015), sociology (Lewis, 2016), and social/personality psychology (Fletcher et al.,
2019). Despite the considerable depth and breadth of these fields, they share in common a key
construct: ideal partner preferences. Ideal partner preferences are the attributes (e.g.,
attractiveness, intelligence, sense of humor) that people say they desire in a romantic partner and,
for 80 years, scholars have been using this construct as the foundation for a variety of theories
and models that explain how humans pursue and maintain mating relationships (Buss, 1989;
Eagly & Wood, 1999; Fletcher, et al., 1999; Hill, 1945; see Eastwick et al., 2014, for a review).
For many decades, scholars made the straightforward assumption that ideal partner
preferences affected how positively people feel about their romantic partners—which is itself a
key predictor of health and mortality (Robles et al., 2014). But only in the last 25 years have
researchers begun to empirically examine the preference-matching question: That is, does a
person positively evaluate a given romantic partner to the extent that the partner’s attributes
match the person’s ideals? This matching hypothesis is the core novel prediction offered by the
Ideal Standards Model—an influential model in the close-relationships tradition (Fletcher, et al.,
2000; Fletcher et al., 1999; Simpson, et al., 2001)—and this hypothesis emerges in evolutionary
psychological models as well (Buss, 1989; Conroy-Beam & Buss, 2016; Li & Meltzer, 2015;
Shackelford & Buss, 1997; Sugiyama, 2005). Indeed, it is challenging to articulate what the
ancestral, functional consequences of ideal partner preferences would be unless the match
Preference-Matching Worldwide 5
between preferences and a partner’s attributes had some meaningful association with romantic
evaluations.
Does the empirical evidence support this matching hypothesis? In brief, the evidence is
murky, and it has actually become murkier rather than clearer over time. Today, researchers can
cite empirical papers supporting or refuting any point they wish to make about this matching
hypothesis. This state of affairs is unfortunate, because precise effect size estimates for the
matching hypothesis will have generative and theory-building implications no matter what they
turn out to be. If the match between ideals and a partner’s traits predicts romantic evaluations
with (at least) modest effect sizes, then scholars should be able to assess participants’ ideals and
match them with new, compatible partners or determine whether their current relationships are
likely to encounter difficulties. But if these effect sizes are small or near-zero, then explanations
for the role of compatibility in human mating will need to become grounded in alternative
theories that do not rely on attribute matching (e.g., the way two people co-construct their
expectations, shared reality, or relationship narrative; Berscheid & Ammazzalorso, 2001,
Eastwick, et al., 2023; Rossignac-Milon & Higgins, 2018). Inspired by other large collaborative
replication efforts (Coles et al., 2020; Vohs et al., 2021), the current project aims to gather the
strongest possible evaluation of the predictive validity of ideal partner preferences—perhaps the
most interdisciplinary and theoretically central construct in research on human mating.
Ongoing Challenge #1: Lack of Standard Analytic Practices
One reason that the predictive-validity evidence to date remains murky is differing
analytic and reporting practices. There are many ways that the matching hypothesis has been
operationalized—some more rigorous than others. Specifically, researchers have tested the
predictive validity of ideal partner preferences in four primary ways: ideal-trait correlations, the
Preference-Matching Worldwide 6
raw pattern metric, the corrected pattern metric, and the level metric. Our own systematic review
yielded 35 published studies (Table S1) that have reported data that (a) examine participants’
evaluations of a person they have met face-to-face (i.e., from speed-dating partners to established
romantic partners), and (b) bear on at least one of these four approaches. The four approaches are
illustrated with a mock dataset in Table S2.
First, scholars sometimes report ideal-trait correlations: for a particular trait, the
researcher calculates the association between participants’ ideals and the partners’ traits
(Example 1a in Table S2) in a sample that presumably involved some prior selection event (e.g.,
the partners are people whom the participants selected as a romantic partner). In other words, do
people with a stronger preference for a trait end up with partners who are higher on the trait?
However, the selection event is not used as a measured variable (i.e., there are no “unselected”
partners)—so it cannot serve as a dependent measure—and no evaluative outcomes, such as
attraction or relationship satisfaction, are collected (Conroy-Beam & Buss, 2016; Gerlach et al.,
2019). Thus, these correlations are not rigorous tests of the matching hypothesis, as there are
many alternative explanations for any such correlation (Eastwick et al., 2019; Fletcher et al.,
2020). Indeed, the canonical papers using this approach (e.g., Fletcher et al., 1999; Murray et al.,
1996) generally presumed that these correlations reflected a motivated reasoning process (e.g.,
people are motivated to believe that their current partner possesses the traits that they ideally
want) rather than ideal partner preference-matching. These correlations are included in the
analysis plan because they are available as a matter of course when conducting the more rigorous
tests described next.
A second pattern metric (raw) approach uses the within-person correlation between (a)
each participant’s ideals and (b) a target partner’s traits (usually rated by participants themselves)
Preference-Matching Worldwide 7
across all available traits. Researchers subject this correlation to a Fisher z-transformation and
then use it to predict an evaluative outcome (e.g., relationship satisfaction; Example 1b in Table
S2). This approach typically reveals moderately sized associations (r = .20-.40) with relationship
satisfaction, which is consistent with the ideal partner preference-matching hypothesis. However,
as methodologists have compellingly described (Wood & Furr, 2016; Rogers et al., 2018), this
approach has a major shortcoming: The predictive power of the raw pattern metric is confounded
with the normative desirability of the ideal traits and target partner traits that are used to calculate
the within-person correlation. In other words, the raw pattern metric approach may have
garnered support for the ideal partner preference-matching hypothesis because people tend to
report positive evaluative outcomes when they think their partner has positive traits; thus, this
approach does not uniquely test whether the match between ideals and partner traits has
predictive effects. Approaches using Euclidean distance metrics share this shortcoming (e.g.,
Conroy-Beam et al., 2016; see Rogers et al., 2018).
A third pattern metric (corrected) approach follows Wood and Furr’s (2016)
recommendation to mean-center each ideal rating and partner trait rating (a and b in the
paragraph above) prior to the calculation of the within-person correlation; just as with the raw
pattern metric, this correlation can then be z-scored and used to predict an evaluative outcome
(Example 1c in Table S2). This procedure removes the normative desirability confound and
permits a clean test of the ideal partner preference-matching hypothesis, and published effect
sizes range from near zero to r ~.25 (Eastwick et al., 2019; Fletcher et al., 2020; Lam et al.,
2016).
A fourth level metric approach refers to the statistical interaction between the
participant’s ideal and the partner’s trait (i.e., the ideal × trait term) when predicting an
Preference-Matching Worldwide 8
evaluative outcome (controlling for the main effects of the ideal and trait; example 1d in Table
S2). For example, assume there is a positive association of (a) perceiving a partner to be funny
with (b) attraction to that partner. The level metric tests whether this association is stronger (i.e.,
more positive) among participants who have high (rather than low) ideals for a funny partner—as
if participants with high ideals are “weighting” the trait more positively in their evaluative
judgments. This approach is designed to be implemented one-trait-at-a-time, which is critical
when testing theories positing that ideals for specific attributes have functional outcomes (e.g.,
the hypothesis that heterosexual women have a stronger preference for financial success in a
partner because they have historically needed to differentiate strong from weak providers more
so than heterosexual men; Buss, 1989; Eastwick & Finkel, 2008; Eastwick et al., 2014; Li et al.,
2013; Perusse, 1993). Significant effects emerge sporadically using this approach (e.g., Fletcher
et al., 2020; Valentine et al., 2020), but high-powered level metric tests across multiple attributes
are uncommon.
Critically, few papers report more than one of the four approaches (see Table S1), and
researchers who draw conclusions from the weaker approaches (i.e., ideal-trait correlations, the
raw pattern metric) are more likely to conclude support for the matching hypothesis than are
researchers who use the stronger approaches (i.e., the corrected pattern metric, the level metric).
The current registered report addressed the challenge of discrepant reporting practices by
bringing together a diverse team of researchers who all committed to a preregistered analysis
plan with all four analytic strategies described above.
Ongoing Challenge #2: Differences between Established Relationships and Initial
Attraction
Preference-Matching Worldwide 9
A second reason that the state of the matching hypothesis is uncertain is that ideal partner
preference-matching effects may depend on relationship context. The matching hypothesis has
historically received support when participants evaluated a current romantic partner, as suggested
by studies of established relationships (e.g., Fletcher et al., 1999, 2000, 2020; Zentner, 2005).
But the hypothesis has not commonly been supported when participants evaluated a partner with
whom they were not romantically involved, as suggested by studies of initial attraction (e.g.,
Eastwick & Finkel, 2008; Selterman & Gideon, 2022; Wu et al., 2018). However, direct
comparisons of effect sizes for established relationship versus initial attraction partners remain
elusive, as studies conducted in these two contexts typically differ from each other in
innumerable ways.
To address context as a potentially critical moderator, the current project collected data
on both established relationship and initial attraction partners using a method (adapted from
Eastwick et al., 2011, and Sparks et al., 2020) that enables a clean comparison between these two
contexts. Specifically, participants who were in an established relationship completed scales
about their current romantic partner, and participants who were single completed the identical
scales about the partner with whom they would most desire to have a romantic relationship. By
using the same items and procedure in both relationship contexts, the two effect sizes can be
compared to each other more straightforwardly than in prior studies.
Researchers have speculated that a difference between initial attraction and established
relationship contexts could emerge because the ideal standards model primarily applies to long-
term partnerships, and/or because participants only draw from their (abstract) ideal partner
preferences once the relationship itself becomes an abstract entity with a hypothetical future
(Eastwick et al., 2014; Meltzer et al., 2014). Nevertheless, there are two reasons for such a
Preference-Matching Worldwide 10
difference that would be grounded in motivated perceptional processes rather than the ideal
standards model per se. First, people may adjust their perceptions of their partner’s traits to
match their ideals (Murray et al., 1996), perhaps especially if they are happy in their current
relationship. This interpretation is always plausible whenever participants provide their own
ratings of a partner’s traits—the most common method in this literature by far.1 To examine this
possibility, we also assessed each partner’s level of formal education (e.g., high school, college
degree)—a more objective measure that should be less subject to motivated re-interpretation than
typical trait ratings of the partner. To the extent that preference-matching effects are a function of
motivated perception of the partner’s traits, the effect size for the level metric should be smaller
for level of education.
Second, people may adjust their ideals to match their perceptions of their partner’s traits
(Gerlach et al., 2019; Neff & Karney, 2003), perhaps especially if they are happy in their current
relationship. One way to address this alternative explanation is to collect participants’ ideals
before the relationship forms in the first place (Eastwick et al., 2011). To examine the possibility
that people use their pre-relationship ideals when evaluating an ongoing relationship, we also
recruited an additional sample through Cloud Research. These participants reported their ideals
when single and then, after they started a new romantic relationship (several months later), they
completed measures about their current romantic partner. To the extent that preference-matching
1 To illustrate, 30 of the 35 studies in Table S1, or 86%, used this approach, whereas 23% asked partners
to self-report their own traits, and 20% used some “objective” measure of the trait. (These numbers add to
more than 100% because some studies employed multiple approaches.) The current study is primarily
designed to establish robust effect size estimates for the (most common) participant-perception approach,
which could then inform power analyses for future investigations of the other two (considerably more
intensive, but usually less well-powered) approaches.
Preference-Matching Worldwide 11
effects are a function of the motivated shifting of one’s own ideals, the effect sizes in this “newly
partnered” sample should be smaller.
The Current Research
This collaborative effort produced the largest cross-national dataset of participants’
evaluations and judgments about preferred-gender targets they know personally (e.g., romantic
partners, friends, acquaintances). The specific research questions in the Primary Planned
Analyses are outlined in Table 1. Research Questions (RQs) 1-4 rely on traditional null
hypothesis significance testing; nevertheless, interpretations will focus primarily on effect size
estimates vis-à-vis Cohen’s (1992) small, medium, and large conventions. Effect sizes for the
level metric (i.e., statistical interactions) will be interpreted as fractions of the attribute main
effects. In tutorials of interaction statistical power (Baranger et al., 2023; Giner-Sorolla, 2018), a
“knock out” interaction (i.e., interaction effect size beta = main effect size beta) is akin to a
medium sized effect, and a “50% attention” interaction (i.e., interaction effect size beta = 50% of
main effect size beta) is akin to a small effect. All four research questions were evaluated with all
four analytic approaches described above.
Preference-Matching Worldwide 12
Table 1 – Design Table: Primary Planned Analyses (Research Questions and Hypotheses)
Note: All (a) ideal-trait correlations and (d) level metric tests involve 35 separate tests, one for each
attribute in Tables 2 and 3. In these cases, we used a Holm-Bonferroni correction (Holm, 1979) to control
the family-wise Type-I error rate, and we discuss possible power implications in the A Priori Power
Analysis Plan section in the Supplemental Materials.
Research Question
Hypothesis N
1
What is the (overall) effect
size of ideal partner-
preference matching?
a. Ideal-trait correlations (rs) are greater than zero.
10,358
full sample
b. The raw pattern metric (r) is greater than zero.
c. The corrected pattern metric (r) is greater than zero.
d. Level metric tests (interaction βs) are greater than zero.
2
What is the effect size of
ideal partner-preference
matching in initial attraction
contexts?
a. Ideal-trait correlations (rs) are greater than zero.
4,152
subsample
b. The raw pattern metric (r) is greater than zero.
c. The corrected pattern metric (r) is greater than zero.
d. Level metric tests (interaction βs) are greater than zero.
3
What is the effect size of
ideal partner-preference
matching in established
relationship contexts?
a. Ideal-trait correlations (rs) are greater than zero.
5,544
subsample
b. The raw pattern metric (r) is greater than zero.
c. The corrected pattern metric (r) is greater than zero.
d. Level metric tests (interaction βs) are greater than zero.
4
Does the effect size of ideal
partner-preference matching
depend on initial attraction
vs. established relationship
context?
a. Ideal-trait correlations (rs) are larger when reporting on
current partners than desired partners.
4,152 vs.
5,544
subsamples
b. The raw pattern metric (r) is larger when reporting on
current partners than desired partners.
c. The corrected pattern metric (r) is larger when reporting on
current partners than desired partners.
d. Level metric tests (interaction βs) are larger when reporting
on current partners than desired partners.
Preference-Matching Worldwide 13
Method
This study mimics the design of an influential, initial test of the predictive validity of
ideal partner preference-matching (Fletcher et al., 1999, Study 6). Specifically, participants (a)
provided their ideals on a variety of traits, (b) rated their current romantic partner on those same
traits, and finally (c) reported an evaluation of their current partner as the outcome dependent
measure. This procedure remains the gold-standard in this research space, but it was updated in
three ways: (a) participants who were single were not excluded from participating, but were
instead given the chance to evaluate the person with whom they most desire to have a romantic
relationship (as in Eastwick et al., 2011, Study 3); (b) participants also evaluated three additional
targets—peers of their preferred gender (as in Sparks et al., 2020)—to enable additional analytic
tests (elaborated below); and (c) participants rated a larger set of traits (not just the traits
highlighted in Fletcher et al., 1999, but also the Big Five personality traits; Goldberg, 1993).
Ethics
Each research group ensured that they had approval from their institution’s Ethics
Committee or IRB to conduct the study, that the study was covered by the approved UC Davis
IRB (exempt protocol 1898056-1 “The Preference Matching Project”), or that the study was
exempt (see the Supplemental Materials for details).
Preference-Matching Worldwide 14
Figure 1 – 60 Samples Included in the Preference-Matching Project
Note: Locations indicate the university where the data were collected or—in the cases of online community samples—the center of the
relevant country. Map created with Datawrapper (Lorenz et al., 2012).
Preference-Matching Worldwide 15
Participants
Our final sample consisted of N = 10,358 participants (after planned exclusions; see
“Data Processing” for details) from 60 samples and 43 different countries (Table S3 and Figure
1). Some of the 60 samples assessed only student (undergraduate and graduate) participants (k =
22 samples), some assessed only community participants (k = 8), and some assessed a blend of
student and community participants (k = 30). Students typically received course credit, and
community members were compensated in a manner determined appropriate for their local
context (e.g., cash, electronic payments, gift cards, raffles, and some were not directly
compensated).
Participants were M = 28.5 years old (SD = 11.7; we assumed that values less than 10 or
greater than 100 were typos). In terms of gender, N = 6,833 (66.0%) were women, N = 3,394
(32.8%) were men, N = 127 (1.2%) preferred to self-describe their gender, and N = 4 provided no
response. In terms of sexual orientation, N = 8,366 (80.7%) were straight/heterosexual, N =
1,217 (11.7%) were bisexual, N = 361 (3.5%) preferred to self-describe, N = 202 (2.0%) were
gay, N = 162 (1.6%) were lesbian, and N = 50 (0.5%) either skipped this question or this
question was intentionally omitted because queer identities were punishable in that context. In
terms of education, N = 89 (0.9%) reported “less than high school,” N = 3,601 (34.8%) “high
school,” N = 2,559 (24.7%) “some college,” N = 2,556 (24.7%) “four-year degree,” N = 1,370
(13.2%) “Master’s degree,” N = 182 (1.7%) “Doctorate or professional degree,” and N = 1
provided no response.
Procedure
Preference-Matching Worldwide 16
The entire study consisted of a survey that could be completed on an electronic device.
Data collection began on February 1, 2023 (after the stage 1 registered report was approved) and
closed on November 10, 2023.
After providing consent and clicking a ReCAPTCHA button (to prevent bots from
accessing the survey), participants completed two blocks of measures (in counterbalanced order).
In the first block, they rated the desirability of 35 ideal partner preference attributes (as well as
their ideal for a “high level of education,” to be used in a separate analysis), and they completed
a brief set of demographic items and individual-difference measures.
The second block consisted of a set of items about specific partners. Using a procedure
implemented successfully by Sparks et al. (2020, Study 2), participants were asked to provide the
first name and last initial of four individuals whom they know personally.2 They were instructed
to choose individuals of their romantically preferred gender who are not related to them, who are
around the same age as them (i.e., peers), and whom they have met in person. Participants who
were in a romantic relationship were instructed to list their current romantic partner as the first of
the four individuals; participants who were single were asked to list “the person with whom you
would most desire to have a romantic relationship” (Eastwick et al., 2011, Study 3) as the first of
the four individuals. Third, they rated the first of the four targets (i.e., the current partner or most
desired partner) on the same set of 35 attributes. Fourth, they rated the first of the four targets on
the romantic evaluation dependent measure. The third and fourth steps were counterbalanced.
Fifth, they repeated the third and fourth steps (randomly counterbalanced for each target) for
each of the remaining three targets (presented in a random order). The first target was completed
2 This potentially identifying information was removed and replaced with numerical codes prior to the public
posting of the dataset.
Preference-Matching Worldwide 17
prior to the remaining three peer targets because the preregistered analysis plan focused on these
targets in particular.
Data, analysis code, codebook, and preregistration (i.e., the Stage 1 manuscript) are
available at https://osf.io/b29vu/?view_only=35a15592f8b04cdfb9ab32f45c73f3c6.
Materials
Translation. For surveys in languages other than English, participating laboratories
translated the original English materials into the target language (see Table S3). All laboratories
first used the translate feature in Qualtrics (which uses Google translate) to generate the initial
translation, edited as necessary, and then had an independent researcher who was fluent in the
target language read it over for comprehensibility. Then, consistent with translation best-
practices (Benet-Martinez, 2007), one or more (different) researchers who were fluent in English
and the target language back-translated, compared the back-translation against the original, and
resolved discrepancies. Researchers at different universities who were administering surveys in
the same language collaborated to ensure that their surveys were as similar as possible. In total,
the surveys were administered in 22 different languages (see Table S3 for details).
Ideal partner preferences. Participants rated 35 attributes (Table S4) in an ideal
romantic partner on a scale ranging from 1 (not at all desirable) to 11 (highly desirable). Scale
derivation work by Fletcher et al. (1999) produced a popular measure of three factors:
warmth/trustworthiness, vitality/attractiveness, and status/resources. We included five items
assessing warmth/trustworthiness, five items assessing vitality/attractiveness, and four items
assessing status/resources from this measure. We also included ten moderately-to-highly
desirable traits that emerged in a more recent article using a similar scale-derivation procedure
(Sparks et al., 2020), the Ten-Item Personality Inventory (i.e., a measure of the Big Five
Preference-Matching Worldwide 18
personality traits; Gosling et al., 2003), and one trait with potentially crucial cross-cultural
relevance (smells good; Roberts et al., 2020). The full collection of 35 attributes contained a mix
of attributes that typically range from low to high levels of (self-reported) desirability in an ideal
partner.
In addition, participants rated the extent to which “a high level of education is desirable”
in their ideal romantic partner on a 1 (not at all desirable) to 11 (highly desirable) scale.
Partner attributes. Participants rated how the 35 attributes characterized each target on
a scale from 1 (not at all characteristic) to 11 (highly characteristic). They also indicated the
highest level of education that the partner had completed from a set of six options ranging from
low to high (e.g., less than high school, high school, some college, four-year degree, Master’s
degree, Doctorate or Professional degree). The wording of these categories was adapted to each
countries’ educational context where needed; all adaptations contained six categories in
ascending order. We decided a priori to treat this item separately from the other 35 attributes
because it is distinct on both a conceptual (i.e., it is not really a psychological trait but rather an
objective fact about a person) and measurement level.
Romantic evaluation (dependent measure). Participants reported their romantic
evaluation of each of their four nominated targets on six items (“I am romantically interested in
_____,” “_____ is the only person I want to be romantically involved with,” “_____ is very
much my ideal romantic partner,” “It is important to me to see or talk with _______ regularly,”
“_______ is the first person that I would turn to if I had a problem,” and “If I achieved
something good, _______ is the person that I would tell first”) on a 1 (strongly disagree) to 11
(strongly agree) scale (see Table S5). Importantly, this measure was designed to be equally
applicable to relationships with peers and with romantic partners (see Supplemental Material
Preference-Matching Worldwide 19
below for scale-derivation details). Reliabilities were α = .92, ω = .92 on the full sample, α = .91,
ω = .91 on the partnered sample, and α = .85, ω = .85 on the single sample.
Individual-difference measures and demographic information. Participants
completed additional items including a 16-item measure of individualism/collectivism (e.g., “I’d
rather depend on myself than others,” “Parents and children must stay together as much as
possible,” Triandis & Gelfand, 1998), a 12-item measure of relational mobility (e.g., “They [the
people around you] have many chances to get to know other people,” “It is easy for them to meet
new people,” Thomson et al., 2018), and an item assessing relationship status (i.e., yes vs. no to
“I am currently in a committed, romantic relationship”).
Participants also indicated the nature of their relationship with each of the four targets
using the following (mutually exclusive) categories: spouse or fiancé,
boyfriend/girlfriend/committed romantic partner, casual romantic/sexual partner, friend,
colleague or co-worker, acquaintance, stranger. Additional individual differences and
demographic information (beyond those referenced in the manuscript) are described in the
Supplemental Materials.
Attention checks. In addition to the ReCAPTCHA button, there were two additional
“directed query” attention checks (Abbey & Meloy, 2017). First, after the consent form,
participants saw an item that lists the names of the seven continents and instructions that read: “If
you are reading this query, please select ‘Other’ and type the word ‘nonsense’ in the blank to
assure the researchers that you are reading the instructions.” Because some participants typed in
a nonsense word into the blank space, we decided (before running any analyses) to use all
participants who selected ‘Other’ and typed something in the space. Second, for the first target
Preference-Matching Worldwide 20
only, the romantic evaluation items contained an additional item that stated: “Please select ‘3’ for
this item to show that you are paying attention.”
Relationship formation hypothesis. As described above, one possible explanation for
the stronger support for ideal partner preference-matching in established close relationship (vs.
initial attraction) contexts is that people may be motivated to change their ideals to match their
current partner’s attributes (Gerlach et al., 2019; Neff & Karney, 2003). To test this possibility,
we collected a separate sample of N = 1,585 participants (i.e., online workers from the “Cloud
Research Approved List” on MTurk; Hauser et al., 2023) who completed two surveys at two
points in time, about 3.5 months apart (M = 104 days, SD = 12, range = 77-124). The sample
consists of (a) participants who were in a relationship with the same partner at both time points
(N = 709), (b) participants who were single at both time points (N = 687), and (c) participants
who were single at the first time point and in a relationship with a new partner at the second time
point (N = 189).
The recruitment plan and demographics for this sample is described in detail in the
Relationship Formation Hypothesis section of the Supplemental Materials; we preregistered that
these participants would be analyzed separately from the main analyses that correspond to the
Table S3 worldwide sample, given the procedural differences and the fact that these participants
were all from the U.S.
These participants completed a subset of the measures reported above. Specifically, at
time 1, they reported their ideal partner preferences and demographics in a 3-minute survey (for
US$1), and then at time 2, they completed the partner attribute and dependent measure items in a
10-minute survey (for US$5). They completed the relationship status item on both surveys, but
Preference-Matching Worldwide 21
the surveys did not include the additional individual differences and the three additional targets
(i.e., these participants only completed items about the current partner or most desired partner).
Data Processing
Once again, our final international sample consisted of N = 10,358 participants. Not
included in this value are the participants who were excluded from analyses because they (a)
“straightlined” (i.e., give the same numerical response to) either the 35 ideal partner preference
items or the 35 attribute ratings (N = 194), (b) failed to pass both attention checks (N = 2,600), or
(c) failed to reach the debriefing screen (N = 6,932; most of these participants stopped
responding a short way into the survey).
Participants were included in the N = 10,358 total and the overall analysis (i.e., research
question 1) but excluded from the relationship status subgroup analyses if they (a) indicated that
they were “single” but then categorized the first target they nominated as “spouse or fiancé” or
“boyfriend/girlfriend/committed romantic partner,” or (b) indicated that they were “in a
relationship” but then categorized the first target they nominated as anything other than “spouse
or fiancé” or “boyfriend/girlfriend/committed romantic partner.” N = 662 were included in the
overall sample but excluded from the relationship status subgroup analyses for these reasons,
which yielded N = 5,544 participants in the “partnered” category (with an average relationship
length of M = 6.3 years and SD = 8.8 years, assuming the N = 12 values above 1,000 months
were typos), and N = 4,152 participants in the “single” category for analyses.
We did not anticipate, nor did we have, a high proportion of missing/incomplete data
(less than 1% for all variables). Nevertheless, we also used predictive means matching using the
mice package for R (van Buuren & Groothuis-Oudshoorn, 2011) to investigate the possible
Preference-Matching Worldwide 22
consequences of missingness in a separate set of sensitivity analyses for Tables 2 and 3 (see
Supplemental Materials).
Results
Primary Planned Analyses
As preregistered, these analyses pertained only to the first target that participants
evaluated. All analyses were conducted as multilevel models that accounted for the nesting of
participant within the k = 60 samples (see Table 2 and 3 notes). Specifically, we included
random intercept (u0) and slope (u1) terms in each analysis, and the random slope (u1) for sample
was omitted when a given analysis did not converge. Overall, these random terms were fairly
modest in magnitude: For the overall sample analyses reported in Table 2, random intercept (u0)
terms accounted for 2.3% of the variance on average (i.e., 2.3% of the residual variance in the
trait dependent measure was attributable to the sample) and random slope (u1) terms accounted
for 0.3% of the variance. For the overall sample analyses reported in Table 3, random intercept
(u0) terms accounted for 3.4% of the variance on average, and random slope (u1) terms accounted
for 0.6% of the variance. In other words, the trait means (i.e., the DV in Table 2) and romantic
evaluation DV (i.e., the DV in Table 3) showed some minor differences (about 3%) across
samples. However, the association of ideals with traits (i.e., the associations in Table 2) and the
association of traits with romantic evaluations (i.e., the associations in Table 3) differed very
little (less than 1%) across samples.3 All variables were standardized (M = 0, SD = 1) for each
analysis.
As described above (and in Table S2), ideal-trait correlations refer to the between-
persons association of the ideal rating and the partner attribute rating for a given attribute. One
3 We calculated these % variance values using the r2mlm package in R (Shaw et al., 2020).
Preference-Matching Worldwide 23
association is calculated for each attribute, and the dependent measure is not used in this
calculation (Table 2). The pattern metric (raw) is the association between (a) a Fisher-z scored
version of the within-person correlation between the 35 ideal ratings and the 35 partner-attribute
ratings, and (b) the romantic evaluation measure. The pattern metric (corrected) is the
association between (a) a Fisher-z scored version of the within-person correlation between the 35
ideal ratings and the 35 partner-attribute ratings after sample-mean centering all 70 items, and (b)
the romantic evaluation measure. The level metric is the ideal × attribute interaction predicting
the romantic evaluation measure, controlling for the main effect of ideal and attribute (Table 3).
Given that we are assessing three constructs from Fletcher et al. (1999) and all five of the
Big Five constructs (see Table 2), the ideal-trait correlations and level metric tests were
conducted not only at the item level, but also at the construct level for the three Fletcher et al.
(1999) constructs (i.e., warmth/trustworthiness, vitality/attractiveness, and status/resources) and
the Big Five (i.e., Extraversion, Agreeableness, Conscientiousness, Emotional Stability, and
Openness to Experience). The pattern metric analyses were calculated on the full set of 35
attributes, because such profile correlations require many items to assess reliably (Wood & Furr,
2016).
Given that the corrected pattern metric and the level metric provide the strongest tests of
the ideal-partner preference matching hypothesis, our interpretations of the findings rely
primarily on these effect sizes. We provide the ideal-trait correlations and raw pattern metric
effect sizes for completeness and transparency. Importantly, the ideal-trait correlations and level
metric analyses in Tables 2 and 3 require 35 statistical tests, one for each attribute. Therefore, we
implemented a Holm-Bonferroni correction (Holm, 1979) for all instances where we conducted
35 statistical tests, and we only conclude support for attributes that pass this significance
Preference-Matching Worldwide 24
threshold (i.e., .05/35 = .0014 = alpha for the lowest p-value of the 35; .05/34 = .0015 = alpha for
the second lowest p-value of the 35; .05/33 = .0015 = alpha for the third lowest p-value, and so
on). In all tables, the attributes are listed in the order that participants spontaneously nominated
them in the classic Fletcher et al. (1999) paper (see Table S4). A summary of the central findings
is depicted in Figure 2.
Figure 2 – Results for Research Questions (RQs) 1-3
Note: Values for ideal-trait correlations and level metric are averaged across the 35 traits. Bars
depict upper and lower 95% CIs.
Weak inference tests. As anticipated, ideal-trait correlations (Table 2) were positive and
significant across the board (β1 = .33 across the 35 traits on average): Participants who had high
Preference-Matching Worldwide 25
ideals for a trait tended to report that the target possessed higher amounts of that trait. These
correlations trended higher for partnered (β1 = .34 on average) than single (β1 = .32 on average)
participants, and 16 out of 35 of the partnered vs. single comparisons passed the Bonferroni-
Holm correction. Nevertheless, these partnered vs. single differences tended to be very small.
Also as expected, the raw pattern metric (i.e., the within-person correlation between the
35 ideals and traits) predicted romantic interest strongly, with effect sizes in the medium-to-large
range (β1 = .37 in the full sample, see Table 3). As with the ideal-trait correlations, this
association was slightly stronger for partnered (β1 = .38) than single (β1 = .32) participants.
In a non-preregistered analysis, we additionally examined whether a measure of
Euclidean distance (i.e., the square root of the sum of the squared differences between ideals and
traits; Rogers et al., 2018) predicted the romantic evaluation DV when used in place of the raw
pattern metric. Results showed that this measure performed similarly: Larger Euclidean distances
negatively predicted positive evaluations in the full sample (β1 = -.31, p < .001), and for both
partnered (β1 = -.31, p < .001) and single (β1 = -.29, p < .001) participants.
Table 2 –Ideal-Trait Correlations (Analysis Plan 1a through 4a)
Ideal-Trait Correlations
Attribute
Overall
Partnered Single
t for
comparison
1
Attractive (V/A)
.29***
.28***
.31***
2.44
2
Intelligent
.35***
.38***
.31***
-4.69***
3
Humorous
.39***
.40***
.37***
-3.96***
4
Considerate (W/T)
.31***
.30***
.30***
-1.04
5
Honest
.30***
.33***
.25***
-5.19***
6
Understanding (W/T)
.31***
.31***
.29***
-1.19
7
Ambitious
.41***
.45***
.38***
-6.19***
8
Sporty and Athletic
.39***
.41***
.36***
-3.24**
9
Fun
.37***
.35***
.39***
-0.63
10
Sensitive (W/T)
.36***
.36***
.36***
-1.79
11
A good lover (V/A)
.34***
.33***
.32***
-4.24***
12
Nice body (V/A)
.29***
.27***
.32***
2.69
13
Confident
.34***
.34***
.32***
-2.85
Preference-Matching Worldwide 26
14
Sexy (V/A)
.36***
.34***
.41***
1.91
15
Financially secure (S/R)
.24***
.25***
.25***
-0.62
16
Supportive (W/T)
.31***
.31***
.28***
-2.22
17
Dresses well (S/R)
.33***
.34***
.31***
-2.78
18
A good listener (W/T)
.28***
.26***
.29***
-2.50
19
Loyal
.27***
.33***
.20***
-8.43***
20
Successful (S/R)
.29***
.30***
.28***
-3.61***
21
Adventurous (V/A)
.38***
.39***
.38***
-4.09***
22
Good job (S/R)
.28***
.30***
.27***
-2.22
23
Religious
.57***
.63***
.53***
-8.97***
24
Patient
.26***
.28***
.26***
-1.94
25
Extraverted, enthusiastic (Ext)
.37***
.41***
.34***
-4.52***
26
Critical, quarrelsome (Agr)
.39***
.39***
.42***
-0.10
27
Dependable, self-disciplined (Con)
.31***
.33***
.29***
-3.61***
28
Anxious, easily upset (Emo)
.27***
.28***
.27***
-0.83
29
Open to new experiences, complex (Opn)
.36***
.37***
.34***
-3.15**
30
Reserved, quiet (Ext)
.35***
.39***
.31***
-4.31***
31
Sympathetic, warm (Agr)
.32***
.32***
.32***
-1.51
32
Disorganized, careless (Con)
.25***
.24***
.26***
-0.29
33
Calm, emotionally stable (Emo)
.27***
.29***
.25***
-3.82***
34
Conventional, uncreative (Opn)
.34***
.35***
.32***
-3.09**
35
Smells good
.38***
.34***
.42***
-0.32
W/T average
.41***
.40***
.39***
-2.95**
V/A average
.40***
.37***
.43***
-0.74
S/R average
.34***
.34***
.34***
-1.95
Ext average
.36***
.42***
.31***
-5.76***
Agr average
.37***
.36***
.38***
0.68
Con average
.29***
.29***
.29***
-1.94
Emo average
.27***
.27***
.26***
-1.79
Opn average
.36***
.37***
.36***
-3.53***
Note: W/T: warmth/trustworthiness; V/A: vitality/attractiveness; S/R: status/resources; Ext:
Extraversion; Agr: Agreeableness; Con: Conscientiousness; Emo: Emotional Stability; Opn: Openness to
Experience. In the Big Five averages, Items 26, 28, 30, 32, and 34 were reverse scored. Values are the
regression estimated betas (β1’s) from the following equation: Partner attribute = β0 + β1Ideal + u0 +
u1Ideal + ε . The random slope (u1) for sample is omitted when models do not converge. t for comparison
refers to the β3 estimate in the following model, which tests the difference between the partnered and
single columns: Partner attribute = β0 + β1Ideal + β2RelStatus + β3Ideal×RelStatus + u0 + u1Ideal + ε ** p
< .01, *** p < .001. Asterisks are omitted for estimates that fail a Holm-Bonferroni test (Holm, 1979)
within each column of 35 traits.
Preference-Matching Worldwide 27
Table 3 – Effect Sizes for Tests of Ideal Partner Preference-Matching (Analysis Plan 2b-4b, 2c-
4c, 2d-4d)
Analysis
Overall
Partnered Single
t for
comparison
Pattern metric
Raw
.37***
.38***
.32***
3.06**
Corrected
.19***
.17***
.19***
3.27**
Level Metric
1
Attractive (V/A)
.02**
.00
.05***
3.14**
2
Intelligent
.03***
.00
.03
3.46***
3
Humorous
.04***
.01
.06***
4.30***
4
Considerate (W/T)
.00
-.04***
.04***
5.79***
5
Honest
.02
-.01
.02
2.72
6
Understanding (W/T)
.02
-.01
.04***
4.72***
7
Ambitious
.07***
.05***
.08***
3.69***
8
Sporty and Athletic
.07***
.06***
.08***
2.41
9
Fun
.02
-.03**
.05***
6.45***
10
Sensitive (W/T)
.06***
.07***
.06***
0.80
11
A good lover (V/A)
.04***
.02
.06***
1.72
12
Nice body (V/A)
.02
.01
.06***
3.66***
13
Confident
.04***
.01
.04***
3.70***
14
Sexy (V/A)
.02**
.02
.04***
2.74
15
Financially secure (S/R)
.04***
.04***
.06***
2.51
16
Supportive (W/T)
.01
-.01
.02
3.59***
17
Dresses well (S/R)
.03***
.03
.04***
2.14
18
A good listener (W/T)
.01
-.02
.05***
5.62***
19
Loyal
.03***
.03**
.02
0.46
20
Successful (S/R)
.05***
.03
.06***
3.97***
21
Adventurous (V/A)
.05***
.07***
.07***
3.36***
22
Good job (S/R)
.05***
.05***
.06***
2.16
23
Religious
.13***
.10***
.07***
-0.21
24
Patient
.01
-.02
.04
4.01***
25
Extraverted, enthusiastic (Ext)
.07***
.09***
.03
-1.63
26
Critical, quarrelsome (Agr)
.08***
.10***
.08***
1.36
27
Dependable, self-disciplined (Con)
.03***
-.01
.06***
5.27***
28
Anxious, easily upset (Emo)
.07***
.05***
.08***
3.25**
29
Open to new experiences, complex (Opn)
.05***
.05***
.06***
3.78***
30
Reserved, quiet (Ext)
.09***
.09***
.07***
0.28
31
Sympathetic, warm (Agr)
.02
-.01
.04
4.38***
32
Disorganized, careless (Con)
.04***
.05***
.05***
1.73
33
Calm, emotionally stable (Emo)
.03***
.02
.04***
2.10
34
Conventional, uncreative (Opn)
.07***
.09***
.05***
-0.04
35
Smells good
.01
.03
.02
1.06
W/T average
.00
-.03***
.02
4.50***
V/A average
.01
-.02*
.05***
5.30***
S/R average
.03***
.03**
.07***
4.30***
Ext average
.07***
.08***
.04**
-1.43
Preference-Matching Worldwide 28
Agr average
.03**
.04***
.05***
1.22
Con average
.03**
.01
.06***
4.73***
Emo average
.05***
.01
.07***
3.82***
Opn average
.05***
.05***
.05**
2.41*
Note: W/T: warmth/trustworthiness; V/A: vitality/attractiveness; S/R: status/resources. Ext: Extraversion;
Agr: Agreeableness; Con: Conscientiousness; Emo: Emotional Stability; Opn: Openness to Experience.
In the Big Five averages, Items 26, 28, 30, 32, and 34 were reverse scored. Values for pattern metric
(raw) and pattern metric (corrected) are the regression estimated beta (β1) from the following equation:
Romantic evaluation = β0 + β1PatternMetric + u0 + u1PatternMetric + ε. Values for the level metric are the
ideal × trait interaction estimated beta’s (β3’s) from the following equation: Romantic evaluation = β0 +
β1Ideal + β2PartnerAttribute + β3Ideal×PartnerAttribute + u0 + u1PartnerAttribute + ε. In all cases, the
random slope (u1) for sample is omitted when models do not converge. “t for comparison” for the pattern
metric tests refers to the β3 estimate in the following model: Romantic evaluation = β0 + β1PatternMetric
+ β2RelStatus + β3PatternMetric×RelStatus + u0 + u1PatternMetric + ε. “t for comparison” for the level
metric tests refers to the β7 estimate in the following model: Romantic evaluation = β0 + β1Ideal +
β2PartnerAttribute + β3Ideal×PartnerAttribute + β4RelStatus + β5Ideal×RelStatus +
β6PartnerAttribute×RelStatus + β7Ideal×PartnerAttribute×RelStatus + u0 + u1PartnerAttribute + ε . * p <
.05, ** p < .01, *** p < .001. Asterisks are omitted for estimates that fail a Holm-Bonferroni test (Holm,
1979) within each column of 35 traits.
