# Cultural contributions to adults' self-rated mental health problems and strengths: 7 culture clusters, 28 societies, 16 906 adults

William Copeland, Masha Ivanova, Thomas Achenbach, Lori Turner, Guangyu Tong, Adelina Ahmeti-Pronaj, Alma Au, Monica Bellina, J. Carlos Caldas, Yi-Chuen Chen, et al.

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## To cite this version:

William Copeland, Masha Ivanova, Thomas Achenbach, Lori Turner, Guangyu Tong, et al. Cultural contributions to adults' self-rated mental health problems and strengths: 7 culture clusters, 28 societies, 16 906 adults. Psychological Medicine, 2023, 53 (16), pp.7581-7590. 10.1017/S0033291723001332. hal-04390087

HAL Id: hal-04390087

https://hal.parisnanterre.fr/hal-04390087v1

Submitted on 3 Jun 2025

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1 Running head: Society, Culture, and Mental Health

2

3 Societal and Cultural Contributions to Adults' Self-Rated Mental Health Problems and

4 Strengths: 7 Culture Clusters, 28 Societies, 16,906 Adults

5 1. William E. Copeland, University of Vermont, 1 South Prospect Street, Burlington, VT
6 05401. Email: william.copeland@med.uvm.edu

7 2. Masha Y. Ivanova: University of Vermont, 1 South Prospect Street, Burlington, VT 05401.
8 Email: Masha.Ivanova@uvm.edu. Phone: 802-656-2796.

9 3. Thomas M. Achenbach: University of Vermont, 1 South Prospect Street, Burlington, VT
10 05401. Email: Thomas.Achenbach@uvm.edu. Phone: 802-656-2629.

11 4. Lori V. Turner: University of Vermont, 1 South Prospect Street, Burlington, VT 05401.
12 Email: Lori.Turner@med.uvm.edu. Phone: 802-656-2599.

13 5. Guangyu Tong: Yale University, 135 College St. New Haven, CT 06510. Email:
guangyu.tong@yale.edu. Phone: 203-432-4771.

15 6. Adelina Ahmeti-Pronaj: University Clinical Center of Kosova, Department of Child and
16 Adolescent Psychiatry, 10,000 Prishtine, Kosova. Phone: +37744600524.

17 7. Alma Au: Hong Kong Polytechnic University, Department of Applied Social Sciences, Hung
18 Hom, Kowloon, Hong Kong, China. Email: Alma.Au@inet.polyu.edu.hk. Phone: 852-2766
19 7944.

20 8. Monica Bellina: Eugenio Medea Scientific Institute, Department of Child Psychiatry, 7
21 Padiglione, Via Don Luigi Monza 20, Bosisio Parini (Lecco), Italy 23842. Email:
22 monica.bellina@bp.lnf.it. Phone: +39 031 877 813.

23 9. J. Carlos Caldas: Instituto Superior de Ciências da Saúde - Norte, Departamento de Ciências
24 Sociais e do Comportamento, Rua Central de Gandra, 1317, 4585-116 Gandra, PRD,
25 Portugal. Email: jcarloscaldas@gmail.com. Phone: +351-933295345.

26 10. Yi-Chuen Chen: National Chung Cheng University, Department of Psychology, 168
27 University Road, Min-Hsiung, Chia-Yi, Taiwan, 62102. Email: psyycc@ccu.edu.tw. