Gender Traditionalism and Wellbeing: Change across First Year at a Global University

Gender Traditionalism and Wellbeing: Change across First Year at a Global University | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gender Traditionalism and Wellbeing: Change across First Year at a Global University Matthew Nielson, PJ Henry This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8561546/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There is mounting evidence for a link between beliefs about gender and wellbeing, but the outcomes are sometimes contradictory and seem strongly affected by demographic factors. The body of work is predominantly based on Western samples with scant diversity with which to test these ideas. The body of work is also almost entirely cross-sectional, limiting the ability to make causal claims or understand directionality. We tested whether longitudinal change in gender traditionalism predicts wellbeing in young adults across the first year of university with a diverse sample of 650 students at an international university in the Middle East. We paired latent class analysis with latent change score modeling to analyze how gender traditionalism may change over the course of young adult’s first year at university, how those changes relate to wellbeing, and determine whether this process differed by profiles determined by gender, ethnicity, conservatism, religious affiliation, and sexual orientation. Contrary to expectations and of theoretical interest, results indicated an overall increase in gender traditionalism that significantly predicted better wellbeing for conservative, religious Muslims. Progressive Arab women emerged as a profile with several distinctive patterns including being the only group who did not increase in gender traditionalism. This work is the first to offer strong evidence (change driven) for the relation between gender traditionalism and wellbeing, and it reinforces the importance of identifying demographic moderators of this link. conservatism intersectionality latent change score religion sexism Figures Figure 1 Figure 2 Introduction Individual-level attitudes, such as the endorsement of traditional gender roles that women should be submissive, feminine nurturers and men should be masculine, dominant providers (i.e., gender traditionalism), are internalized and can shape personal identity and daily experiences above and beyond external socialization (Nielson et al., 2020) in ways that can influence psychological health (King et al., 2020; Jaehn et al., 2020). While numerous studies have found that more egalitarian gender attitudes (at either the individual or national level) are associated with better wellbeing—across diverse cultural contexts including Russia, China, Australia, and the Netherlands (Baird et al., 2019; Chen et al., 2023; Jaehn et al., 2020; King et al., 2022; Read & Grundy, 2011; Sweeting et al., 2014; Van De Vijver, 2007; Zhang et al., 2014)—some findings are mixed (Amarachi et al., 2020; Gui, 2019) or even contradictory (e.g., Napier et al., 2010; Nilles et al., 2023; Tesch-Römer et al., 2008). Much of this research is cross-sectional and has been conducted on samples that are predominantly Western or with little internal diversity with which to test the effect of demographic factors (e.g., Coleman & Andersson, 2024; Herreen et al., 2021; Homan & Burdette, 2021; King et al., 2022; Read & Grundy, 2011; Vantieghem et al., 2014). A longitudinal assessment that differentiates between cultural profiles using change in individual-level gender traditionalism to predict wellbeing could help sort out some of the literature’s inconsistencies by testing how different types of people might respond differently to traditional gender role endorsement. For example, young adults from countries and with backgrounds where conservative/traditional gender attitudes are more prevalent may show a different pattern from those from other countries (Amarachi et al., 2020; Gui, 2019; Rashid et al., 2022). Furthermore, a longitudinal analysis allows us to test (for the first time to our knowledge) the dynamics of this relation during the first year of college – a pivotal period in young adult development. The purpose of this paper is twofold: 1) to test, within a single study and a uniform methodology, the longitudinal relation between gender traditionalism and wellbeing in young adults. 2) to compare this process across different cultural profiles from a multicultural, non-WEIRD (Heinrich et al., 2010) sample. Specifically, we assess whether students in different cultural profiles experience change in gender traditionalism over the course of their first year at a multicultural Middle Eastern university and whether that traditionalism change (if any) predicts wellbeing. Gender Traditionalism and Wellbeing The inconsistencies in the literature concerning the relation between holding gender traditional beliefs and wellbeing, described above, suggest that the relation between gender attitudes and wellbeing is moderated by key demographic factors such as home country /region (i.e., the global region of one’s home country), religiosity, conservatism, sexual orientation, and education. Each of these factors may play a unique role in the relation between gender traditionalism and well-being. Home Country / Region While higher levels of gender traditionalism are often related to lower levels of wellbeing for both men and women across an array of ethnic-racial backgrounds and home countries, the opposite is sometimes true, particularly for men from countries in the Global South (Gui, 2019; Rashid et al., 2022). For Global South women, a negative link between gender traditionalism and wellbeing sometimes still appears, though less frequently than for Western women (Soltanpanah et al., 2018). Home country thus emerges as a key moderator in the relation between gender traditionalism and wellbeing. Specifically, people from Western countries may likely report a negative relation between gender traditionality and wellbeing whereas people from Global South countries may report a positive relation, though this pattern is likely further moderated by participant gender and other demographic factors enumerated below. Gender Although traditional gender ideologies often restrict autonomy in ways that negatively impact the wellbeing of everyone, regardless of gender (Andersson & McSwain, 2025; Lima et al., 2024), their implications may differ for men and women. Cross-cultural work suggests that men often benefit from endorsing traditional roles, as these roles legitimize status and power advantages, whereas women are more likely to experience psychological costs given that traditionalism prescribes subordination and limited opportunities for them (Jaehn et al., 2020; King et al., 2022; Read & Grundy, 2011). At the same time, findings remain mixed across global contexts, with some studies in the Global South showing that both men and women may report higher wellbeing when aligning with prevailing traditional norms (Gui, 2019; Rashid et al., 2022; Soltanpanah et al., 2018). These patterns suggest that gender moderates the traditionalism–wellbeing link in culturally contingent ways: in contexts where egalitarian norms are stronger, women in particular may experience greater benefits to wellbeing from rejecting traditionalism, while in contexts where traditional roles are normative, both men and women may derive a sense of security or belonging from endorsing them (Napier et al., 2010; Tesch-Römer et al., 2008). Religion and Conservatism Gender beliefs are strongly affected by religion and conservatism (Etengoff & Lefevor, 2021) which, in turn, may connect to how gender traditionalism relates to wellbeing. Religious involvement is linked to heightened wellbeing via community building and providing purpose (Myers, 2000). Similarly, conservative ideology is associated with better self-reported health, possibly due to stronger beliefs in a just world (Jost, 2019; Napier et al., 2020). Regarding gender roles, religion often frames traditional gender roles as divinely ordained (Homan & Burdette, 2021), and conservative people, perhaps in part because of religious gender ideology, are far more supportive of traditional gender roles than are liberal people (see Jost, 2017 for a meta-analysis). As such, conservatism and religiosity emerge as interconnected factors that may alter whether gender traditionalism is associated with wellbeing or its lack. Sexual Orientation Another dimension that complicates the gender traditionalism–wellbeing relation is sexual orientation. Societal discomfort with sexual minorities is rooted in perceptions that sexual minorities violate traditional gender norms (Henry & Steiger, 2022). Consequently, sexual minority individuals who internalize gender traditionalism may face unique intrapersonal conflicts, potentially undermining their wellbeing (Suppes et al., 2019). We are unaware of any previous work that directly tests the role of sexual orientation as a moderator of the relation between gender traditionalism and wellbeing, but several studies show that internalizing traditionally gendered ideology is particularly problematic for those who do not feel that they adequately live up to those gendered expectations (Sweeting et al. 2014, UK; Soltanpanah et al. 2018; van de Vijver 2007). In line with research on those who are more religious and conservative, we expect that those who are more heterosexual will likely experience a more positive relation between gender traditionalism and wellbeing than those who are more attracted to the same gender. Education in Young Adulthood In young adulthood, education plays a crucial role in the formation of gender traditionalism and its subsequent relation effect on wellbeing. Young adulthood is a key period for ideological change (Arnett, 2000; McLean et al., 2017; Wilhelm et al., 2023) and, for those who attend, university education is likely a major cause of change. In higher education settings young adults are more likely to be exposed to more liberal ideology than they had been previously (Hanson et al., 2015; Harris & Elison, 1932); this trend holds across Western and Global South countries (Van Hiel et al., 2018). Perhaps as a result, cross-sectional data show that most young adults who attend college report lower levels of gender traditionalism after their time at school (Bryant, 2003). However, this may be due to selection biases: One study showed how US adolescents with more egalitarian views were more likely to report goals of continuing to college/university (Davis and Pearce (2007). Longitudinal evidence concerning conservative/liberal change goes back to the classic Bennington study that showed that women’s conservative beliefs became more progressive over their education (Newcomb, 1943). Other studies have confirmed that, at least within a US context, time at university relates to more progressive belief systems (Fan & Marinim, 2000; Sidanius et al., 2008). However, these changes may depend on demographic factors: for example, business students in one study with higher levels of education showed more positive attitudes toward conservatives and negative attitudes toward socialists, compared to social science students who showed the reverse (Guimond et al., 1989). However, this work needs to be replicated and updated to reflect global diversity. Indeed, few studies have focused on culturally diverse student populations in non-Western contexts, where the tension between traditional norms and liberal educational values may be particularly salient. Additionally, while these studies looked at the effect of education on conservatism, we are not aware of any work that focuses specifically on the effect of education on gender traditionalism. Current Study The present study moves beyond a correlational examination of gender traditionalism and wellbeing to determine whether change in gender traditionalism predicts higher levels of wellbeing among young adults at an international liberal arts university in the Middle East. Specifically, we examine how gender traditionalism evolves over the first year of university for different types of people and whether shifts in gender traditionalism predicts wellbeing. Our sample is uniquely diverse, including four different first-year cohorts attending a liberal arts college in the Middle East with strong international recruitment. For example, the 2024-2025 academic year had around 2,000 undergraduates from over 120 countries (25% from the local country), strong representation from the four major global religions, and with six different ethnic-racial groups each separately accounting for at least 10% of the student body. The diversity of this sample, and its base in a Global South country, provide an ideal test for how the relationship between gender traditionalism and wellbeing may change according to a confluence of demographic factors. The sample represents diversity not just in nationality, but in language, cultural background, and religious beliefs, among other demographics. These demographic factors are combined in unique ways for each individual, and inline with intersectional theorists, we maintain that individuals can be understood not only through their constituent demographic group memberships and identities, but as a combination of multiple identity facets that combine and interact in innumerable ways (Bowleg, 2008). We capitalized on this intersectionality by using latent class analysis (LCA; Hagenaars & McCutcheon, 2002), a data-driven, person-centered analytic technique designed to account for the interdependence of demographic factors. Hypothesis 1 : Young adults will exhibit a decline in gender traditionalism over their first year at university, consistent with prior research on the liberalizing effects of higher education across multiple cultural contexts (Van Hiel et al., 2018. Hypothesis 2 : The relation between gender traditionalism and wellbeing will differ according to demographic factors such that those with a constellation of more conservative demographic factors (e.g., straight, religious, male) will show that higher levels of gender traditionalism predict better wellbeing (Hypothesis 2A: Gui, 2019, China; Henry & Steiger, 2022; Napier et al., 2010; Soltanpanah et al., 2018) whereas those with a constellation of less conservative demographic factors (e.g., not-straight, non-religious, female) would show that lower levels of gender traditionalism predict better wellbeing (Hypothesis 2B: Zhang et al., 2014, China). Hypothesis 3 : Increases in gender traditionalism (if any) will predict better wellbeing for young adults with more conservative demographic factors (Hypothesis 3A) whereas decreases (if any) will relate to better wellbeing for groups from less conservative factors (Hypothesis 3B). Methods Procedure and Participants The data used in this project was from an ongoing, longitudinal analysis of a cohort sequential sample of internationally diverse students attending a global liberal arts college in the Middle East. This project was funded by The Emirates Foundation through the LSE Middle East Centre Academic Collaboration with Arab Universities Programme as well as funding from New York University Abu Dhabi. We used the f­­irst two-waves of data from three different cohorts beginning in September 2021 with the incoming first-year cohort added each year (Cohort 1 n = 320, Cohort 2 n = 401, Cohort 3 n = 238). T1 indicates data collected their first month of their first year, and T2 data collected the first month of their second year. We excluded from this initial pool any participants who did not have data on the constructs of interest from both time points ( n = 307). After these exclusions, the combined sample n = 652. We report all manipulations, measures, and exclusions in these studies. This sample size was not predetermined to answer these specific research questions. The initial round of analyses showed similar patterns as our final analyses (see Table B in supplemental materials), but two of the profiles were prohibitively small ( n = 20 and n = 30 respectively), so we waited till the next cohort data was ready. The results of a post-hoc power analysis are shared at the conclusion of the Results section. At T1, participants ranged in age from 17-23 ( M = 18.60, SD = .99), 55% were women, 45% were men, and they were ethnically diverse and from predominantly non-Western backgrounds: 25% self-identified as South Asian, 18% identified as Arab, 14% identified as White, 11% identified as East Asian, 9% identified as Black, 9% identified as Multiracial 5% identified as Latinx, 4% identified as Central Asian, 4% identified as Southeast Asian, 1% identified as Other, and < 1% identified as Pacific Islander. Most of the participants (49%) reported being from the same family SES status as their peers, the remaining majority (44%) reported their family being at least somewhat above their peers, 4% reported being at least somewhat lower than their peers, and >1% reported being much higher than their peers. The sample represented diversity in religious denominations with 40% Muslim, 27% Christian, 20% not affiliated, 10% Hindu, 3% Buddhist. Participants were more religious than not with 47% reporting as highly religious, 32% as moderately religious, and 20% as low/not religious. Participants represented 21 of the 26 majors represented on campus, with the top five most common being Economics (14%), Social Research and Public Policy (12%), Psychology (12%), Mechanical Engineering (8%), and Political Science (8%). Regarding sexual attractions (measured at T2), 58% ( n = 381) of participants identified as exclusively attracted to the other gender (i.e., straight), 30% ( n = 197) as at least somewhat attracted to their own gender, and 11% ( n = 74) did not respond to the question. T -Tests and chi-square tests showed that participants who failed to complete the study after Wave 1 did not significantly differ from included participants in terms of gender, SES, sexual orientation, or wellbeing ( t s .072). There were, however significant differences indicating that attrited participants were more likely to identify as Arab ( x 2 (11) = 38.92, p < .001) and Muslim ( x 2 (6) = 21.71, p < .001) as well as report higher levels of religiosity ( t (651) = 3.16, p < .001) and gender traditionalism ( x 2 (6) = 21.71, p < .001). Procedure In accordance with the Declaration of Helsinki, the research protocol was reviewed and approved by the New York University Abu Dhabi Institutional Review Board (HRPP-2021-51). Clinical trial number: not applicable. Before gathering data, participants were briefed about the study and provided their informed consent. For those under 18, informed consent of parents or guardians was required before study commencement. At T1, participants were entered into a prize draw for a chance to win an Apple product (e.g., Airpod Pros, Apple watch), and at T2 participants were paid a small sum in addition to being entered into a similar drawing as that of T1. We used Qualtrics to gather data through an online self-reported survey. Transparency and Openness We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study, and we follow journal article reporting standards for quantitative research in psychology (Appelbaum et al., 2018). All analysis code and research materials are available at [https://osf.io/tcfwk/?view_only=65f2f67dc5b6475186ec28b28fe4b6a3]. This study’s design and its analysis were not pre-registered. Measures Conservatism To assess conservatism, we measured left-right orientation where participants responded to a single item asking, “In political matters, people talk of ‘the left’ and ‘the right.’ How would you place your views on this scale, generally speaking?” Responses were a on 10-point scale from 1 ( left ) to 10 ( right ). This item has been validated on Western samples (Evans et al., 1996) and Global South samples (Shinn & Jhee, 2005). Ethnic-Racial Identity Participants identified ethnic-racial groups to which they belonged from a list; multiple choices were allowed. If an individual affiliated with multiple groups (or if they wrote in a multiracial identity), they were labeled as multiracial. For use in the Latent Class Analysis, an ethnic-racial dummy variable was created with one level being Arab/South Asian and the other level composed of all other ethnicities. Gender Identity Participants reported whether they currently describe their gender to others as male, female, not on the gender binary, or “prefer to self-describe.” Due to a lack of power to make adequate comparisons, individuals who reported a non-binary gender ( n = 16) along with those with missing data ( n = 4) were excluded from the analysis. Gender Traditionalism In the last decade, it has become increasingly clear that gender traditional ideologies are not monolithic. Three items were used to capture a male-dominance aspect of gender traditionalism (taken from the WVS; Haerpfer et al., 2020): “Men should make the really important decisions in the family,” “On the whole, men make better political leaders than women do,” and “When jobs are scarce, men should have more rights to do a job than women.” Responses were on a 7-point scale from 1 ( Strongly Disagree ) to 7 ( Strongly Agree ). Standardized Cronbach’s alphas for the scales indicated acceptable fit: W1 α = .87, W2 α = .85. Home Country / Region Participants responded to the following question: “Think of the country you consider to be your home country. This might be the country where you spent most of your childhood, or it might be the country that you would represent, for example, at the United Nations or in the Olympics. If you consider more than one country to be your home, pick the one you feel closest to. Where is this country?” Participants chose from a list of 22 different regions (e.g., North Africa, East Asia) that were further condensed according to the United Nations Regional groups of Member States with the following five regions: African States, Asia-Pacific States, Eastern European States, Latin American and Caribbean States, Western European and other States (United Nations, 2025). Religion Participants were presented with a list of religious denominations options ( Do not belong to a denomination, Christian, Jewish, Muslim, Hindu, Buddhist ) and asked whether they belonged to one. A free response form was included for participants whose denomination was not listed. For use in the Latent Class Analysis, a religion dummy variable was created with one level being the majority religion identification (Muslim) and the other level being all other religious denominations. Religiosity Religiosity was assessed with a single item from the World Values Survey (WVS; Haerpfer et al., 2020) asking participants how important religion is to their lives on a 7-point scale, from 1 ( Completely Unimportant ) to 7 ( Very Important ). Sexual Orientation Participants chose one of the following options to report the gender of the people to whom they felt attracted over the past year: All men, mostly men, mostly men but some women, about equally men and women, mostly women but some men, mostly women, all women. Participants could also choose, “I have not had any physical attraction to anyone.” These responses were then recoded using participant gender to create a dichotomous sexual minority variable where 0 = no degree of same-gender attraction and 1 = at least some degree of same-gender attraction . Wellbeing Subjective wellbeing was assessed with items representing four different areas of wellbeing. A life satisfaction item, “All things considered, how satisfied are you with your life as a whole these days?”, was on a scale from 1 ( completely dissatisfied ) to 10 ( completely satisfied ) from the WVS (Haerpher et al., 2020). To assess self-esteem, we used the Single Item Self Esteem Scale, “I have high self-esteem,” on a scale from 1 ( not very true of me ) to 7 ( very true of me ) (Robins, Hendin, & Trzesniewski, 2001). A loneliness item, “How often do you feel lonely,” on a scale from 1 ( never ) to 5 ( always ) was adapted from the loneliness item developed by Mund and colleagues (2022). Finally, we used a mental health item, “How would you describe your mental health over the past year?” on a scale from 1 ( very bad ) to 7 ( very good ) (Robins et al., 1981). We combined these items into a scale with acceptable reliability, standardized Cronbach’s alpha = .72. Analytical Approach Latent Class Analysis Latent Class Analysis is a person-centered modeling technique that attempts to identify latent subpopulations within a population by classifying people into profiles based on differences in personal and/or environmental attributes (Spurk et al., 2020). This approach is ideal for our project where we expect that various demographic factors will interact to create specific profiles of relations between gender traditionalism and wellbeing. Following the literature review, the demographic factors we included in the LCA include conservatism, ethnic-racial identity (Arab/South Asian, else), gender identity, religious affiliation (Muslim, else), religiosity, and sexual orientation. To determine different profiles, iterative models are compared to identify the optimal solution using r package poLCA (Linzer & Lewis, 2011). We used the following indices to determine fit: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the adjusted Vuong-Lo-Mendell-Rubin Likelihood Ratio Test (VLMR). Better fit is indicated by lower values for the AIC and BIC, and p-values below .05 for the VLMR (Nylund et al., 2007). We also examined posterior probabilities, which range from 0 to 1, with values closer to 1 indicating greater confidence that individuals were correctly assigned to their respective profiles (Lanza et al., 2007). Theoretical rationale, interpretability, and parsimony were also considered in determining the most meaningful and valid profile solution (Asparouhov & Muthén, 2014; Nylund et al., 2007). After estimating the optimal number of profiles based on the factors listed above, a three-step method was employed to examine whether region of home country was associated with profile membership (R3Step; Asparouhov & Muthén, 2014; Vermunt, 2010). The R3Step procedure involves three stages: first, estimating the latent profiles; second, generating a nominal “most likely” class variable for each participant based on the posterior distribution from the latent profile analysis (LPA), which accounts for measurement error; and third, using multinomial logistic regression to predict profile membership using independent variables—in this case, the different home country regions. Following the initial LCA and R3Step analysis, the Bolck, Croon, and Hagenaar (BCH) approach was used to assess whether there were significant mean differences in gender traditionalism across the identified profiles (Bakk & Vermunt, 2016; Bolck et al., 2004). This method applies a form of weighted multiple group analysis, where the weights reflect the measurement error associated with the latent profile variable. Specifically, the measurement error for the “most likely” profile variable is estimated, and the LPA is subsequently re-estimated using this variable, with the measurement error fixed to the previously estimated values. For this model, mean differences in gender traditionalism at T1, were then assessed across profiles. Measurement Invariance To determine that any significant differences in the focal analyses would be associated with the constructs themselves rather than artefacts of the measurement tool, we conducted tests of measurement invariance (configural, metric, scalar; Widaman & Reise, 1997) using the R package Lavaan (Rosseel, 2012) across all categorical constructs used in the subsequent latent profile analysis: cohort, time, sexual orientation, gender, religion (Muslim or not, Muslim being the dominant religion of the sample), and ethnic-racial background (Arab/South Asian or not). If any item failed at the metric or scalar levels, we tested for partial invariance (Byrne et al., 1989). Change negative loglikelihood scores with Satorra-Bentler Scaled Chi-Square adjustments were used as fit indices (significant scores indicate a worse fit for more constrained models). Classification uncertainty was accounted for using the average posterior probabilities by most likely class membership where higher probabilities (around .07; Masyn, 2013) indicate successful categorization (Van Lissa et al., 2023). Latent Change Score Models To test the hypotheses, we conducted a multigroup latent change score model (LCSM) that tracked change in gender traditionalism from T1 to T2 and then regressed the resultant change variable onto a wellbeing at T2 (see Figure 1 for a path diagram). The multigroup aspect compared outcomes across the four profiles produced in the LCA. A LCSM was chosen because the random intercepts separate out within-person construct stability over time, thus modeling time-invariant confounding variables that are omitted in traditional cross-lagged panel models (Steyer et al., 2000). Additionally, LCSM are favored over models like the Random-Intercept Cross-Lag Panel Models which do not handle highly individual change patterns over time (Usami et al., 2019), as we might expect from the diversity of our data. The model was estimated using the R package lavaan (Rosseel, 2012). Model fit was determined with a comparative fit index (CFI) score above 0.95, a Tucker Lewis Index (TLI) above 0.95, a root mean square error of approximation (RMSEA) score below 0.06, and a standardized root mean squared residual (SRMR) of .08 or lower (Hu & Bentler, 1999). Results Descriptive Statistics Descriptive analyses were conducted on variables of interest (see Table 1 for means, standard deviations, and correlations). Histograms and normality tests indicated that all variables met assumptions of normality with measures of skew and kurtosis between the thresholds of ± 2 and ± 7 respectively (Curran et al., 1996 ). Measurement Invariance Measurement invariance analyses revealed that most constructs achieved scalar invariance except for religious affiliation and sexual orientation (see Table 2 for invariance testing results). For the item “Men should make the really important decisions in the family,” Muslim and exclusively heterosexual participants showed significantly higher intercepts than their counterparts in the sample. We interpret this as a feature rather than a bug of our sample (see Kusano, Napier, & Jost, 2025, multinational), plus the item correlates well with the others in the scale, thus we retained it. Table 1 Sample descriptive statistics Total Variables n Range M SD Skew Kurtosis 1. 2. 3. 4. 5. 6. 1. SES 649 1–10 5.38 1.72 0.03 -0.28 - .01 − .15** − .10* − .11** − .09* 2. Religiosity T1 645 1–7 4.83 2.01 -0.56 -1.02 - .34*** .32*** .30*** .08* 3. Conservatism T1 640 1–7 2.88 1.42 0.64 -0.15 - .43*** .40*** .10* 4. Gender Trad. T1 601 1–7 2.27 1.33 0.95 0.11 - .67*** .20*** 5. Gender Trad. T2 634 1–7 2.33 1.37 1.07 0.60 - .20*** 6. Wellbeing T2 629 1.25–8 4.95 1.28 -0.32 -0.15 - * p < .05 ** p < .01 *** p < .001 Note Gender Trad. = gender traditionalism Table 2 Results for tests of measurement invariance Cohort (Class of 2025 vs Class of 2026 vs Class of 2027) Model tested χ 2 df p χ2Δ χ2 Δ p Invariant Items CFI TLI RMSEA Configural 12.62 15 0.983 7,8,9 1.00 1.00 .000 Metric 27.56 23 0.854 14.95 .060 7,8,9 .998 .995 .032 Scalar 39.56 31 0.904 12.00 .151 7,8,9 .995 .993 .039 Time (Beginning of 1st Year vs Beginning of 2nd Year) Model tested χ 2 df p χ2Δ χ2 Δ p Invariant Items CFI TLI RMSEA Configural 5.32 5 .379 7,8,9 1.00 1.00 .010 Metric 7.33 7 .395 2.01 .366 7,8,9 1.00 1.00 .009 Scalar 11.97 10 .287 4.64 .200 7,8,9 .999 .998 .018 Sexual Orientation (Exclusively Heterosexual vs Not) Model tested χ 2 df p χ2Δ χ2 Δ p Invariant Items CFI TLI RMSEA Configural 29.20 10 .001 7,8,9 .989 .968 .085 Metric 39.94 14 < .001 10.74 .030 .985 .969 .083 Partial 35.15 12 < .001 5.95 .078 8,9 0.988 .965 .083 Gender (Women vs Men) Model tested χ 2 df p χ2Δ χ2 Δ p Invariant Items CFI TLI RMSEA Configural 19.03 10 0.04 7,8,9 0.984 0.953 0.096 Metric 28.05 14 0.014 9.02 0.061 7,8,9 0.976 0.948 0.101 Scalar 34.11 18 0.012 6.06 0.195 7,8,9 0.972 0.953 0.096 Religious Affiliation (Muslim vs Not) Model tested χ 2 df p χ2Δ χ2 Δ p Invariant Items CFI TLI RMSEA Configural 131.81 16 < .001 7,8,9 0.941 0.890 0.158 Metric 140.23 20 < .001 8.42 0.077 7,8,9 0.939 0.908 0.144 Scalar 170.00 24 < .001 29.77 0.001 0.926 0.907 0.144 Partial 101.35 16 < .001 1.58 0.453 8,9 0.933 0.909 0.141 Ethnicity (Arab/South Asian vs Not) Model tested χ 2 df p χ2Δ χ2 Δ p Invariant Items CFI TLI RMSEA Configural 97.83 10 < .001 7,8,9 0.931 0.871 0.168 Metric 99.77 14 < .001 1.94 0.747 7,8,9 0.933 0.899 0.148 Scalar 114.13 18 < .001 14.63 0.006 0.924 0.905 0.144 Partial 101.35 16 < .001 2.98 0.493 8,9 0.912 0.900 0.137 Note. We used Widaman and Reise’s ( 1997 ) paradigm of configural invariance (equivalent items used across groups) metric invariance (factor loadings constrained to be equivalent across groups), and scalar invariance (the intercepts constrained to be equal across groups). The p value of the chi-square change (χ2 Δ p ) determines whether or not a model is significantly worse fitting than the previous, less constrained model. Thus, a χ2 Δ p value higher than .05 for subsequent models indicates that the more constrained model (e.g., less invariant across construct of interest) does not have significantly worse fitting should continue to the next level of constraints. Latent Cultural Profiles We conducted a LCA to assess the effect of demographic factors on the relation between gender traditionalism and wellbeing. The following T1 variables were used to create latent classes: gender identity, political conservatism, racial-ethnic background (Arab/South Asian or not), religious affiliation (Muslim or not), religiosity, and sexual orientation. We accepted a four-class model with the best fit of the converging models that had excellent posterior probabilities (ranged from .98 to 1.00). See Table A in the supplemental materials for the full model fit and comparisons. Four profiles emerged, which we interpreted as follows. The first included a Progressive Western Women profile ( n = 170, 30%) which was characterized as 87% women, 100% non-Arab and non-Muslim, 57% not exclusively heterosexual, low conservatism (16%), and low religiosity (26%). For a depiction of probabilities for profile membership, see Figure 2. We labeled the second profile Straight Non-Muslim Men ( n = 158, 28%) were 87% men, 26% Arab, non-Muslim (93%), mostly exclusively heterosexual (84%), moderately conservative (38%) and religious (34%). Profile 3 was labelled Progressive Arab Women ( n = 105, 18%) 76% women, 78% Arab, 61% Muslim, 53% not exclusively heterosexual, very low conservatism (7%), and moderately religious (36%). Finally, Conservative, Religious Muslims ( n = 135, 24%) were almost evenly divided by gender (57% women), 72% Arab, 100% Muslim, 89% exclusively heterosexual, more conservative (65%), and highly religious (100%). Home country / region as an Indicator of Latent Profiles Next, R3Step analyses were conducted to examine how home country / region was associated with latent profile membership (see Table 3 for full R3Step outcomes). The results indicated that participants from Western Europe were significantly more likely to belong to the Progressive Western Women (6.87 times, p = .006) and Straight Non-Muslim Men classes (4.92 times, p = .023) compared to the Conservative Religious Muslims profile. Participants from Africa were more likely to belong to Straight Non-Muslim Men compared to the Conservative Religious Muslims (1.19 times, p = .012) and less likely to belong to the Progressive Arab Women class compared to Conservative Religious Muslims (42% odds, p = .030). Table 3 . Odds ratios and p-values for the R3Step method showing likelihood of being sorted into latent profiles based on world region of home country. Class Reference: Conservative Religious Muslims UN-Region Reference: Asia-Pacific Odds Ratios Standard Error p-value Progressive Western Women Africa 0.80 0.36 .539 Progressive Western Women Eastern European 1.54 0.87 .534 Progressive Western Women Latin Am & Carib 1.26 0.77 .420 Progressive Western Women West Europe, Else 6.87 0.70 .006 Conserv. Non-Muslim Men Africa 1.19 0.31 .012 Conserv. Non-Muslim Men Eastern European 3.23 1.38 .562 Conserv. Non-Muslim Men Latin Am & Carib 1.22 0.78 .462 Conserv. Non-Muslim Men West Europe, Else 4.92 0.70 .023 Progressive Arab Women Africa 0.42 0.39 .030 Progressive Arab Women Eastern European 2.22 0.58 .675 Progressive Arab Women Latin Am & Carib 0.73 0.87 .103 Progressive Arab Women West Europe, Else 3.14 0.73 .116 Note. All values are R3Step logistic regression analyses. If the odds ratio is above one, the person is more likely to be assigned to the latent group (in comparison to the reference group); if the odds ratio is below one, the person is less likely to be assigned to the latent group (in comparison to the reference group). Gender Traditionalism Mean Differences across Latent Profiles BCH procedures were used to examine whether mean levels of gender traditionalism differed across the identified profiles. The results revealed that the only significant difference ( p = .045) in gender traditionalism across profiles was at T1 between Progressive Western Women ( M = 2.04) and Conservative Religious Muslims ( M = 2.49). Full results of the BCH procedure are presented in Table 4. Gender Traditionalism Change Predicting Wellbeing To test our hypotheses, we conducted a LCSM as a multi-group model using the four classes produced from the LCA. The model had overall good fit: c 2 (84) = 1681.19, p < .001, CFI = .988, TLI = .995, RMSEA .027 (.000, .068), SRMR = .028. For full results, see Table 5. Hypothesis 1, that people would decline in gender traditionalism over their first year at university, was not supported, and was in fact reversed for three of the four groups. Progressive Western Women ( B = .79, SE = .31, p = .011), Straight Non-Muslim Men ( B = 1.25, SE = .30, p < .001) and Conservative Religious Muslims ( B = .71, SE = .28, p < .010) showed significant increase in gender traditionalism from T1 to T2. Only Progressive Arab Women showed no significant change ( B = .45, SE = .28, p = .065). Hypothesis 2A, that higher levels of gender traditionalism would be associated with better wellbeing for more conservative, religious and heterosexual individuals was supported. Hypothesis 2B, that more progressive individuals would show a deleterious effect, was not supported. Higher levels of gender traditionalism predicted better wellbeing for Conservative Religious Muslims ( B = .21, SE = .07, p = .004) but not for any other group: Straight Non-Muslim Men ( B = .14, SE = .11, p = .207), Progressive Arab Women ( B = .25, SE = .16, p = .120) and Progressive Western Women ( B = .18, SE = .16, p = .247). The commonality for the palliative aspect of endorsing traditional gender beliefs seemed to be about religiosity or an Islamic religious identification, more so than conservatism. Table 4 Means and equality tests on gender traditionality at T1 and T2 across classes using the BCH procedure. T1 T2 Group n Mean Gender Trad 95% CI Mean Gender Trad 95% CI 1. Progressive Western Women 170 2.04 [1.85, 2.23] 2.18 [1.98, 2.38] 2.Straight Non-Muslim Men 158 2.20 [2.01, 2.40] 2.31 [2.10, 2.52] 3. Progressive Arab Women 105 2.16 [1.92, 2.40] 2.20 [1.96, 2.43] 4. Conservative Religious Muslims 135 2.49 [2.23, 2.75] 2.58 [2.42, 2.84] Equality tests of Means Chi square p-value Chi square p-value 1 vs 2 0.01 .919 0.01 .945 1 vs 3 0.01 .893 0.00 .992 1 vs 4 9.24 .045 0.02 .886 2 vs 3 0.00 .979 0.00 .956 2 vs 4 0.02 .878 0.01 .940 3 vs 4 0.03 .864 0.02 .896 Overall 7.76 .051 3.16 .368 Note. Gender traditionality range = 1–7. Table 5 Selected output for the Latent Change Score Model Progressive Western Women ( n = 170) Straight Non-Muslim Men ( n = 158) Progressive Arab Women ( n = 105) Conservative Religious Muslims ( n = 135) B SE p B SE p B SE p B SE p H1: Mean gender trad change .79 .31 .011 1.25 .30 < .001 .45 .24 .065 .71 .28 .010 H2: Mean gender trad on wellbeing .18 .16 .247 .14 .11 .207 .25 .16 .120 .21 .07 .004 H3: Gender trad change on wellbeing .13 14 .359 .10 13 .415 − .06 .22 .784 .36 .14 .009 Mean gender trad 1.85 .10 < .001 2.83 .12 < .001 1.95 .13 < .001 3.07 .16 < .001 Gender trad change with gender trad mean − .37 .17 .025 − .48 .10 < .001 − .22 .14 .114 -0.32 .08 .010 Gender trad mean variance .75 .28 .006 1.22 .22 < .001 .95 .29 .001 2.94 .45 < .001 Gender trad change variance .59 .20 .004 .88 .19 < .001 .44 .16 .005 1.04 .29 < .001 Note. Significant outcomes are highlighted. Gender trad = gender traditionalism. Mean gender trad = mean gender traditionalism for each group. Gender trad change with gender trad mean = the correlation between the means of gender traditionalism for each group and the rate of change of gender traditionalism for that group. Gender trad mean variance = between-person variance (differences not explained by measurement error) on mean gender traditionalism. Gender trad change variance = between-person variance (differences not explained by measurement error) on mean change of gender traditionalism. A post-hoc ANOVA indicated that groups more likely to contain men had significantly higher levels of gender traditionalism. Hypothesis 3A, that changes in gender traditionalism would predict improvements in wellbeing was again supported while Hypothesis 3B (deleterious effect for less conservative) was not. For Conservative Religious Muslims , an increase in gender traditionalism predicted improved wellbeing ( B = .36, SE = .14, p = .009); for the remaining groups there was no significant relation: Progressive Western Women ( B = .13, SE = .14, p = .359), Progressive Arab Women ( B = − .06, SE = .21, p = .784), and Straight Non-Muslim Men ( B = .10, SE = .13, p = .415). Of interest, the correlations between gender traditionalism and gender traditionalism change were significant for each group except Progressive Arab Women such that those with higher levels of gender traditionalism had lower rates of increase over time ( r ’s < − .22, p ’s < .001). Finally, all four groups still showed significant between-person variance (differences not explained by measurement error) on means of gender traditionalism and gender traditionalism change over time ( B ’s > .44, p ’s < .007).] Post hoc power analyses were conducted using RAMPath, a Monte Carlo-based method in R (Zhang & Liu, 2018 ), to determine whether the participant number in each group was large enough to detect small to medium effect sizes based on estimated levels of effects and variance. The results indicated that each of the four groups achieved power of at least .80. Gender Traditionalism Predicting Wellbeing at the Individual-Level A sensitivity regression analysis was conducted to assess whether gender traditionalism affects well-being at the individual level and whether this is moderated by any of the demographic factors used in the LCA. Specifically, we regressed gender traditionalism T1 on wellbeing T2 and tested to see whether gender (woman, man), world region (Asia-Pacific, Africa, Eastern Europe, Latin America & Caribbean, Western Europe and other Developed Nations), religion (Muslim, not religious, Christian, Buddhist, Hindu, Jewish), conservatism, sexual orientation, and religiosity moderated this relation. Results indicated that the overall model did not explain much variance: R 2 = .11, Adj R 2 = .03, F (27, 322) = 1.43, p = .082, that no interactions were significant, and that only three predictors significantly related to wellbeing: being from Eastern Europe ( b = .56, p = .034) and Western Europe/other developed nations ( b = .62, p = .035) relative to being from the Asia-Pacific region and being attracted to own-gender ( b = − .13, p = .020; see Table C in the supplemental materials for the full output). These largely null results can be interpreted to support our decision to identify latent subgroups within our sample and assess their mean levels and patterns of change accordingly. Discussion The present study sought to accomplish two aims. First, we sought to confirm the relation between gender traditionalism and wellbeing by testing whether any change in gender traditionalism over the course of young adult’s freshman year at university would predict wellbeing. Second, we studied these trends with a largely understudied sample of students from across the globe studying at a progressive university in the Middle East. Contrary to Hypothesis 1, that young adults would decline in gender traditionalism during the first year at university, the majority of participants showed significant increases in gender traditionalism. The palliative effect of gender traditionalism on the wellbeing of religious, conservative individuals from Hypothesis 2, was supported. Young adults in the Conservative Religious Muslim groups (both men and women) showed better wellbeing with more gender traditionalism. However, there was no support for the deleterious relation between gender traditionalism and wellbeing from those with less conservative backgrounds. Similarly, for Hypothesis 3, that change in gender traditionalism would relate to wellbeing above and beyond mean levels of gender traditionalism the palliative effect was supported: young adults in the Conservative, Religious Muslim group, an increase in gender traditionalism over time predicted higher levels of wellbeing. There was no support for any deleterious effect. The main aim of this paper was to provide a more rigorous test of the association between gender traditionalism and wellbeing by exploring directionality within a longitudinal analysis on a diverse, non-Western sample. This is the first example, to our knowledge, that provides evidence that heightened wellbeing is attributed to a change in gender traditionalism. Importantly, this effect only came through for young adults with particularly high cultural levels of religiosity, conservatism, and non-Western backgrounds. One way that this university sample appears to differ from more homogenous, Western samples is that, far from reporting liberalization, many of these students reported increasing their gender traditionalism during their time at university. One explanation for this outcome is that our sample is uniquely diverse. It is composed of students from all over the globe attending university in a Middle Eastern country, which sets our sample apart from typical research conducted on more homogenous samples in WEIRD contexts (Heinrich et al., 2010). While the country of this university is growing more tolerant, it is still within the Middle East North Africa region, and many students were also from this area, which scores lowest of all regions on gender parity (World Economic Forum, 2024 ). Our sample participants may have been early socialized in a more gender-restrictive environment, thus the liberalizing effect seen in other universities across the globe (Van Hiel et al., 2018 ) did not materialize in our sample, and in fact was reversed. Additionally, the diversity of students and lecturers at this university provide more heterogenous ideological developmental pathways. For example, when presenting initial findings of this work to a gathering at of study participants and other university personnel, one student commented on her own experience with ideological change (or lack of it) during her time there. She shared how she grew up in a large, liberal American city and during her time at home was surrounded largely by liberal homogeneity. Coming to school in the Middle East was the first time that she had been exposed to different, conservative ways of thinking, and she came to appreciate that ideology and became more conservative as a result. Progressive Arab Women emerged as a profile with trends that differed from the other emergent profiles. First, they were the only profile that did not significantly increase in their gender traditionalism over time. Additionally, unlike the other three profiles where the more traditional they were, the less likely they were to increase in traditionalism over time, the degree of traditionalism for Progressive Arab Women did not predict their rate of change. These results do not seem to arise from intragroup variance: Progressive Arab Women had the lowest variance among their patterns of change over time. Finally, despite moderate religiosity, Progressive Arab Women reported the lowest levels of conservatism across all four profiles. These findings complement previous work from the global south, namely China (Gui, 2019) and Pakistan (Rashid et al., 2022), that indicate that even in the same cultural context, women have significantly different experiences with gender traditionalism compared to men. Recent empirical work in the MENA region indicates increasing public support among women for gender equality, especially in urban settings and among younger cohorts (Thomas & Kasselstrand, 2019). Legal and policy reforms, such as those under Saudi Arabia’s Vision 2030 expanding women’s labor market access and changes to personal status laws, are increasingly supportive of women’s agency (Polok, 2024 ). Qualitative studies among Arab Bedouin women show that reflexive narratives about birth rates, marriage norms, and family expectations are leading to significant shifts in behavior, as women negotiate, resist, or reform traditional conventions that they perceive as constraining (Zoabi & Fuller, 2024 ). These developments suggest that those fitting the Progressive Arab Women profile show not simply superficial change, but deeper processes of identity, legal, and social transformation. This is an avenue of research rich with implications for both cultural change and attitude related wellbeing. Table 6 Limitations Table Category Limitation Implication Justification Sample Attrition Arab, Muslim, more religious, and more gender-traditional participants were less likely to participate in Wave 2. May bias results and underrepresent more conservative perspectives; limits reproducibility. These groups were still well-represented and produced the most significant outcomes. Latent Class Heterogeneity Within-group variance in latent classes (e.g., men in 'Progressive, Religious Women' group). Reduces clarity of group-level interpretations; complicates generalizability. Group compositions were enumerated in text and figure to identify heterogeneity. Measurement Error Ambiguity in sexual attraction item in a conservative context; wellbeing composite is untested despite good reliability. Potential misreporting and construct validity concerns; affects reproducibility and internal validity. Reliability for the wellbeing measure was good (α = .72) for such a heterogeneous sample. Statistical Power One subgroup (Secular, Non-Muslim Women) did not meet 80% power threshold. Reduced confidence in subgroup-specific findings; limits robustness. The three other subgroups all had power which exceeded 80%. Cultural Determinism Risk Findings may be interpreted as culturally deterministic without deeper exploration of intra-group variability or agency. Limits theoretical nuance and may oversimplify cultural dynamics. We utilized a person-specific approach, Latent Class Analysis, to gain as much intrasample complexity and nuance as possible. Oversimplified categorical variables in LCA (e.g., sexual Identity) Sexual orientation, ethnic-racial identity, and gender treated as binary (same-gender attraction vs. not). Fails to capture complexity of identities; limits inclusivity and theoretical depth. Nuance was lost by our choice to dichotomize, but it enabled more variables to be included in the LCA. Additionally, regarding sexuality, differentiating between those willing to report even a degree of same-gender attraction, and those who are not is likely a meaningful distinction in this cultural context. Contextual Generalizability Conducted in a unique, international liberal arts university in the Middle East. Limits generalizability to other educational or national contexts. The unique context provides valuable insights but limits applicability to traditional WEIRD samples. Limitations and Future Directions See Table 6 for a simplified presentation of limitations, implications, and justifications. One limitation of the study was manifest in sample attrition. Participants who were Arab, Muslim, more religious, and more gender traditional were less likely to participate in the second wave of the study. Our expectation is that this attrition is the result of the perceived liberal bent of the survey where sensitive issues like attraction, gender ideology, and democratic freedom were assessed. These student demographics were still strongly represented in our study (over 50%) and drove many of the findings, however, the attrited participants might have shown even starker patterns. Our sample was incredibly diverse, which provides a rich addition to the literature body, but can also be difficult to handle statistically. To include a greater number of demographic factors in the LCA, we chose to condense the categorical variables into dummy codes with two levels. While this reduced the variability in these individual variables, having a broader array of factors is more valuable for this project. Although each of the four items in our wellbeing measure are previously validated and reliable single-item measures, our composite approach is previously untested. The reliability was good for such a heterogenous sample ( a = .72), but this measure may have introduced some error. The relation between gender traditionalism and wellbeing is complex and nuanced. The longitudinal data and our structural equation model improved on previous cross-sectional work by clearly identifying how gender traditionalism drives wellbeing, thus eliminating the concern of potential unmeasured confounds in the immediate relation. Still, there was a great deal of heterogeneity in the classes produced by our LCA, and the literature identifies other variables that might moderate the gender traditionalism—wellbeing link. These constructs include system justification ideology (Jost, 2019 ; Napier et al., 2010 ), gender role satisfaction (i.e., the degree to which someone feels personally fulfilled with their gender roles) or the potential discrepancy between gender role attitudes and gender role expression (e.g., those with less conservative ideology who are nonetheless compelled toward more traditional gender role expression; Soltanpanah et al., 2018 ; Sweeting et al., 2014 ). Regarding the exploration into how gender traditionalism and wellbeing might affect sexual minorities, we did not assess internalized homophobia and in-group identification, both of which likely play a role (Suppes et al., 2019 ). Conclusion This study examined the dynamic relationship between gender traditionalism and wellbeing among university students in a diverse, international setting. Contrary to expectations, most students increased their gender traditionalism over their first year. For students with more markers of conservatism, this increase over time predicted better wellbeing. As such, our study provides conclusive evidence that the relation between gender traditionalism and wellbeing for some groups of people is truly due to gender traditionalism rather than other potentially confounding factors. Our findings highlight the cultural complexity of gender ideology development in young adulthood and suggest that ideological shifts—and their psychological consequences—follow culturally contingent pathways. Declarations Author Contribution M.N.: Conceptualization, Data curation, Investigation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – reviewing and editing. P.H.: Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – reviewing and editing. Data Availability All analysis code and research materials are available at [https://osf.io/tcfwk/?view_only=65f2f67dc5b6475186ec28b28fe4b6a3]. This study’s design and its analysis were not pre-registered. References Adler, N. E., Epel, E. S., Castellazzo, G., & Ickovics, J. R. (2000). Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women. Health Psychology , 19 (6), 586. http://dx.doi.org/10.1037/0278-6133.19.6.586 Amarachi, M. O., Jun, K. N., & Ifeanyichukwu, A. O. (2020). 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G. West (Eds.), The science of prevention: Methodological advances from alcohol and substance abuse research (pp. 281–324). American Psychological Association. Wilhelm, J., Schober, P. S., & Sánchez Guerrero, L. (2023). Gender ideologies across the transition to adulthood in Germany: How early romantic relationships slow down the egalitarian trend. Advances in Life Course Research , 58 , 100574. https://doi.org/10.1016/j.alcr.2023.100574 World Economic Forum (2024). The Global Gender Gap Report 2024 . World Economic Forum. https://www.weforum.org/publications/global-gender-gap-report-2024/​QoG Data Finder. Zhang, J., Zhao, S., Lester, D., & Zhou, C. (2014). Life satisfaction and its correlates among college students in China: A test of social reference theory. Asian Journal of Psychiatry , 10 , 17–20. https://doi.org/10.1016/j.ajp.2013.06.014 Zhang, Z., & Liu, H. (2018). Sample size and measurement occasion planning for latent change score models through Monte Carlo simulation. Longitudinal multivariate psychology (pp. 189–211). Routledge. Zoabi, H., & Fuller, L. (2024). Reflexivity and the change in women’s status: The case of Arab Bedouin women in Israel. Cogent Social Sciences , 10 (1), 2294561. https://doi.org/10.1080/23311886.2023.2294561 Additional Declarations No competing interests reported. Supplementary Files Supplemental.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8561546","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":641549722,"identity":"c13c06b5-250b-48dc-a8ef-d1b5f37e8222","order_by":0,"name":"Matthew Nielson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie2RMQrCQBBFRxa0GU27Nt5AmBBQBPEsGwJbCVpahRwgBxD0EDnChgVtBFvFRglYWcQuhaCrnYIxpcW+YophHvOHAbBY/hQFU1Mb0Xvvh0Km4nNKVFTgpXBRUelGzVTlFHb67WzFr8VwAg19ZLj5rvRUS6Rz0t5gIQN3LuQgQkkMd2UKkkZSfnIYuxkKbTKOgWH+Q7lR6Cf7ba5v4k7gXCooQMxPdljLQCgC/txSFsykSmNzC22k58YyoDo/U7osO38de3kxCzu01mdeDEfkOMHpeFl9V4B9NupQ4ZEWi8ViKecBDT1RICtqQ/0AAAAASUVORK5CYII=","orcid":"","institution":"University of Exeter","correspondingAuthor":true,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Nielson","suffix":""},{"id":641549723,"identity":"308089e2-5b41-4739-bf8e-6f9af56ff8b0","order_by":1,"name":"PJ Henry","email":"","orcid":"","institution":"New York University Abu Dhabi","correspondingAuthor":false,"prefix":"","firstName":"PJ","middleName":"","lastName":"Henry","suffix":""}],"badges":[],"createdAt":"2026-01-09 13:38:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8561546/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8561546/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109759796,"identity":"094fac13-1943-4d90-bd37-24d3d5c5d987","added_by":"auto","created_at":"2026-05-22 07:27:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePath Diagram for Latent Change Score Model of Gender Traditionalism Predicting Wellbeing\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8561546/v1/0d9bb4768768f84e7e98d593.png"},{"id":109761169,"identity":"e58f8b6e-2406-44a0-b1e6-9c508aa55bcc","added_by":"auto","created_at":"2026-05-22 07:29:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":139414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eProbabilities for Profile Membership in the Four-Class Latent Class Analysis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8561546/v1/f1ab60e403b43c58649db0d7.png"},{"id":109764047,"identity":"de40fd8b-8557-4dc0-88b7-53553d0d0d68","added_by":"auto","created_at":"2026-05-22 07:36:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":870041,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8561546/v1/edefbc5f-abe1-41e3-a552-74eae083e3db.pdf"},{"id":109473169,"identity":"e5892882-e4b1-46c0-b29c-a28f8bcfc456","added_by":"auto","created_at":"2026-05-18 13:36:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31522,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental.docx","url":"https://assets-eu.researchsquare.com/files/rs-8561546/v1/2c6d6664ebba5b4ba07da744.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender Traditionalism and Wellbeing: Change across First Year at a Global University","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIndividual-level attitudes, such as the endorsement of traditional gender roles that women should be submissive, feminine nurturers and men should be masculine, dominant providers (i.e., gender traditionalism), are internalized and can shape personal identity and daily experiences above and beyond external socialization (Nielson et al., 2020) in ways that can influence psychological health (King et al., 2020; Jaehn et al., 2020). While numerous studies have found that more egalitarian gender attitudes (at either the individual or national level) are associated with better wellbeing\u0026mdash;across diverse cultural contexts including Russia, China, Australia, and the Netherlands (Baird et al., 2019; Chen et al., 2023; Jaehn et al., 2020; King et al., 2022; Read \u0026amp; Grundy, 2011; Sweeting et al., 2014; Van De Vijver, 2007; Zhang et al., 2014)\u0026mdash;some findings are mixed (Amarachi et al., 2020; Gui, 2019) or even contradictory (e.g., Napier et al., 2010; Nilles et al., 2023; Tesch-R\u0026ouml;mer et al., 2008). Much of this research is cross-sectional and has been conducted on samples that are predominantly Western or with little internal diversity with which to test the effect of demographic factors (e.g., Coleman \u0026amp; Andersson, 2024; Herreen et al., 2021; Homan \u0026amp; Burdette, 2021; King et al., 2022; Read \u0026amp; Grundy, 2011; Vantieghem et al., 2014).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA longitudinal assessment that differentiates between cultural profiles using change in individual-level gender traditionalism to predict wellbeing could help sort out some of the literature\u0026rsquo;s inconsistencies by testing how different types of people might respond differently to traditional gender role endorsement. For example, young adults from countries and with backgrounds where conservative/traditional gender attitudes are more prevalent may show a different pattern from those from other countries (Amarachi et al., 2020; Gui, 2019; Rashid et al., 2022). Furthermore, a longitudinal analysis allows us to test (for the first time to our knowledge) the dynamics of this relation during the first year of college \u0026ndash; a pivotal period in young adult development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe purpose of this paper is twofold: 1) to test, within a single study and a uniform methodology,\u0026nbsp;the longitudinal relation between gender traditionalism and wellbeing in young adults. 2) to compare this process across different cultural profiles from a multicultural, non-WEIRD (Heinrich et al., 2010) sample. Specifically, we assess whether students in different cultural profiles experience change in gender traditionalism over the course of their first year at a multicultural Middle Eastern university and whether that traditionalism change (if any) predicts wellbeing.\u003c/p\u003e\n\u003ch2\u003eGender Traditionalism and Wellbeing\u003c/h2\u003e\n\u003cp\u003eThe inconsistencies in the literature concerning the relation between holding gender traditional beliefs and wellbeing, described above, suggest that the relation between gender attitudes and wellbeing is moderated by key demographic factors such as home country /region (i.e., the global region of one\u0026rsquo;s home country), religiosity, conservatism, sexual orientation, and education. Each of these factors may play a unique role in the relation between gender traditionalism and well-being.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eHome Country / Region\u003c/h3\u003e\n\u003cp\u003eWhile higher levels of gender traditionalism are often related to lower levels of wellbeing for both men and women across an array of ethnic-racial backgrounds and home countries, the opposite is sometimes true, particularly for men from countries in the Global South (Gui, 2019; Rashid et al., 2022).\u0026nbsp;For Global South women, a negative link between gender traditionalism and wellbeing sometimes still appears, though less frequently than for Western women (Soltanpanah et al., 2018).\u0026nbsp;Home country thus emerges as a key moderator in the relation between gender traditionalism and wellbeing. Specifically, people from Western countries may likely report a \u003cem\u003enegative\u003c/em\u003e relation between gender traditionality and wellbeing whereas people from Global South countries may report a \u003cem\u003epositive\u003c/em\u003e relation, though this pattern is likely further moderated by participant gender and other demographic factors enumerated below.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eGender\u003c/h3\u003e\n\u003cp\u003eAlthough traditional gender ideologies often restrict autonomy in ways that negatively impact the wellbeing of everyone, regardless of gender (Andersson \u0026amp; McSwain, 2025; Lima et al., 2024), their implications may differ for men and women. Cross-cultural work suggests that men often benefit from endorsing traditional roles, as these roles legitimize status and power advantages, whereas women are more likely to experience psychological costs given that traditionalism prescribes subordination and limited opportunities for them (Jaehn et al., 2020; King et al., 2022; Read \u0026amp; Grundy, 2011). At the same time, findings remain mixed across global contexts, with some studies in the Global South showing that both men and women may report higher wellbeing when aligning with prevailing traditional norms (Gui, 2019; Rashid et al., 2022; Soltanpanah et al., 2018). These patterns suggest that gender moderates the traditionalism\u0026ndash;wellbeing link in culturally contingent ways: in contexts where egalitarian norms are stronger, women in particular may experience greater benefits to wellbeing from rejecting traditionalism, while in contexts where traditional roles are normative, both men and women may derive a sense of security or belonging from endorsing them (Napier et al., 2010; Tesch-R\u0026ouml;mer et al., 2008).