Strong inference tests. The corrected pattern metric successfully predicted the romantic
evaluation (β1 = .19 in the full sample, see Table 3). In other words, a pure measure of
preference-matching across 35 different traits predicted the evaluative dependent measure with a
small-to-medium effect size. The association was actually larger in the single (β1 = .19) than the
partnered (β1 = .17) subsample, but the difference was quite small.4
The level metric results were more modest, although many were significantly different
from zero (Table 3). As with the corrected pattern metric, these effects tended to be larger for
single than partnered participants, although again, such differences were very small. Intriguingly,
level metric interaction effects tended to be larger for traits that are not as commonly assessed in
this research space, like religiosity and extraversion. The level metric interaction effects were
4 Some perspectives (e.g., Biesanz, 2010; Fletcher et al., 2020; Wood et al., 2019) add a measure of
normative matching alongside “distinctiveness” metrics like these. Using this approach, effect sizes are
about half as large as those reported here, but still significant; see the “Normative Preference-Matching”
section below.
Preference-Matching Worldwide 29
quite small for traits that are normatively very desirable and commonly studied, like
warmth/trustworthiness and vitality/attractiveness traits.
Overall, the level metric effect sizes illustrate why such interactions have been hard to
detect in prior studies: The average interaction β3 = .04 is a 15% attenuation interaction given the
average β2 = .27. To put the effect size challenges in context, we used the Shiny App
InteractionPoweR (Baranger et al., 2023; Finsaas et al., 2021) and the average values across all
the 35 level metric tests: β1 = .02, β2 = .27, β3 = .04 (see equation in note of Table 3), and the
average ideal-trait correlation β = .33 from Table 2. Using these values, achieving 80% power to
detect an interaction effect of β3 = .04 would require N = 4,475 participants.5 (The largest level
metric effect—religiosity—would still require N = 470 to achieve 80% power.) In summary, the
current data suggest that level metric effects do exist, but such interactions will require
substantial, if not enormous, resources to detect.
Level of education level metric analysis. It was also possible to test the level metric
interaction for level of education using the same multilevel analyses described in the Table 3
note (Romantic evaluation = β0 + β1Ideal + β2PartnerAttribute + β3Ideal×PartnerAttribute + u0 +
u1PartnerAttribute + ε) using the ideal “level of education” item and the partner’s actual level of
education (coded on a 6-point continuous scale). We calculated this estimate for the overall
sample, single participants, and partnered participants, and we also tested the difference between
single and partnered participants. For the overall sample, this interaction was β3 = .06, t(1991.84)
5 The power to detect a standardized interaction effect β is very close to the power to detect a correlation
of size β, with two caveats: (a) larger main effects of the two interacting variables (in this case, ideals and
attribute perceptions) increase power, and (b) a larger correlation between the two main effects can
increase or decrease power, depending on the size of the main effects (Baranger et al., 2023). These
mitigating forces are not especially large in these analyses, and so the N required to achieve 80% power to
detect β3 = .04 (4,475) is only slightly smaller than the N required to achieve 80% power to detect r = .04
(4,900).
Preference-Matching Worldwide 30
= 6.33, p < .001. For single participants, this interaction was β3 = .03, t(1421.33) = 1.95, p =
.051; for partnered participants, this interaction was β3 = .03, t(5522.28) = 2.70, p = .007; and the
difference between single and partnered participants was not significant, t(9020.83) = 0.50, p =
.618.
Relationship formation hypothesis. This hypothesis pertains to the separate sample of
CloudResearch participants who completed the surveys at two time points, about 3.5 months
apart. We conducted the raw pattern metric, corrected pattern metric, and level metric analyses
on these three samples (see Table 4). Some of the findings echoed the Table 3 results for the full
international sample. For example, for both the steadily partnered and the newly partnered
sample, the raw pattern metric was considerably larger than the corrected pattern metric
(especially in the steadily partnered sample), but the corrected pattern metric was still significant
and of a meaningful effect size (β = .24). Estimates for the steadily partnered and newly
partnered sample were similar, suggesting that both sets of participants maintained their ideals
over the intervening months and drew from them when evaluating their partners, regardless of
whether or not they were dating that partner when they reported their ideals at time 1.
Intriguingly, for the single participants, the corrected pattern metric was essentially zero: Unlike
the participants in the international single sample in Table 3, ideal partner preference-matching
seemed to have no bearing on the evaluations of these single participants—a finding we revisit in
the Discussion. Once again, level metric findings were erratic and small on average (the smaller
sample size here yielded a larger range of negative and positive values, relative to Table 3);
preferences for religiosity and extraversion perhaps deserve additional study going forward
nonetheless.
Preference-Matching Worldwide 31
With respect to level of education: For the steadily partnered sample, the level metric
interaction was β3 = .05, t(705) = 1.46, p = .144; for steadily single participants, this interaction
was β3 = .03, t(683) = 0.77, p = .444; for newly partnered participants, this interaction was β3 =
.05, t(185) = 0.71, p = .480. The difference between these three samples was not significant,
F(2,1573) = 0.12, p = .890.
Table 4 – Relationship Formation Hypothesis
Analysis
Steadily
Partnered
Steadily
Single
Newly
Partnered
F for
comparison
Pattern Metric
Raw
.50***
.18***
.39***
20.86***
Corrected
.24***
.01
.24***
10.78***
Level Metric
1
Attractive (V/A)
.00
.02
.07
0.56
2
Intelligent
-.05
-.03
.08
1.93
3
Humorous
.03
.02
.14***
3.44
4
Considerate (W/T)
.02
-.03
-.09
1.28
5
Honest
-.06
-.02
.08
1.75
6
Understanding (W/T)
.00
-.02
-.07
0.43
7
Ambitious
.12***
.02
.16
3.68
8
Sporty and Athletic
.06
.05
.10
0.28
9
Fun
-.03
-.01
.08
0.95
10
Sensitive (W/T)
.03
-.01
.06
0.53
11
A good lover (V/A)
-.07
-.03
.07
2.43
12
Nice body (V/A)
-.04
.01
.06
1.12
13
Confident
.03
.07
.15
1.16
14
Sexy (V/A)
.04
-.01
.01
0.67
15
Financially secure (S/R)
.10
-.02
.19
5.33
16
Supportive (W/T)
.06
-.04
.04
2.80
17
Dresses well (S/R)
.03
.02
.26***
6.54
18
A good listener (W/T)
-.01
-.08
.02
1.77
19
Loyal
-.01
-.05
.01
0.72
20
Successful (S/R)
.03
-.03
.18
3.62
21
Adventurous (V/A)
.10
.02
.21**
3.87
22
Good job (S/R)
.14***
.02
.15
4.03
23
Religious
.24***
-.01
.37***
13.51***
24
Patient
.09
.01
-.07
2.96
25
Extraverted, enthusiastic (Ext)
.09
-.01
.08
2.14
26
Critical, quarrelsome (Agr)
.15***
.05
.10
2.25
27
Dependable, self-disciplined (Con)
.05
-.03
.19**
4.94
28
Anxious, easily upset (Emo)
.06
.02
.05
0.36
29
Open to new experiences, complex (Opn)
.01
.09
.13
2.18
Preference-Matching Worldwide 32
30
Reserved, quiet (Ext)
.14***
.02
.16
4.12
31
Sympathetic, warm (Agr)
.07
.04
-.06
1.69
32
Disorganized, careless (Con)
.01
-.01
.02
0.16
33
Calm, emotionally stable (Emo)
.10
.02
.07
1.34
34
Conventional, uncreative (Opn)
.03
.04
-.04
0.66
35
Smells good
.03
-.04
.10
2.50
W/T average
-.04
-.05
-.05
0.06
V/A average
-.06
.00
.04
1.55
S/R average
.07*
.00
.23***
4.54*
Ext average
.13***
.03
.07
2.42
Agr average
.10**
.08*
-.03
1.41
Con average
.05
-.04
.11
2.58
Emo average
.08*
.00
.07
1.36
Opn average
-.03
.07
.00
2.24
Note: W/T: warmth/trustworthiness; V/A: vitality/attractiveness; S/R: status/resources. Ext: Extraversion;
Agr: Agreeableness; Con: Conscientiousness; Emo: Emotional Stability; Opn: Openness to Experience.
In the Big Five averages, Items 26, 28, 30, 32, and 34 were reverse scored. Note that in these analyses,
there is no within-sample dependency. Values for pattern metric (raw) and pattern metric (corrected) are
the regression estimated betas (β1) from the following equation: Romantic evaluation = β0 +
β1PatternMetric + ε. Values for the level metric are the ideal × trait interaction estimated beta’s (β3’s)
from the following equation: Romantic evaluation = β0 + β1Ideal + β2PartnerAttribute +
β3Ideal×PartnerAttribute + ε. RelStatus is a 3-level categorical variable, so “F for comparison” for the
pattern metric tests refers to the omnibus test of the two β3 estimates in the following model: Romantic
evaluation = β0 + β1PatternMetric + β2RelStatus + β3PatternMetric×RelStatus + ε. “F for comparison” for
the level metric tests refers to the omnibus test of the two β7 estimates in the following model: Romantic
evaluation = β0 + β1Ideal + β2PartnerAttribute + β3Ideal×PartnerAttribute + β4RelStatus +
β5Ideal×RelStatus + β6PartnerAttribute×RelStatus + β7Ideal×PartnerAttribute×RelStatus + ε . * p < .05,
** p < .01, *** p < .001. Asterisks are omitted for estimates that fail a Holm-Bonferroni test (Holm,
1979) within each column of 35 traits.
Exploratory Descriptive Analyses
Table 5 presents descriptive analyses of the average preferences of participants in the
dataset, both stated (i.e., rated ideals) and revealed (i.e., the association between the attribute and
the evaluative dependent measure; Wood & Brumbaugh, 2009). Colloquially speaking, the ideal
partner preference ratings (i.e., the means for each attribute) capture the extent to which people
generally say that each attribute is important in an ideal partner, whereas the revealed
preferences (i.e., the slopes for each attribute predicting the dependent variable) capture the
Preference-Matching Worldwide 33
extent to which each attribute actually predicts people’s romantic evaluations of partners.6 This
table also includes the rank ordering of both sets of 35 preferences.
On the whole, stated and revealed preferences aligned in terms of ranking, although some
intriguing differences did emerge. For example, the attributes “confident,” “a good listener,”
“patient,” and “calm, emotionally stable” ranked considerably more highly as stated preferences
than as revealed preferences. In contrast, the attributes “attractive,” “a good lover,” “nice body,”
“sexy,” and “smells good” ranked considerably more highly as revealed preferences than as
stated preferences. In fact, “a good lover” was the #1 largest revealed preference but actually
ranked 12th in terms of stated preferences. (We also conducted separate analyses on the partnered
and single subsamples, revealing identical conclusions; see Tables S10 and S11 in the
Supplemental Materials.)
Table 5 also calculates gender differences in the preference for attractiveness (i.e., the
average of the items “attractive,” “nice body,” and “sexy”) and earning potential (i.e., the
average of the items “ambitious,” “financially secure,” and “good job”). Some theoretical
perspectives anticipate that men will place greater weight on attractiveness, and women will
place greater weight on earning potential (Buss, 1989). These gender differences indeed emerged
when participants reported their stated preferences. Nevertheless, consistent with past meta-
analytic work (Eastwick et al., 2014) and the very small level metric analyses documented in
Table 3, these gender differences did not emerge in participants’ revealed preferences.
We can also use the Table 5 ranking approach to illuminate why a gender difference
incongruity emerges between stated and revealed preferences. Men’s stated preferences tended to
6 This analysis applies at the level of the entire dataset on the primary target only; we calculate a related
form of revealed preference (which we call a “functional preference”; Ledgerwood et al., 2018) that
makes use of all four targets in a later section.
Preference-Matching Worldwide 34
underestimate the value they actually placed on “attractive,” “nice body,” and “sexy” by about 6
ranks (out of 35; 1 = highest ranked, 35 = lowest ranked) on average (see Table S12 in the
Supplemental Materials for details). That is, their stated preferences for these three traits ranked
9, 18, and 17 (respectively) but their revealed preferences for these three traits ranked 7, 13, and
6. However, women underestimated the value they placed on these three traits by a full 13 ranks
(out of 35): Their stated preferences for these three traits ranked 18, 28, and 23 (respectively) but
their revealed preferences for these three traits ranked 8, 17, and 5 (i.e., about the same as men).
As for “ambitious,” “financially secure,” and “good job,” men’s stated preferences
underestimated their value by about 4 ranks: Their stated preferences for these three traits ranked
25, 25 (tied), and 27 (respectively) but their revealed preferences for these three traits ranked 22,
24, and 20. In contrast, women’s stated preferences overestimated their value by about 4 ranks:
Their stated preferences for these three traits ranked 22, 17, and 18 (respectively) but their
revealed preferences for these three traits ranked 24, 25, and 21 (i.e., again, about the same as
men). In summary, both men’s and women’s stated preferences appeared to underestimate the
weight they place on attractiveness, but this underestimation effect was more pronounced for
women than for men. In contrast, men’s stated preferences slightly underestimated the weight
they placed on earning potential, and women’s stated preferences slightly overestimated the
weight they placed on earning potential.
Preference-Matching Worldwide 35
Table 5 – Descriptive Statistics for Stated and Revealed Preferences
Stated Preferences
Revealed Preferences
Attribute
N
M SD Rank
β Rank
Attractive
10343
8.86
1.89
16
.42***
8
Intelligent
10348
9.39
1.65
9
.38***
12
Humorous
10345
9.34
1.78
11
.36***
13
Considerate
10343
9.59
1.60
7
.40***
10
Honest
10347
10.08
1.38
2
.43***
5
Understanding
10346
9.84
1.46
4
.42***
7
Ambitious
10344
8.13
2.34
24
.22***
24
Sporty and Athletic
10347
7.16
2.43
29
.10***
29
Fun
10351
9.43
1.66
8
.38***
11
Sensitive
10340
8.10
2.35
25
.28***
19
A good lover
10338
9.26
1.99
12
.56***
1
Nice body
10348
8.02
2.15
26
.32***
16
Confident
10347
8.77
1.87
17
.18***
26
Sexy
10342
8.39
2.20
19
.42***
6
Financially secure
10342
8.38
2.19
20
.20***
25
Supportive
10346
9.92
1.52
3
.49***
3
Dresses well
10344
8.19
2.18
23
.24***
22
A good listener
10346
9.69
1.60
5
.35***
14
Loyal
10345
10.10
1.53
1
.51***
2
Successful
10344
8.22
2.22
22
.29***
17
Adventurous
10338
7.89
2.36
27
.16***
27
Good job
10342
8.30
2.17
21
.24***
21
Religious
10340
4.83
3.33
31
.04**
31
Patient
10342
9.35
1.70
10
.29***
18
Extraverted, enthusiastic
10343
7.70
2.19
28
.13***
28
Critical, quarrelsome
10339
3.41
2.65
33
-.04**
33
Dependable, self-disciplined
10348
9.26
1.81
12
.33***
15
Anxious, easily upset
10341
3.10
2.24
34
.02
32
Open to new experiences, complex
10346
8.64
2.10
18
.23***
23
Reserved, quiet
10338
5.53
2.66
30
.07***
30
Sympathetic, warm
10345
9.61
1.59
6
.40***
9
Disorganized, careless
10340
2.80
2.17
35
-.05***
34
Calm, emotionally stable
10344
9.26
1.75
12
.26***
20
Conventional, uncreative
10343
4.02
2.56
32
-.07***
35
Smells good
10346
9.10
1.98
15
.45***
4
W/T average
10356
9.43
1.29
.48***
V/A average
10353
8.48
1.56
.50***
S/R average
10353
8.27
1.81
.31***
Ext average
10344
7.08
1.78
.04**
Agr average
10348
9.10
1.61
.25***
Con average
10348
9.23
1.56
.21***
Emo average
10347
9.08
1.56
.14***
Opn average
10350
8.31
1.75
.19***
Preference-Matching Worldwide 36
Note: Effect sizes d and q are coded such that positive effect sizes are in the predicted direction. Gender
differences were only calculated for participants who identified as a man or a woman and who selected
the option “straight/heterosexual” for their sexuality. Stated preferences are means. Revealed preferences
are β1 terms in the equation: Romantic evaluation = β0 + β1PartnerAttribute + u0 + u1PartnerAttribute + ε.
In all cases, the random slope (u1) for sample is omitted when models do not converge.
Secondary Planned Analyses
Normative preference-matching. A difference between the effect sizes associated with
the raw pattern metric and the corrected pattern metric implies—but does not directly test—the
idea that participants positively evaluate partners to the extent that they perceive those partners to
have consensually desirable traits (Fletcher et al., 2020). The direct test of this idea entails
calculating a normative pattern metric: the association between (a) a Fisher z-scored version of
the within-person correlation between the sample average of the 35 ideal ratings (not the
participant’s own rating) and (the participants’ own ratings of) the 35 partner-attribute ratings,
and (b) the romantic evaluation measure.
Using the multilevel analyses described in the Table 3 note, we calculated this estimate
for the overall sample, single participants, and partnered participants, and we also tested the
difference between single and partnered participants. For the overall sample, this effect was β1 =
.37, t(39.35) = 29.38, p < .001. For single participants, this effect was β1 = .32, t(33.68) = 18.14,
p < .001; for partnered participants, this effect was β1 = .39, t(56.99) = 19.84, p < .001; and the
difference between single and partnered participants was significant, t(6765.48) = 2.17, p = .030.
Attribute
N
Stated Preferences
Revealed Preferences
M
SD
β
Gender Diff.
Gender Diff.
Attractiveness Composite
t
d
t
q
Heterosexual Men
2935
8.73
1.70
13.10***
0.22
.46***
0.19
0.02
Heterosexual Women
5408
8.35
1.80
.45***
Earning Potential Composite
Heterosexual Men
2933
7.50
1.85
27.51***
0.71
.27***
0.78
0.00
Heterosexual Women
5410
8.74
1.63
.28***
Preference-Matching Worldwide 37
These effect sizes suggest that, when participants perceived that partners had normatively “ideal”
traits, they evaluated those partners very positively, regardless of their own idiosyncratic ideal
partner preferences.
In some research areas that examine analogous forms of multivariate matching (e.g.,
Biesanz, 2010; Fletcher et al., 2020; Wood et al., 2019), it is common practice to predict a
dependent measure from both the normative and distinctive metrics simultaneously. Similarly,
we can predict the romantic evaluation DV using the following equation:
Romantic evaluation = β0 + β1NormativePatternMetric + β2CorrectedPatternMetric + u0 +
u1NormativePatternMetric + u2CorrectedPatternMetric + ε (Eq. 1)
Using this approach, the normative preference-matching effects closely approximate the effect
sizes when included in the equation alone: in the full sample, β1 = .34, t(37.85) = 26.59, p < .001;
in the single subsample, β1 = .29, t(32.15) = 15.76, p < .001; in the partnered subsample β1 = .37,
t(59.33) = 20.21, p < .001. However, the corrected pattern metric effect sizes were approximately
half the size of what they were when included in the equation alone: in the full sample, β2 = .11,
t(31.42) = 10.71, p < .001; in the single subsample, β2 = .13, t(41.70) = 6.69, p < .001; in the
partnered subsample, β2 = .09, t(47.09) = 5.66, p < .001. In other words, idiosyncratic preference-
matching offers a small (β = .09-.13), yet significant, boost above and beyond normative
preference-matching, and normative preference-matching is approximately 3 times as large.
Individual difference moderation. It is plausible that ideal partner preference-matching
effects vary across studies in the existing literature due to individual differences across
participant populations. A study by Lam et al. (2016) points to the intriguing possibility that
there are important cross-cultural factors at play. In this reasonably large (N = 472) study, these
scholars found that the corrected pattern metric had a significant predictive association with
relationship evaluations in Taiwan (r = .22) but not in the U.S. (r = .05), and the difference
Preference-Matching Worldwide 38
between these two correlations was significant. Reasons for a Taiwan-U.S. difference remain
somewhat speculative, but one relevant distinction between these two cultures is relational
mobility—that is, the ability to meet new people and select into (and out of) relationships on the
basis of personal desires (Kito et al., 2017; Thomson et al., 2018; Yuki & Schug, 2012).