\u003c/p\u003e\n\u003ch3\u003eReligion and Conservatism\u003c/h3\u003e\n\u003cp\u003eGender beliefs are strongly affected by religion and conservatism (Etengoff \u0026amp; Lefevor, 2021) which, in turn, may connect to how gender traditionalism relates to wellbeing. Religious involvement is linked to heightened wellbeing via community building and providing purpose (Myers, 2000). Similarly, conservative ideology is associated with better self-reported health, possibly due to stronger beliefs in a just world (Jost, 2019; Napier et al., 2020). Regarding gender roles, religion often frames traditional gender roles as divinely ordained (Homan \u0026amp; Burdette, 2021), and conservative people, perhaps in part because of religious gender ideology, are far more supportive of traditional gender roles than are liberal people (see Jost, 2017 for a meta-analysis). As such, conservatism and religiosity emerge as interconnected factors that may alter whether gender traditionalism is associated with wellbeing or its lack.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSexual Orientation\u003c/h3\u003e\n\u003cp\u003eAnother dimension that complicates the gender traditionalism\u0026ndash;wellbeing relation is sexual orientation. Societal discomfort with sexual minorities is rooted in perceptions that sexual minorities violate traditional gender norms (Henry \u0026amp; Steiger, 2022). Consequently, sexual minority individuals who internalize gender traditionalism may face unique intrapersonal conflicts, potentially undermining their wellbeing (Suppes et al., 2019). We are unaware of any previous work that directly tests the role of sexual orientation as a moderator of the relation between gender traditionalism and wellbeing, but several studies show that internalizing traditionally gendered ideology is particularly problematic for those who do not feel that they adequately live up to those gendered expectations (Sweeting et al. 2014, UK; Soltanpanah et al. 2018; van de Vijver 2007). In line with research on those who are more religious and conservative, we expect that those who are more heterosexual will likely experience a more positive relation between gender traditionalism and wellbeing than those who are more attracted to the same gender.\u003c/p\u003e\n\u003ch3\u003eEducation in Young Adulthood\u003c/h3\u003e\n\u003cp\u003eIn young adulthood, education plays a crucial role in the formation of gender traditionalism and its subsequent relation effect on wellbeing. Young adulthood is a key period for ideological change (Arnett, 2000; McLean et al., 2017; Wilhelm et al., 2023) and, for those who attend, university education is likely a major cause of change. In higher education settings young adults are more likely to be exposed to more liberal ideology than they had been previously (Hanson et al., 2015; Harris \u0026amp; Elison, 1932); this trend holds across Western and Global South countries (Van Hiel et al., 2018). Perhaps as a result, cross-sectional data show that most young adults who attend college report lower levels of gender traditionalism after their time at school (Bryant, 2003). However, this may be due to selection biases: One study showed how US adolescents with more egalitarian views were more likely to report goals of continuing to college/university (Davis and Pearce (2007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLongitudinal evidence concerning conservative/liberal change goes back to the classic Bennington study that showed that women\u0026rsquo;s conservative beliefs became more progressive over their education (Newcomb, 1943). Other studies have confirmed that, at least within a US context, time at university relates to more progressive belief systems (Fan \u0026amp; Marinim, 2000; Sidanius et al., 2008). However, these changes may depend on demographic factors: for example, business students in one study with higher levels of education showed more positive attitudes toward conservatives and negative attitudes toward socialists, compared to social science students who showed the reverse (Guimond et al., 1989). However, this work needs to be replicated and updated to reflect global diversity. Indeed, few studies have focused on culturally diverse student populations in non-Western contexts, where the tension between traditional norms and liberal educational values may be particularly salient. Additionally, while these studies looked at the effect of education on conservatism, we are not aware of any work that focuses specifically on the effect of education on gender traditionalism.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCurrent Study\u003c/h2\u003e\n\u003cp\u003eThe present study moves beyond a correlational examination of gender traditionalism and wellbeing to determine whether change in gender traditionalism predicts higher levels of wellbeing among young adults at an international liberal arts university in the Middle East. Specifically, we examine how gender traditionalism evolves over the first year of university for different types of people and whether shifts in gender traditionalism predicts wellbeing. Our sample is uniquely diverse, including four different first-year cohorts attending a liberal arts college in the Middle East with strong international recruitment. For example, the 2024-2025 academic year had around 2,000 undergraduates from over 120 countries (25% from the local country), strong representation from the four major global religions, and with six different ethnic-racial groups each separately accounting for at least 10% of the student body.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe diversity of this sample, and its base in a Global South country, provide an ideal test for how the relationship between gender traditionalism and wellbeing may change according to a confluence of demographic factors. The sample represents diversity not just in nationality, but in language, cultural background, and religious beliefs, among other demographics. These demographic factors are combined in unique ways for each individual, and inline with intersectional theorists, we maintain that individuals can be understood not only through their constituent demographic group memberships and identities, but as a combination of multiple identity facets that combine and interact in innumerable ways (Bowleg, 2008). We capitalized on this intersectionality by using latent class analysis (LCA; Hagenaars \u0026amp; McCutcheon, 2002), a data-driven, person-centered analytic technique designed to account for the interdependence of demographic factors.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e: Young adults will exhibit a decline in gender traditionalism over their first year at university, consistent with prior research on the liberalizing effects of higher education across multiple cultural contexts (Van Hiel et al., 2018.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e: The relation between gender traditionalism and wellbeing will differ according to demographic factors such that those with a constellation of more conservative demographic factors (e.g., straight, religious, male) will show that higher levels of gender traditionalism predict better wellbeing (Hypothesis 2A: Gui, 2019, China; Henry \u0026amp; Steiger, 2022; Napier et al., 2010; Soltanpanah et al., 2018) whereas those with a constellation of less conservative demographic factors (e.g., not-straight, non-religious, female) would show that lower levels of gender traditionalism predict better wellbeing (Hypothesis 2B: Zhang et al., 2014, China).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e: Increases in gender traditionalism (if any) will predict better wellbeing for young adults with more conservative demographic factors (Hypothesis 3A) whereas decreases (if any) will relate to better wellbeing for groups from less conservative factors (Hypothesis 3B).\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Methods","content":"\u003ch2\u003eProcedure and Participants\u003c/h2\u003e\n\u003cp\u003eThe data used in this project was from an ongoing, longitudinal analysis of a cohort sequential sample of internationally diverse students attending a global liberal arts college in the Middle East. This project was funded by The Emirates Foundation through the LSE Middle East Centre Academic Collaboration with Arab Universities Programme as well as funding from New York University Abu Dhabi. We used the f\u0026shy;\u0026shy;irst two-waves of data from three different cohorts beginning in September 2021 with the incoming first-year cohort added each year (Cohort 1 \u003cem\u003en\u003c/em\u003e = 320, Cohort 2 \u003cem\u003en\u003c/em\u003e = 401, Cohort 3 \u003cem\u003en\u003c/em\u003e = 238). T1 indicates data collected their first month of their first year, and T2 data collected the first month of their second year. We excluded from this initial pool any participants who did not have data on the constructs of interest from both time points (\u003cem\u003en\u003c/em\u003e = 307). After these exclusions, the combined sample \u003cem\u003en\u003c/em\u003e = 652. We report all manipulations, measures, and exclusions in these studies. This sample size was not predetermined to answer these specific research questions. The initial round of analyses showed similar patterns as our final analyses (see Table B in supplemental materials), but two of the profiles were prohibitively small (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 20 and \u003cem\u003en\u003c/em\u003e = 30 respectively), so we waited till the next cohort data was ready. The results of a post-hoc power analysis are shared at the conclusion of the Results section.\u003c/p\u003e\n\u003cp\u003eAt T1, participants ranged in age from 17-23 (\u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 18.60, \u003cem\u003eSD\u0026nbsp;\u003c/em\u003e= .99), 55% were women, 45% were men, and they were ethnically diverse and from predominantly non-Western backgrounds: 25% self-identified as South Asian, 18% identified as Arab, 14% identified as White, 11% identified as East Asian, 9% identified as Black, 9% identified as Multiracial 5% identified as Latinx, 4% identified as Central Asian, 4% identified as Southeast Asian, 1% identified as Other, and \u0026lt; 1% identified as Pacific Islander. Most of the participants (49%) reported being from the same family SES status as their peers, the remaining majority (44%) reported their family being at least somewhat above their peers, 4% reported being at least somewhat lower than their peers, and \u0026gt;1% reported being much higher than their peers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sample represented diversity in religious denominations with 40% Muslim, 27% Christian, 20% not affiliated, 10% Hindu, 3% Buddhist. Participants were more religious than not with 47% reporting as highly religious, 32% as moderately religious, and 20% as low/not religious. Participants represented 21 of the 26 majors represented on campus, with the top five most common being Economics (14%), Social Research and Public Policy (12%), Psychology (12%), Mechanical Engineering (8%), and Political Science (8%). Regarding sexual attractions (measured at T2), 58% (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 381) of participants identified as exclusively attracted to the other gender (i.e., straight), 30% (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 197) as at least somewhat attracted to their own gender, and 11% (\u003cem\u003en\u003c/em\u003e = 74) did not respond to the question.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eT\u003c/em\u003e-Tests and chi-square tests showed that participants who failed to complete the study after Wave 1 did not significantly differ from included participants in terms of gender, SES, sexual orientation, or wellbeing (\u003cem\u003et\u003c/em\u003es \u0026lt; 1.8, \u003cem\u003ep\u003c/em\u003es \u0026gt; .072). There were, however significant differences indicating that attrited participants were more likely to identify as Arab (\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(11) = 38.92, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and Muslim (\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(6) = 21.71, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) as well as report higher levels of religiosity (\u003cem\u003et\u003c/em\u003e(651) = 3.16, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and gender traditionalism (\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e(6) = 21.71, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eIn accordance with the Declaration of Helsinki, the research protocol was reviewed and approved by the New York University Abu Dhabi Institutional Review Board (HRPP-2021-51). Clinical trial number: not applicable. Before gathering data, participants were briefed about the study and provided their informed consent. For those under 18, informed consent of parents or guardians was required before study commencement. At T1, participants were entered into a prize draw for a chance to win an Apple product (e.g., Airpod Pros, Apple watch), and at T2 participants were paid a small sum in addition to being entered into a similar drawing as that of T1. We used Qualtrics to gather data through an online self-reported survey.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTransparency and Openness\u003c/h3\u003e\n\u003cp\u003eWe report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study, and we follow journal article reporting standards for quantitative research in psychology (Appelbaum et al., 2018). All analysis code and research materials are available at [https://osf.io/tcfwk/?view_only=65f2f67dc5b6475186ec28b28fe4b6a3]. This study\u0026rsquo;s design and its analysis were not pre-registered.\u003c/p\u003e\n\u003ch2\u003eMeasures\u003c/h2\u003e\n\u003ch3\u003eConservatism\u003c/h3\u003e\n\u003cp\u003eTo assess conservatism, we measured left-right orientation where participants responded to a single item asking, \u0026ldquo;In political matters, people talk of \u0026lsquo;the left\u0026rsquo; and \u0026lsquo;the right.\u0026rsquo; How would you place your views on this scale, generally speaking?\u0026rdquo; Responses were a on 10-point scale from 1 (\u003cem\u003eleft\u003c/em\u003e) to 10 (\u003cem\u003eright\u003c/em\u003e). This item has been validated on Western samples (Evans et al., 1996) and Global South samples (Shinn \u0026amp; Jhee, 2005).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eEthnic-Racial Identity\u003c/h3\u003e\n\u003cp\u003eParticipants identified ethnic-racial groups to which they belonged from a list; multiple choices were allowed. If an individual affiliated with multiple groups (or if they wrote in a multiracial identity), they were labeled as multiracial. For use in the Latent Class Analysis, an ethnic-racial dummy variable was created with one level being Arab/South Asian and the other level composed of all other ethnicities.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eGender Identity\u003c/h3\u003e\n\u003cp\u003eParticipants reported whether they currently describe their gender to others as male, female, not on the gender binary, or \u0026ldquo;prefer to self-describe.\u0026rdquo; Due to a lack of power to make adequate comparisons, individuals who reported a non-binary gender (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 16) along with those with missing data (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 4) were excluded from the analysis.\u003c/p\u003e\n\u003ch3\u003eGender Traditionalism\u003c/h3\u003e\n\u003cp\u003eIn the last decade, it has become increasingly clear that gender traditional ideologies are not monolithic.\u0026nbsp;Three items were used to capture a male-dominance aspect of gender traditionalism (taken from the WVS;\u0026nbsp;Haerpfer et al., 2020): \u0026ldquo;Men should make the really important decisions in the family,\u0026rdquo; \u0026ldquo;On the whole, men make better political leaders than women do,\u0026rdquo; and \u0026ldquo;When jobs are scarce, men should have more rights to do a job than women.\u0026rdquo; Responses were on a 7-point scale from 1 (\u003cem\u003eStrongly Disagree\u003c/em\u003e) to 7 (\u003cem\u003eStrongly Agree\u003c/em\u003e). Standardized Cronbach\u0026rsquo;s alphas for the scales indicated acceptable fit: W1 \u0026alpha; = .87, W2 \u0026alpha; = .85.