Americans, by virtue of their higher relational mobility, might be more likely than Taiwanese to
“try out” relationships that mismatch their ideals, perhaps especially if they presume that they
could later end the relationship with minimal consequences. Then, if people are motivated on
average to feel positively about their partners after investing time and energy into the
relationship (Joel & MacDonald, 2021), high relational mobility populations may include a
larger proportion of people with ideal-mismatching partners who nevertheless report high
satisfaction. A second potentially relevant distinction is individualism-collectivism (Triandis &
Gelfand, 1998), as individuals in collectivistic cultures may be especially likely to adopt the ideal
partner preferences of their parents (Locke et al., 2020). If the attributes of one’s romantic
partner implicate family members in collectivistic societies, this fact may motivate collectivistic
(but not individualistic) individuals to remain attuned to the extent to which the partner
mismatches their ideals.
To test whether relational mobility (i.e., the average of the 12 items; Thomson et al.,
2018), individualism (i.e., either the 4-item horizontal individualism or 4-item vertical
individualism subscales; Triandis & Gelfand, 1998), or collectivism (i.e., either the 4-item
horizontal collectivism or 4-item vertical collectivism subscales; Triandis & Gelfand, 1988)
affect ideal partner preference-matching, we examined whether these five individual difference
measures moderated all the analyses reported in Table 3 that pertained to research questions 1-3
(i.e., effect sizes associated with the overall sample, single participants and partnered
Preference-Matching Worldwide 39
participants). Again, we used Bonferroni-Holm correlations for each set of 35 tests. Table 6 uses
“+” signs to indicate positive, significant interaction terms (i.e., ideal preference-matching is
stronger among participants who are higher in relational mobility/individualism/collectivism),
and “–” signs to indicate negative significant interaction terms (i.e., ideal preference-matching is
stronger among participants who are lower in relational mobility/individualism/collectivism).
The predicted direction of moderation is depicted in a row at the top of Table 6. Reliabilities for
relational mobility were α = .82 (ω = .81) on the full sample, α = .82 (ω = .81) on the partnered
sample, and α = .82 (ω = .80) on the single sample; reliabilities for horizontal individualism were
α = .71 (ω = .72) on the full sample, α = .69 (ω = .70) on the partnered sample, and α = .73 (ω =
.74) on the single sample; reliabilities for vertical individualism were α = .67 (ω = .68) on the full
sample, α = .67 (ω = .67) on the partnered sample, and α = .69 (ω = .69) on the single sample;
reliabilities for horizontal collectivism were α = .74 (ω = .74) on the full sample, α = .74 (ω =
.74) on the partnered sample, and α = .73 (ω = .73) on the single sample; reliabilities for vertical
collectivism were α = .69 (ω = .72) on the full sample, α = .68 (ω = .71) on the partnered sample,
and α = .69 (ω = .72) on the single sample.
Very few of these interactions were statistically significant. And, crucially, in the full
table, 21 interactions were in the predicted direction of moderation (i.e., no shading in Table 6),
and 23 interactions were in the opposite of the predicted direction (i.e., grey shading). For
example, when interactions emerged for the corrected pattern metric, they tended to be positive
interactions (8 out of 9 times), regardless of whether the prior literature anticipated that these
interactions would be negative (relational mobility, individualism) or positive (collectivism).
Given the ambiguity of these results and related concerns about moderation with measured
variables (Rohrer et al., 2022), we hesitate before interpreting them any more deeply.
Preference-Matching Worldwide 40
Table 6 – Secondary Planned Analyses
Analysis
Overall
Partnered Single
Functional
Prefs
R
Ind Col
R
Ind
Col
R
Ind
Col
H
V
H
V
H
V
H
V
H
V
H
V
Predicted Direction of Moderation
−
−
−
+
+
−
−
−
+
+
−
−
−
+
+
Pattern Metric
Raw
−
−
−
−
−
−
−
−
+
+
2.61%
Corrected
+
+
+
+
+
−
+
+
+
6.87%
Level Metric
1
Attractive (V/A)
+
1.21%
2
Intelligent
0.53%
3
Humorous
2.02%
4
Considerate (W/T)
+
1.47%
5
Honest
+
0.98%
6
Understanding (W/T)
2.04%
7
Ambitious
4.87%
8
Sporty and Athletic
4.53%
9
Fun
+
1.68%
10
Sensitive (W/T)
4.15%
11
A good lover (V/A)
4.12%
12
Nice body (V/A)
+
3.82%
13
Confident
+
3.51%
14
Sexy (V/A)
+
+
3.68%
15
Financially secure (S/R)
4.11%
16
Supportive (W/T)
1.34%
17
Dresses well (S/R)
3.32%
18
A good listener (W/T)
2.29%
19
Loyal
1.49%
20
Successful (S/R)
2.71%
21
Adventurous (V/A)
4.75%
22
Good job (S/R)
4.13%
23
Religious
−
−
−
4.77%
24
Patient
4.59%
25
Extraverted, enthusiastic (Ext)
−
4.98%
26
Critical, quarrelsome (Agr)
3.60%
27
Dependable, self-disciplined (Con)
1.67%
28
Anxious, easily upset (Emo)
4.94%
29
Open to new experiences, complex (Opn)
3.96%
30
Reserved, quiet (Ext)
3.51%
31
Sympathetic, warm (Agr)
2.36%
32
Disorganized, careless (Con)
3.75%
33
Calm, emotionally stable (Emo)
3.11%
34
Conventional, uncreative (Opn)
2.97%
35
Smells good
3.02%
W/T average
−
−
2.13%
Preference-Matching Worldwide 41
Note: W/T: warmth/trustworthiness; V/A: vitality/attractiveness; S/R: status/resources. Ext: Extraversion;
Agr: Agreeableness; Con: Conscientiousness; Emo: Emotional Stability; Opn: Openness to Experience;
R: Relational mobility moderation; Ind: Individualism moderation (H = Horizontal, V = Vertical); Col:
Collectivism moderation (H = Horizontal, V = Vertical). In the Big Five averages, Items 26, 28, 30, 32,
and 34 were reverse scored. Individual-difference moderation values for pattern metric (raw) and pattern
metric (corrected) derive from the interaction beta (β3) from the following equation: Romantic evaluation
= β0 + β1PatternMetric + β2IndividualDifference + β3 PatternMetric × IndividualDifference + u0 +
u1PatternMetric + ε. Values for the level metric derive from the interaction beta’s (β7’s) from the
following equation: Romantic evaluation = β0 + β1Ideal + β2PartnerAttribute + β3Ideal× PartnerAttribute
+ β4IndividualDifference + β5Ideal×IndividualDifference + β6PartnerAttribute×IndividualDifference +
β7Ideal×PartnerAttribute×IndividualDifference + u0 + u1PartnerAttribute + ε. In all cases, the random
slope (u1) for sample is omitted when models do not converge. The “+” indicates significant positive
moderation; the “−” indicates significant negative moderation; the predicted pattern of moderation is
depicted in the first five rows. The “+” and “–” signs were omitted for estimates that failed a Holm-
Bonferroni test (Holm, 1979) within each column of 35 traits. Shaded “+” and “–” signs are in the
opposite of the predicted direction. Functional preferences refer to the R2t (v) variance estimate from Rights
& Sterba (2019) that captures the percentage of variance (out of 100%) accounted for by individual
differences in the association of the attribute/pattern metric with the romantic evaluation dependent
measure.
Functional preferences. Given that participants rated four total targets in the primary
sample, it was possible to calculate each participant’s functional preference for each attribute
(Ledgerwood et al., 2018). A functional preference (also called a “driver of liking,” Lawless &
Heymann, 2010) is the strength with which an attribute (e.g., attractiveness) predicts a given
person’s romantic evaluations across a series of targets—how much the attribute “matters” for a
given participant. In this case, each participant’s functional preference can be measured as the
association of an attribute with the dependent measure across the four targets. Functional
preferences in this context are very similar to the revealed preferences described above. The
distinction is that a functional preference (typically) refers to a preference that has been
V/A average
+
+
+
+
+
+
2.01%
S/R average
3.36%
Ext average
2.91%
Agr average
+
2.10%
Con average
+
2.61%
Emo average
+
+
4.01%
Opn average
−
2.75%
Preference-Matching Worldwide 42
measured separately for each participant, and this requires that the participant rates multiple
targets. The descriptive analyses in Table 5 only used the first (primary) target that participants
evaluated.
A new approach by Rights and Sterba (2019) permits the calculation of the extent to
which these functional preferences exhibit stable individual differences across targets.
Specifically, the R package r2mlm (Shaw et al., 2020) provides the percentage of variance
accounted for by the random effects component (i.e., “slope variation” or R2t (v)) for a particular
attribute as a fraction of the total variance.7
We calculated these values for all 35 attributes, the 3 Fletcher et al. (1999) constructs
(both jointly and separately), the 5 Big Five traits, and the 2 pattern metric analyses (Table 6).
The R2t (v) variance estimates for the 35 attributes, the 3 Fletcher et al. (1999) constructs, and the
5 Big Five constructs essentially denote the extent to which there are stable individual
differences in the tendency for some people to exhibit stronger functional preferences than other
people for a given attribute (Eastwick, Finkel, et al., 2023).
These values tended to be larger than zero, but they were fairly modest: The average of
the 35 traits was R2t (v) = 3.1%, and no trait exceeded 5%. In other words, individual differences
in the way that participants weigh a given trait accounts for about 3% of the variance in romantic
evaluations. We also calculated the R2t (v) variance estimates for the two pattern metric analyses;
these values denote the extent to which there are stable individual differences in the tendency to
desire a partner who matches (vs. mismatches) one’s ideals across all attributes. For example, the
results for the corrected pattern metric indicated that individual differences in the way that
7 Unlike the analyses above, this analysis ignores nesting within sample, and we conduct the analysis on a
dataset that contains four rows per participant (one for each target). Now, the random slope effect
captures variability across participants (not samples) in the extent to which the attribute predicts the
dependent measure.
Preference-Matching Worldwide 43
people weigh the match between ideals and traits across all traits accounts for about 7% of the
variance in romantic evaluations.
Discussion
Central Takeaways
This is the first report from the Preference-Matching Project: the largest examination of
ideal partner preference-matching to date (N = 10,358 participants). In brief, ideal partner
preference-matching predicted romantic evaluations—when collapsing across a large array of
traits. That is, the effect size for the corrected pattern metric was modest but meaningful (β =
.19), and it did not differ appreciably between the partnered (β = .17) vs. single (β = .19)
subsamples. Normative desirability proved to be an important consideration, too: Participants
who perceived that partners matched the normative (i.e., sample-wide) ideal partner strongly
desired those partners (β = .37). When included together (as recommended by Beisanz, 2010;
Fletcher et al., 2020), normative preference-matching remained strong (β = .34), while the
corrected pattern metric was cut to a small (but still significant) effect size (β = .11). Approaches
like the raw pattern metric and Euclidean distance revealed medium-to-large effects, likely
because they blend normative and distinctive matching together into a single measurement
mixture (Rogers et al., 2018; Wood & Furr, 2016).
The level metric (i.e., ideal × trait interaction) tests were also highly informative. These
effects were quite small on average (β = .04), which may be why they have rarely been
significant in prior studies (β = .04 would require a sample size of N = 4,475 to detect with 80%
power; no prior study was even close to achieving such a large sample size, see Table S1). Also
notable is that the level metric tests for the commonly studied, highly desirable attributes in this
literature (e.g., traits in the warmth/trustworthiness and vitality/attractiveness categories) did not
Preference-Matching Worldwide 44
even differ from zero in the full sample (β = .00-01). Alternatively, traits that are rarely studied
in this literature and that received moderate ideal ratings on average showed much larger level
metric effects, like extraversion (β = .07) and religious (β = .13). It appears that the predictive
validity of specific traits is more likely to be detectable for traits that land in a middling range of
desirability (i.e., what could be called “horizontal” attributes; Hitsch et al., 2010), rather than
traits that are highly normatively desirable (i.e., “vertical” attributes).
In two cases, expected moderation effects failed to emerge. First, we did not find much
evidence that certain people or certain populations were especially likely to rely on their ideals.
Our preregistered tests of potentially relevant individual differences (Table 6) revealed no
interpretable pattern. Indeed, the multi-level modeling approach of Rights and Sterba (2019)
suggested that the slope random effects corresponding to the sample (u1) in Table 3 explained
less than 1% of the variance. In other words, the average association between an attribute and the
romantic evaluation dependent measure tended not to vary reliably depending on which of the 60
samples generated it. Slightly larger (but still modest) amounts of variability emerged for (a) the
tendency for some people to desire particular traits more than others across four different
partners (3.1%; Table 6), (b) the tendency for mean levels of the traits to vary across samples
(2.3%; cultures vary in the extent to which participants view partners as “humorous” or
“ambitious”), and (c) the tendency for mean levels of the dependent measure to vary across
samples (3.4%; cultures vary in the extent to which participants are happy in their relationships).
These latter three types of effects might be more promising candidates for tests of moderation.
Second, for the most part, effect sizes in the partnered and single samples were similar.
Many scholars (including several in the project coordinator group of the current project) once
believed that ideal partner preference-matching was more likely to predict outcomes in
Preference-Matching Worldwide 45
established relationships rather than initial attraction contexts (Eastwick et al., 2014). It is
possible that the earlier literature suggested this pattern because studies of ongoing relationships
classically gravitated toward the uncorrected pattern metric (which reveals medium-to-large
effects; Fletcher et al., 1999, 2000), whereas initial attraction studies were inspired by
perspectives on gender differences for specific traits in isolation (e.g., attractiveness, earning
potential) and therefore tended to rely on level metric tests (which reveal very small effects;
Eastwick & Finkel, 2008).
Nevertheless, one curious data point remains: Why did the single participants in the
relationship formation subsample show no effects whatsoever? These participants first reported
their ideals in isolation while they were single. Then, about 3.5 months later, these (still single)
participants completed the rest of the procedure. The corrected pattern and level metric tests
suggested that these participants were not drawing from their previously reported ideals at all
(Table 4). And yet, this separation of 3.5 months seemed to matter very little for participants who
were partnered at both time points, or participants who were single at time 1 and partnered at
time 2. There are perhaps two ways of explaining these data. First, perhaps the people who were
single at both time points had several rejection experiences in the interim, and their ideals
changed more than the single participants who had the acceptance experience of becoming
partnered during this time frame (Charlot et al., 2019). Second, perhaps single people who are
very attracted to a particular partner are motivated to interpret the partner’s traits in line with
their ideals, but only if they have recently been reminded of their ideals. Researchers in this area
should keep a keen eye on whether single participants are reporting their ideals and measures
about a potential partner at the same or a different moment in time. (Most speed-dating studies,
for example, ask participants to report their ideals on an intake form, and then participants
Preference-Matching Worldwide 46
evaluate potential partners several days later.) This seemingly incidental methodological feature
may matter a great deal for reasons that are not yet clear.
Finally, we presented a new approach that allows researchers to explore the distinction
between stated preferences (i.e., preferences for traits as rated on scales) and revealed
preferences (i.e., preferences as captured by the strength of the association between the trait and
the DV). When the 35 attributes were ranked in the whole sample, it was possible to document
cases where stated preference judgments (relatively) overestimated revealed preference
judgments: Participants actually liked attributes like “confident,” “a good listener,” “patient,”
and “calm, emotionally stable” less than they thought they did. In other cases, participants’ stated
preferences were underestimates, as in the case of “attractive,” “a good lover,” “nice body,”
“sexy,” and “smells good.” This approach was also able to illuminate why gender differences
emerge for stated (but not revealed) preferences for attractiveness and earning potential attributes
(Table 5). Specifically, for attractiveness, both men’s and women’s stated preferences
underestimated their revealed preferences, but women’s tendency to underestimate proved far
stronger than men’s. For earning potential, a “mirror image” pattern emerged such that men’s
stated preferences underestimated their revealed preferences but women’s stated preferences
overestimated their revealed preferences. Moving forward, this approach could be used to
examine other research questions on accuracy and bias using various measures of preferences.
Strengths and Limitations
This study has a number of strengths. Our partnership with the Psychological Science
Accelerator (Moshontz et al., 2018) meant that the data were collected across 43 countries using
a questionnaire that had been translated (and back-translated) into 22 different languages.
Critically, our highly powered design meant that the estimates of effect sizes throughout this
Preference-Matching Worldwide 47
paper are far more precise than is typical in most studies in this research area. Also, this paper
was approved as a registered report, which meant that the design and analytic approach were
reviewed before the data were collected.
This study also makes several important theoretical contributions. The pattern of effect
sizes suggests that studies are far more likely to find empirical support to the extent that they
focus on matching across many variables simultaneously rather than single attributes in isolation
(e.g., gender differences in specific attributes; Eastwick & Finkel, 2008; a “top-3 most
important” attributes approach, Sparks et al., 2020). Furthermore, the fact that effect sizes tended
to be about three times larger for normative matching rather than the corrected pattern metric
sheds new light on the intuitive idea that “people know what they want in a partner.” Yes,
people’s stated preferences capture the attributes that are generally desirable in partners, but a
given person’s distinctive preferences only modestly (but still significantly) capture the attributes
that they find especially desirable. These estimates also help clarify theories about the origin and
nature of relationship variance (i.e., compatibility), as they represent one of the strongest
attempts to use attribute-matching to explain why people are more likely to experience attraction
and romantic contentment with some partners rather than others. The current data suggest that
the corrected pattern metric across 35 traits may be able to explain 2-4% of relationship variance.
But of course, SRM approaches suggest that romantic evaluative measures are mostly (i.e., >
50%) comprised of relationship variance (Kenny, 2019). The lion’s share of human romantic
compatibility remains unaccounted for, and we may have to stretch beyond attribute-matching
concepts like similarity and preference-matching to explain it (Eastwick et al., 2023).
This study also has some limitations. This study only used measured variables, and
experimental approaches will be required to understand the causal consequences of ideals
Preference-Matching Worldwide 48
(Eastwick, Smith, et al., 2019; Rohrer et al., 2022). Furthermore, the participants’ partners did
not actually take part in this study, and effect sizes will likely decline across the board if the
partner’s (rather than the participant’s) reports of the partner’s traits are used instead (Hromatko
et al., 2015). If one conservatively estimated that the zero-order corrected pattern metric would
decline to (say) r = .10, a sample size of N = 779 participants would be necessary to achieve 80%
power—a challenging but not impossible task. Also, the 35 attributes that we assessed here are
certainly not exhaustive, and our results suggest the wisdom of testing the predictive validity of
other traits that (a) receive middling (i.e., not especially high) normative desirability ratings, or
(b) are prioritized in some cultures more than others. Finally, even though we sampled
participants from all over the world, most of them had at least a high school level of education,
and many of them likely live in situations where they have substantial freedom of choice over
who they could select as a romantic partner. Future research would need to examine how mate
evaluations take place in contexts where people themselves have limited input over whom they
are expected to court or marry.
Conclusion
The current study partnered with the Psychological Science Accelerator to test the
predictive validity of ideal partner preferences across 43 different countries. Results revealed that
ideals did indeed have predictive power, although results were highly dependent on whether
preference-matching was conceptualized as a normative match (βs ranging from .30-.40), an
idiosyncratic or distinctive match (βs .10-.20), or as the level of specific traits (average β = .04).
These data—especially given the size and breadth of the dataset—should be able to provide
effect size benchmarks for future studies of human mate preferences, regardless of whether
Preference-Matching Worldwide 49
researchers are interested in stated preferences, revealed preferences, or preference-matching
effects.
Data and Code Availability
Data, codebook, and analysis scripts, are openly available through the Open Science
Framework: https://osf.io/b29vu/?view_only=35a15592f8b04cdfb9ab32f45c73f3c6.
Preference-Matching Worldwide 50
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Supplemental Materials
Table S1 –Published Tests of the Predictive Validity of Ideal-Partner Preference Matching
Very weak
inference
Weak
inference
Strong
inference
Article
Ideal-trait
correlations
Pattern
(raw)
Pattern
(corrected)
Level
N
Murray et al. (1996)
242
Botwin et al. (1997)
a
216
Fletcher et al. (1999, Study 5)
83
Fletcher et al. (1999, Study 6)
89
Fletcher et al. (2000)
100
Zentner (2005, Study 2)
98
Todd et al. (2007)
47
Eastwick & Finkel (2008)
163
Eastwick (2009, Study 2)
146
Eastwick, Eagly, et al. (2011, Study 4)
187
Eastwick, Eagly, et al. (2011, Study 5)
71
Eastwick, Finkel, et al. (2011, Study 1)
100
Eastwick, Finkel, et al. (2011, Study 3)
b
502
Murray et al. (2011)
386
Eastwick & Neff (2012)
338
Li et al. (2013, Study 3)
142
Li et al. (2013, Study 4)
93