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eHome Country / Region\u003c/h3\u003e\n\u003cp\u003eParticipants responded to the following question: \u0026ldquo;Think of the country you consider to be your home country. This might be the country where you spent most of your childhood, or it might be the country that you would represent, for example, at the United Nations or in the Olympics. If you consider more than one country to be your home, pick the one you feel closest to. Where is this country?\u0026rdquo; Participants chose from a list of 22 different regions (e.g., North Africa, East Asia) that were further condensed according to the United Nations Regional groups of Member States with the following five regions: African States, Asia-Pacific States, Eastern European States, Latin American and Caribbean States, Western European and other States (United Nations, 2025).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eReligion\u003c/h3\u003e\n\u003cp\u003eParticipants were presented with a list of religious denominations options (\u003cem\u003eDo not belong to a denomination,\u003c/em\u003e \u003cem\u003eChristian, Jewish, Muslim, Hindu, Buddhist\u003c/em\u003e) and asked whether they belonged to one. A free response form was included for participants whose denomination was not listed. For use in the Latent Class Analysis, a religion dummy variable was created with one level being the majority religion identification (Muslim) and the other level being all other religious denominations.\u003c/p\u003e\n\u003ch3\u003eReligiosity\u003c/h3\u003e\n\u003cp\u003eReligiosity was assessed with a single item from the World Values Survey (WVS;\u0026nbsp;Haerpfer et al., 2020) asking participants how important religion is to their lives on a 7-point scale, from 1 (\u003cem\u003eCompletely Unimportant\u003c/em\u003e) to 7 (\u003cem\u003eVery Important\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSexual Orientation\u003c/h3\u003e\n\u003cp\u003eParticipants chose one of the following options to report the gender of the people to whom they felt attracted over the past year: All men, mostly men, mostly men but some women, about equally men and women, mostly women but some men, mostly women, all women. Participants could also choose, \u0026ldquo;I have not had any physical attraction to anyone.\u0026rdquo; These responses were then recoded using participant gender to create a dichotomous sexual minority variable where 0 = \u003cem\u003eno degree of same-gender attraction\u003c/em\u003e and 1 = \u003cem\u003eat least some degree of same-gender attraction\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eWellbeing\u003c/h3\u003e\n\u003cp\u003eSubjective wellbeing was assessed with items representing four different areas of wellbeing. A life satisfaction item, \u0026ldquo;All things considered, how satisfied are you with your life as a whole these days?\u0026rdquo;, was on a scale from 1 (\u003cem\u003ecompletely dissatisfied\u003c/em\u003e) to 10 (\u003cem\u003ecompletely satisfied\u003c/em\u003e) from the WVS (Haerpher et al., 2020). To assess self-esteem, we used the Single Item Self Esteem Scale, \u0026ldquo;I have high self-esteem,\u0026rdquo; on a scale from 1 (\u003cem\u003enot very true of me\u003c/em\u003e) to 7 (\u003cem\u003every true of me\u003c/em\u003e) (Robins, Hendin, \u0026amp; Trzesniewski, 2001). A loneliness item, \u0026ldquo;How often do you feel lonely,\u0026rdquo; on a scale from 1 (\u003cem\u003enever\u003c/em\u003e) to 5 (\u003cem\u003ealways\u003c/em\u003e) was adapted from the loneliness item developed by Mund and colleagues (2022). Finally, we used a mental health item, \u0026ldquo;How would you describe your mental health over the past year?\u0026rdquo; on a scale from 1 (\u003cem\u003every bad\u003c/em\u003e) to 7 (\u003cem\u003every good\u003c/em\u003e) (Robins et al., 1981). We combined these items into a scale with acceptable reliability, standardized Cronbach\u0026rsquo;s alpha = .72.\u003c/p\u003e\n\u003ch2\u003eAnalytical Approach\u003c/h2\u003e\n\u003ch3\u003eLatent Class Analysis\u003c/h3\u003e\n\u003cp\u003eLatent Class Analysis is a person-centered modeling technique that attempts to identify latent subpopulations within a population by classifying people into profiles based on differences in personal and/or environmental attributes (Spurk et al., 2020). This approach is ideal for our project where we expect that various demographic factors will interact to create specific profiles of relations between gender traditionalism and wellbeing. Following the literature review, the demographic factors we included in the LCA include conservatism, ethnic-racial identity (Arab/South Asian, else), gender identity, religious affiliation (Muslim, else), religiosity, and sexual orientation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo determine different profiles, iterative models are compared to identify the optimal solution using r package poLCA (Linzer \u0026amp; Lewis, 2011). We used the following indices to determine fit: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and the adjusted Vuong-Lo-Mendell-Rubin Likelihood Ratio Test (VLMR). Better fit is indicated by lower values for the AIC and BIC, and p-values below .05 for the VLMR (Nylund et al., 2007). We also examined posterior probabilities, which range from 0 to 1, with values closer to 1 indicating greater confidence that individuals were correctly assigned to their respective profiles (Lanza et al., 2007). Theoretical rationale, interpretability, and parsimony were also considered in determining the most meaningful and valid profile solution (Asparouhov \u0026amp; Muth\u0026eacute;n, 2014; Nylund et al., 2007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter estimating the optimal number of profiles based on the factors listed above, a three-step method was employed to examine whether region of home country was associated with profile membership (R3Step; Asparouhov \u0026amp; Muth\u0026eacute;n, 2014; Vermunt, 2010). The R3Step procedure involves three stages: first, estimating the latent profiles; second, generating a nominal \u0026ldquo;most likely\u0026rdquo; class variable for each participant based on the posterior distribution from the latent profile analysis (LPA), which accounts for measurement error; and third, using multinomial logistic regression to predict profile membership using independent variables\u0026mdash;in this case, the different home country regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing the initial LCA and R3Step analysis, the Bolck, Croon, and Hagenaar (BCH) approach was used to assess whether there were significant mean differences in gender traditionalism across the identified profiles (Bakk \u0026amp; Vermunt, 2016; Bolck et al., 2004). This method applies a form of weighted multiple group analysis, where the weights reflect the measurement error associated with the latent profile variable. Specifically, the measurement error for the \u0026ldquo;most likely\u0026rdquo; profile variable is estimated, and the LPA is subsequently re-estimated using this variable, with the measurement error fixed to the previously estimated values. For this model, mean differences in gender traditionalism at T1, were then assessed across profiles.\u003c/p\u003e\n\u003ch3\u003eMeasurement Invariance\u003c/h3\u003e\n\u003cp\u003eTo determine that any significant differences in the focal analyses would be associated with the constructs themselves rather than artefacts of the measurement tool, we conducted tests of measurement invariance (configural, metric, scalar; Widaman \u0026amp; Reise, 1997) using the R package Lavaan (Rosseel, 2012) across all categorical constructs used in the subsequent latent profile analysis: cohort, time, sexual orientation, gender, religion (Muslim or not, Muslim being the dominant religion of the sample), and ethnic-racial background (Arab/South Asian or not). If any item failed at the metric or scalar levels, we tested for partial invariance (Byrne et al., 1989). Change negative loglikelihood scores with Satorra-Bentler Scaled Chi-Square adjustments were used as fit indices (significant scores indicate a worse fit for more constrained models). Classification uncertainty was accounted for using the average posterior probabilities by most likely class membership where higher probabilities (around .07; Masyn, 2013) indicate successful categorization (Van Lissa et al., 2023).\u003c/p\u003e\n\u003ch3\u003eLatent Change Score Models\u003c/h3\u003e\n\u003cp\u003eTo test the hypotheses, we conducted a multigroup latent change score model (LCSM) that tracked change in gender traditionalism from T1 to T2 and then regressed the resultant change variable onto a wellbeing at T2 (see Figure 1 for a path diagram).\u003c/p\u003e\n\u003cp\u003eThe multigroup aspect compared outcomes across the four profiles produced in the LCA. A LCSM was chosen because the random intercepts separate out within-person construct stability over time, thus modeling time-invariant confounding variables that are omitted in traditional cross-lagged panel models (Steyer et al., 2000). Additionally, LCSM are favored over models like the Random-Intercept Cross-Lag Panel Models which do not handle highly individual change patterns over time (Usami et al., 2019), as we might expect from the diversity of our data. The model was estimated using the R package lavaan (Rosseel, 2012). Model fit was determined with a comparative fit index (CFI) score above 0.95, a Tucker Lewis Index (TLI) above 0.95, a root mean square error of approximation (RMSEA) score below 0.06, and a standardized root mean squared residual (SRMR) of .08 or lower (Hu \u0026amp; Bentler, 1999).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eDescriptive analyses were conducted on variables of interest (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for means, standard deviations, and correlations). Histograms and normality tests indicated that all variables met assumptions of normality with measures of skew and kurtosis between the thresholds of \u0026plusmn;\u0026thinsp;2 and \u0026plusmn;\u0026thinsp;7 respectively (Curran et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement Invariance\u003c/h2\u003e \u003cp\u003eMeasurement invariance analyses revealed that most constructs achieved scalar invariance except for religious affiliation and sexual orientation (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for invariance testing results). For the item \u0026ldquo;Men should make the really important decisions in the family,\u0026rdquo; Muslim and exclusively heterosexual participants showed significantly higher intercepts than their counterparts in the sample. We interpret this as a feature rather than a bug of our sample (see Kusano, Napier, \u0026amp; Jost, 2025, multinational), plus the item correlates well with the others in the scale, thus we retained it.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample descriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRange\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSkew\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eKurtosis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.10*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Religiosity T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.34***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.08*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Conservatism T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.43***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.40***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.10*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Gender Trad. T1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.67***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.20***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Gender Trad. T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.20***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Wellbeing T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/p\u003e \u003cp\u003e** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003cp\u003e*** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eGender Trad. = gender traditionalism\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults for tests of measurement invariance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eCohort (Class of 2025 vs Class of 2026 vs Class of 2027)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ2Δ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ2 Δ p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInvariant Items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime (Beginning of 1st Year vs Beginning of 2nd Year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ2Δ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ2 Δ p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInvariant Items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSexual Orientation (Exclusively Heterosexual vs Not)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ2Δ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ2 Δ p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInvariant Items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (Women vs Men)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ2Δ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ2 Δ p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInvariant Items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligious Affiliation (Muslim vs Not)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ2Δ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ2 Δ p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInvariant Items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity (Arab/South Asian vs Not)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel tested\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ2Δ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ2 Δ p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInvariant Items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScalar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote.\u003c/em\u003e We used Widaman and Reise\u0026rsquo;s (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) paradigm of configural invariance (equivalent items used across groups) metric invariance (factor loadings constrained to be equivalent across groups), and scalar invariance (the intercepts constrained to be equal across groups). The \u003cem\u003ep\u003c/em\u003e value of the chi-square change (χ2 Δ \u003cem\u003ep\u003c/em\u003e) determines whether or not a model is significantly worse fitting than the previous, less constrained model. Thus, a χ2 Δ \u003cem\u003ep\u003c/em\u003e value higher than .05 for subsequent models indicates that the more constrained model (e.g., less invariant across construct of interest) does not have significantly worse fitting should continue to the next level of constraints.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eLatent Cultural Profiles\u003c/h2\u003e\n\u003cp\u003eWe conducted a LCA to assess the effect of demographic factors on the relation between gender traditionalism and wellbeing. The following T1 variables were used to create latent classes: gender identity, political conservatism, racial-ethnic background (Arab/South Asian or not), religious affiliation (Muslim or not), religiosity, and sexual orientation. We accepted a four-class model with the best fit of the converging models that had excellent posterior probabilities (ranged from .98 to 1.00).\u0026nbsp;See Table A in the supplemental materials for the full model fit and comparisons.\u003c/p\u003e\n\u003cp\u003eFour profiles emerged, which we interpreted as follows. The first included a \u003cem\u003eProgressive Western Women\u0026nbsp;\u003c/em\u003eprofile (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 170, 30%) which was characterized as 87% women, 100% non-Arab and non-Muslim, 57% not exclusively heterosexual, low conservatism (16%), and low religiosity (26%). For a depiction of probabilities for profile membership, see Figure 2. We labeled the second profile \u003cem\u003eStraight Non-Muslim Men\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e = 158, 28%) were 87% men, 26% Arab, non-Muslim (93%), mostly exclusively heterosexual (84%), moderately conservative (38%) and religious (34%). Profile 3 was labelled \u003cem\u003eProgressive Arab Women\u0026nbsp;\u003c/em\u003e(\u003cem\u003en\u003c/em\u003e = 105, 18%) 76% women, 78% Arab, 61% Muslim, 53% not exclusively heterosexual, very low conservatism (7%), and moderately religious (36%). Finally, \u003cem\u003eConservative, Religious Muslims\u0026nbsp;\u003c/em\u003e(\u003cem\u003en\u003c/em\u003e = 135, 24%) were almost evenly divided by gender (57% women), 72% Arab, 100% Muslim, 89% exclusively heterosexual, more conservative (65%), and highly religious (100%).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eHome country / region as an Indicator of Latent Profiles\u003c/h3\u003e\n\u003cp\u003eNext, R3Step analyses were conducted to examine how home country / region was associated with latent profile membership (see Table 3 for full R3Step outcomes). The results indicated that participants from Western Europe were significantly more likely to belong to the \u003cem\u003eProgressive Western Women\u003c/em\u003e (6.87 times, \u003cem\u003ep\u003c/em\u003e = .006) and \u003cem\u003eStraight Non-Muslim Men\u003c/em\u003e classes (4.92 times, \u003cem\u003ep\u003c/em\u003e = .023) compared to the \u003cem\u003eConservative Religious Muslims\u003c/em\u003e profile. Participants from Africa were more likely to belong to \u003cem\u003eStraight Non-Muslim Men\u0026nbsp;\u003c/em\u003ecompared to the \u003cem\u003eConservative Religious Muslims\u003c/em\u003e (1.19 times, \u003cem\u003ep\u003c/em\u003e = .012) and less likely to belong to the \u003cem\u003eProgressive Arab Women\u003c/em\u003e class compared to \u003cem\u003eConservative Religious Muslims\u003c/em\u003e (42% odds, \u003cem\u003ep\u003c/em\u003e = .030).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 3\u003c/em\u003e. Odds ratios and p-values for the R3Step method showing likelihood of being sorted into latent profiles based on world region of home country.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"677\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eClass Reference:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eConservative Religious Muslims\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eUN-Region Reference:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAsia-Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratios\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eProgressive Western Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eProgressive Western Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eEastern European\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eProgressive Western Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eLatin Am \u0026amp; Carib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eProgressive Western Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eWest Europe, Else\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eConserv. Non-Muslim Men\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eConserv. Non-Muslim Men\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eEastern European\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eConserv. Non-Muslim Men\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eLatin Am \u0026amp; Carib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eConserv. Non-Muslim Men\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eWest Europe, Else\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e4.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eProgressive Arab Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eAfrica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eProgressive Arab Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eEastern European\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eProgressive Arab Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eLatin Am \u0026amp; Carib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.4623%;\"\u003e\n \u003cp\u003eProgressive Arab Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8272%;\"\u003e\n \u003cp\u003eWest Europe, Else\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7932%;\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7031%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2142%;\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eAll values are R3Step logistic regression analyses. If the odds ratio is above one, the person is more likely to be assigned to the latent group (in comparison to the reference group); if the odds ratio is below one, the person is less likely to be assigned to the latent group (in comparison to the reference group).\u003c/p\u003e\n\u003ch3\u003eGender Traditionalism Mean Differences across Latent Profiles\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eBCH procedures were used to examine whether mean levels of gender traditionalism differed across the identified profiles. The results revealed that the only significant difference (\u003cem\u003ep\u003c/em\u003e = .045) in gender traditionalism across profiles was at T1 between \u003cem\u003eProgressive Western Women\u0026nbsp;\u003c/em\u003e(\u003cem\u003eM =\u0026nbsp;\u003c/em\u003e2.04) and \u003cem\u003eConservative Religious Muslims\u0026nbsp;\u003c/em\u003e(\u003cem\u003eM =\u0026nbsp;\u003c/em\u003e2.49).\u0026nbsp;Full results of the BCH procedure are presented in Table 4.\u003c/p\u003e\n\u003ch2\u003eGender Traditionalism Change Predicting Wellbeing\u003c/h2\u003e\n\u003cp\u003eTo test our hypotheses,\u0026nbsp;we conducted a LCSM as a multi-group model using the four classes produced from the LCA. The model\u0026nbsp;had overall good fit:\u0026nbsp;c\u003csup\u003e2\u003c/sup\u003e(84) = 1681.19, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001, CFI = .988, TLI = .995, RMSEA .027 (.000, .068), SRMR = .028. For full results, see Table 5. Hypothesis 1, that people would decline in gender traditionalism over their first year at university, was not supported, and was in fact reversed for three of the four groups. \u003cem\u003eProgressive Western Women\u003c/em\u003e (\u003cem\u003eB\u003c/em\u003e = .79, SE = .31, \u003cem\u003ep\u003c/em\u003e = .011), \u003cem\u003eStraight Non-Muslim Men\u0026nbsp;\u003c/em\u003e(\u003cem\u003eB\u003c/em\u003e = 1.25, SE = .30, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and \u003cem\u003eConservative Religious Muslims\u0026nbsp;\u003c/em\u003e(\u003cem\u003eB\u003c/em\u003e = .71, SE = .28, \u003cem\u003ep\u003c/em\u003e \u0026lt; .010) showed significant increase in gender traditionalism from T1 to T2. Only \u003cem\u003eProgressive Arab Women\u0026nbsp;\u003c/em\u003eshowed no significant change (\u003cem\u003eB\u003c/em\u003e = .45, SE = .28, \u003cem\u003ep\u003c/em\u003e = .065).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHypothesis 2A, that higher levels of gender traditionalism would be associated with better wellbeing for more conservative, religious and heterosexual individuals was supported. Hypothesis 2B, that more progressive individuals would show a deleterious effect, was not supported. Higher levels of gender traditionalism predicted better wellbeing for \u003cem\u003eConservative Religious Muslims\u0026nbsp;\u003c/em\u003e(\u003cem\u003eB\u003c/em\u003e = .21, SE = .07, \u003cem\u003ep\u003c/em\u003e = .004) but not for any other group: \u003cem\u003eStraight Non-Muslim Men\u003c/em\u003e (\u003cem\u003eB\u003c/em\u003e = .14, SE = .11, \u003cem\u003ep\u003c/em\u003e = .207), \u003cem\u003eProgressive Arab Women\u0026nbsp;\u003c/em\u003e(\u003cem\u003eB\u003c/em\u003e = .25, SE = .16, \u003cem\u003ep\u003c/em\u003e = .120) and \u003cem\u003eProgressive Western Women\u0026nbsp;\u003c/em\u003e(\u003cem\u003eB\u003c/em\u003e = .18, SE = .16, \u003cem\u003ep\u003c/em\u003e = .247). The commonality for the palliative aspect of endorsing traditional gender beliefs seemed to be about religiosity or an Islamic religious identification, more so than conservatism.\u003c/p\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeans and equality tests on gender traditionality at T1 and T2 across classes using the BCH procedure.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Gender Trad\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003cp\u003eTrad\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Progressive Western Women\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[1.85, 2.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.98, 2.38]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.Straight Non-Muslim Men\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[2.01, 2.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[2.10, 2.52]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Progressive Arab Women\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[1.92, 2.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[1.96, 2.43]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Conservative Religious Muslims\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[2.23, 2.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[2.42, 2.84]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEquality tests of Means\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChi square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eChi square\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 vs 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 vs 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 vs 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 vs 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 vs 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 vs 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote.\u003c/em\u003e Gender traditionality range\u0026thinsp;=\u0026thinsp;1\u0026ndash;7.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected output for the Latent Change Score Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eProgressive Western Women\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eStraight Non-Muslim Men\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;158)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eProgressive Arab Women\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003eConservative Religious Muslims\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;135)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1: Mean gender trad change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2: Mean gender trad on wellbeing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3: Gender trad change on wellbeing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean gender trad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender trad change with gender trad mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender trad mean variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender trad change variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c14\" namest=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eNote.\u003c/em\u003e Significant outcomes are highlighted. Gender trad\u0026thinsp;=\u0026thinsp;gender traditionalism. Mean gender trad\u0026thinsp;=\u0026thinsp;mean gender traditionalism for each group. Gender trad change with gender trad mean\u0026thinsp;=\u0026thinsp;the correlation between the means of gender traditionalism for each group and the rate of change of gender traditionalism for that group. Gender trad mean variance\u0026thinsp;=\u0026thinsp;between-person variance (differences not explained by measurement error) on mean gender traditionalism. Gender trad change variance\u0026thinsp;=\u0026thinsp;between-person variance (differences not explained by measurement error) on mean change of gender traditionalism. A post-hoc ANOVA indicated that groups more likely to contain men had significantly higher levels of gender traditionalism.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003e3A, that changes in gender traditionalism would predict improvements in wellbeing was again supported while Hypothesis 3B (deleterious effect for less conservative) was not. For \u003cem\u003eConservative Religious Muslims\u003c/em\u003e, an increase in gender traditionalism predicted improved wellbeing (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.36, SE\u0026thinsp;=\u0026thinsp;.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.009); for the remaining groups there was no significant relation: \u003cem\u003eProgressive Western Women\u003c/em\u003e (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.13, SE\u0026thinsp;=\u0026thinsp;.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.359), \u003cem\u003eProgressive Arab Women\u003c/em\u003e(\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.06, SE\u0026thinsp;=\u0026thinsp;.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.784), and \u003cem\u003eStraight Non-Muslim Men\u003c/em\u003e (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.10, SE\u0026thinsp;=\u0026thinsp;.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.415).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eOf interest, the correlations between gender traditionalism and gender traditionalism change were significant for each group except \u003cem\u003eProgressive Arab Women\u003c/em\u003e such that those with higher levels of gender traditionalism had lower rates of increase over time (\u003cem\u003er\u003c/em\u003e\u0026rsquo;s\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;.22, \u003cem\u003ep\u003c/em\u003e\u0026rsquo;s\u0026thinsp;\u0026lt;\u0026thinsp;.001). Finally, all four groups still showed significant between-person variance (differences not explained by measurement error) on means of gender traditionalism and gender traditionalism change over time (\u003cem\u003eB\u003c/em\u003e\u0026rsquo;s\u0026thinsp;\u0026gt;\u0026thinsp;.44, \u003cem\u003ep\u003c/em\u003e\u0026rsquo;s\u0026thinsp;\u0026lt;\u0026thinsp;.007).]\u003c/p\u003e \u003cp\u003ePost hoc power analyses were conducted using RAMPath, a Monte Carlo-based method in R (Zhang \u0026amp; Liu, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), to determine whether the participant number in each group was large enough to detect small to medium effect sizes based on estimated levels of effects and variance. The results indicated that each of the four groups achieved power of at least .80.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGender Traditionalism Predicting Wellbeing at the Individual-Level\u003c/h2\u003e \u003cp\u003eA sensitivity regression analysis was conducted to assess whether gender traditionalism affects well-being at the individual level and whether this is moderated by any of the demographic factors used in the LCA. Specifically, we regressed gender traditionalism T1 on wellbeing T2 and tested to see whether gender (woman, man), world region (Asia-Pacific, Africa, Eastern Europe, Latin America \u0026amp; Caribbean, Western Europe and other Developed Nations), religion (Muslim, not religious, Christian, Buddhist, Hindu, Jewish), conservatism, sexual orientation, and religiosity moderated this relation. Results indicated that the overall model did not explain much variance: \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.11, \u003cem\u003eAdj R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.03, \u003cem\u003eF\u003c/em\u003e(27, 322)\u0026thinsp;=\u0026thinsp;1.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.082, that no interactions were significant, and that only three predictors significantly related to wellbeing: being from Eastern Europe (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.034) and Western Europe/other developed nations (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.035) relative to being from the Asia-Pacific region and being attracted to own-gender (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.020; see Table C in the supplemental materials for the full output). These largely null results can be interpreted to support our decision to identify latent subgroups within our sample and assess their mean levels and patterns of change accordingly.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study sought to accomplish two aims. First, we sought to confirm the relation between gender traditionalism and wellbeing by testing whether any change in gender traditionalism over the course of young adult\u0026rsquo;s freshman year at university would predict wellbeing. Second, we studied these trends with a largely understudied sample of students from across the globe studying at a progressive university in the Middle East.\u003c/p\u003e \u003cp\u003eContrary to Hypothesis 1, that young adults would decline in gender traditionalism during the first year at university, the majority of participants showed significant \u003cem\u003eincreases\u003c/em\u003e in gender traditionalism. The palliative effect of gender traditionalism on the wellbeing of religious, conservative individuals from Hypothesis 2, was supported. Young adults in the \u003cem\u003eConservative Religious Muslim\u003c/em\u003e groups (both men and women) showed better wellbeing with more gender traditionalism. However, there was no support for the deleterious relation between gender traditionalism and wellbeing from those with less conservative backgrounds. Similarly, for Hypothesis 3, that change in gender traditionalism would relate to wellbeing above and beyond mean levels of gender traditionalism the palliative effect was supported: young adults in the \u003cem\u003eConservative, Religious Muslim\u003c/em\u003e group, an increase in gender traditionalism over time predicted higher levels of wellbeing. There was no support for any deleterious effect.\u003c/p\u003e \u003cp\u003eThe main aim of this paper was to provide a more rigorous test of the association between gender traditionalism and wellbeing by exploring directionality within a longitudinal analysis on a diverse, non-Western sample. This is the first example, to our knowledge, that provides evidence that heightened wellbeing is attributed to a \u003cem\u003echange\u003c/em\u003e in gender traditionalism. Importantly, this effect only came through for young adults with particularly high cultural levels of religiosity, conservatism, and non-Western backgrounds.\u003c/p\u003e \u003cp\u003eOne way that this university sample appears to differ from more homogenous, Western samples is that, far from reporting liberalization, many of these students reported increasing their gender traditionalism during their time at university. One explanation for this outcome is that our sample is uniquely diverse. It is composed of students from all over the globe attending university in a Middle Eastern country, which sets our sample apart from typical research conducted on more homogenous samples in WEIRD contexts (Heinrich et al., 2010).\u003c/p\u003e \u003cp\u003eWhile the country of this university is growing more tolerant, it is still within the Middle East North Africa region, and many students were also from this area, which scores lowest of all regions on gender parity (World Economic Forum, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our sample participants may have been early socialized in a more gender-restrictive environment, thus the liberalizing effect seen in other universities across the globe (Van Hiel et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) did not materialize in our sample, and in fact was reversed. Additionally, the diversity of students and lecturers at this university provide more heterogenous ideological developmental pathways. For example, when presenting initial findings of this work to a gathering at of study participants and other university personnel, one student commented on her own experience with ideological change (or lack of it) during her time there. She shared how she grew up in a large, liberal American city and during her time at home was surrounded largely by liberal homogeneity. Coming to school in the Middle East was the first time that she had been exposed to different, conservative ways of thinking, and she came to appreciate that ideology and became more conservative as a result.\u003c/p\u003e \u003cp\u003e \u003cem\u003eProgressive Arab Women\u003c/em\u003e emerged as a profile with trends that differed from the other emergent profiles. First, they were the only profile that did not significantly increase in their gender traditionalism over time. Additionally, unlike the other three profiles where the more traditional they were, the less likely they were to increase in traditionalism over time, the degree of traditionalism for \u003cem\u003eProgressive Arab Women\u003c/em\u003e did not predict their rate of change. These results do not seem to arise from intragroup variance: \u003cem\u003eProgressive Arab Women\u003c/em\u003e had the lowest variance among their patterns of change over time. Finally, despite moderate religiosity, \u003cem\u003eProgressive Arab Women\u003c/em\u003e reported the lowest levels of conservatism across all four profiles.\u003c/p\u003e \u003cp\u003eThese findings complement previous work from the global south, namely China (Gui, 2019) and Pakistan (Rashid et al., 2022), that indicate that even in the same cultural context, women have significantly different experiences with gender traditionalism compared to men. Recent empirical work in the MENA region indicates increasing public support among women for gender equality, especially in urban settings and among younger cohorts (Thomas \u0026amp; Kasselstrand, 2019). Legal and policy reforms, such as those under Saudi Arabia\u0026rsquo;s Vision 2030 expanding women\u0026rsquo;s labor market access and changes to personal status laws, are increasingly supportive of women\u0026rsquo;s agency (Polok, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Qualitative studies among Arab Bedouin women show that reflexive narratives about birth rates, marriage norms, and family expectations are leading to significant shifts in behavior, as women negotiate, resist, or reform traditional conventions that they perceive as constraining (Zoabi \u0026amp; Fuller, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These developments suggest that those fitting the \u003cem\u003eProgressive Arab Women\u003c/em\u003e profile show not simply superficial change, but deeper processes of identity, legal, and social transformation. This is an avenue of research rich with implications for both cultural change and attitude related wellbeing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLimitations Table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLimitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImplication\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJustification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample Attrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArab, Muslim, more religious, and more gender-traditional participants were less likely to participate in Wave 2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMay bias results and underrepresent more conservative perspectives; limits reproducibility.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThese groups were still well-represented and produced the most significant outcomes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatent Class Heterogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin-group variance in latent classes (e.g., men in 'Progressive, Religious Women' group).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduces clarity of group-level interpretations; complicates generalizability.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup compositions were enumerated in text and figure to identify heterogeneity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasurement Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmbiguity in sexual attraction item in a conservative context; wellbeing composite is untested despite good reliability.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotential misreporting and construct validity concerns; affects reproducibility and internal validity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReliability for the wellbeing measure was good (α\u0026thinsp;=\u0026thinsp;.72) for such a heterogeneous sample.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistical Power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne subgroup (Secular, Non-Muslim Women) did not meet 80% power threshold.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReduced confidence in subgroup-specific findings; limits robustness.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe three other subgroups all had power which exceeded 80%.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultural Determinism Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFindings may be interpreted as culturally deterministic without deeper exploration of intra-group variability or agency.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimits theoretical nuance and may oversimplify cultural dynamics.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWe utilized a person-specific approach, Latent Class Analysis, to gain as much intrasample complexity and nuance as possible.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOversimplified categorical variables in LCA (e.g., sexual Identity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSexual orientation, ethnic-racial identity, and gender treated as binary (same-gender attraction vs. not).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFails to capture complexity of identities; limits inclusivity and theoretical depth.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNuance was lost by our choice to dichotomize, but it enabled more variables to be included in the LCA. Additionally, regarding sexuality, differentiating between those willing to report even a degree of same-gender attraction, and those who are not is likely a meaningful distinction in this cultural context.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual Generalizability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConducted in a unique, international liberal arts university in the Middle East.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLimits generalizability to other educational or national contexts.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe unique context provides valuable insights but limits applicability to traditional WEIRD samples.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eLimitations and Future Directions\u003c/h3\u003e\n\u003cp\u003eSee Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e for a simplified presentation of limitations, implications, and justifications. One limitation of the study was manifest in sample attrition. Participants who were Arab, Muslim, more religious, and more gender traditional were less likely to participate in the second wave of the study. Our expectation is that this attrition is the result of the perceived liberal bent of the survey where sensitive issues like attraction, gender ideology, and democratic freedom were assessed. These student demographics were still strongly represented in our study (over 50%) and drove many of the findings, however, the attrited participants might have shown even starker patterns.\u003c/p\u003e \u003cp\u003eOur sample was incredibly diverse, which provides a rich addition to the literature body, but can also be difficult to handle statistically. To include a greater number of demographic factors in the LCA, we chose to condense the categorical variables into dummy codes with two levels. While this reduced the variability in these individual variables, having a broader array of factors is more valuable for this project.\u003c/p\u003e \u003cp\u003eAlthough each of the four items in our wellbeing measure are previously validated and reliable single-item measures, our composite approach is previously untested. The reliability was good for such a heterogenous sample (\u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.72), but this measure may have introduced some error.\u003c/p\u003e \u003cp\u003eThe relation between gender traditionalism and wellbeing is complex and nuanced. The longitudinal data and our structural equation model improved on previous cross-sectional work by clearly identifying how gender traditionalism drives wellbeing, thus eliminating the concern of potential unmeasured confounds in the immediate relation. Still, there was a great deal of heterogeneity in the classes produced by our LCA, and the literature identifies other variables that might moderate the gender traditionalism\u0026mdash;wellbeing link. These constructs include system justification ideology (Jost, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Napier et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), gender role satisfaction (i.e., the degree to which someone feels personally fulfilled with their gender roles) or the potential discrepancy between gender role attitudes and gender role expression (e.g., those with less conservative ideology who are nonetheless compelled toward more traditional gender role expression; Soltanpanah et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sweeting et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Regarding the exploration into how gender traditionalism and wellbeing might affect sexual minorities, we did not assess internalized homophobia and in-group identification, both of which likely play a role (Suppes et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the dynamic relationship between gender traditionalism and wellbeing among university students in a diverse, international setting. Contrary to expectations, most students increased their gender traditionalism over their first year. For students with more markers of conservatism, this increase over time predicted better wellbeing. As such, our study provides conclusive evidence that the relation between gender traditionalism and wellbeing for some groups of people is truly due to gender traditionalism rather than other potentially confounding factors. Our findings highlight the cultural complexity of gender ideology development in young adulthood and suggest that ideological shifts\u0026mdash;and their psychological consequences\u0026mdash;follow culturally contingent pathways.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.N.: Conceptualization, Data curation, Investigation, Formal analysis, Methodology, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; reviewing and editing. P.H.: Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing \u0026ndash; reviewing and editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll analysis code and research materials are available at [https://osf.io/tcfwk/?view_only=65f2f67dc5b6475186ec28b28fe4b6a3]. 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Reflexivity and the change in women\u0026rsquo;s status: The case of Arab Bedouin women in Israel. \u003cem\u003eCogent Social Sciences\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 2294561. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23311886.2023.2294561\u003c/span\u003e\u003cspan address=\"10.1080/23311886.2023.2294561\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"conservatism, intersectionality, latent change score, religion, sexism","lastPublishedDoi":"10.21203/rs.3.rs-8561546/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8561546/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"There is mounting evidence for a link between beliefs about gender and wellbeing, but the outcomes are sometimes contradictory and seem strongly affected by demographic factors. The body of work is predominantly based on Western samples with scant diversity with which to test these ideas. The body of work is also almost entirely cross-sectional, limiting the ability to make causal claims or understand directionality. We tested whether longitudinal change in gender traditionalism predicts wellbeing in young adults across the first year of university with a diverse sample of 650 students at an international university in the Middle East. We paired latent class analysis with latent change score modeling to analyze how gender traditionalism may change over the course of young adult’s first year at university, how those changes relate to wellbeing, and determine whether this process differed by profiles determined by gender, ethnicity, conservatism, religious affiliation, and sexual orientation. Contrary to expectations and of theoretical interest, results indicated an overall increase in gender traditionalism that significantly predicted better wellbeing for conservative, religious Muslims. Progressive Arab women emerged as a profile with several distinctive patterns including being the only group who did not increase in gender traditionalism. This work is the first to offer strong evidence (change driven) for the relation between gender traditionalism and wellbeing, and it reinforces the importance of identifying demographic moderators of this link.","manuscriptTitle":"Gender Traditionalism and Wellbeing: Change across First Year at a Global University","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 13:36:34","doi":"10.21203/rs.3.rs-8561546/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3637a030-ccf3-44c9-a4fe-680ef716f02e","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"2","date":"2026-05-07T14:21:43+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T13:36:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 13:36:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8561546","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8561546","identity":"rs-8561546","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}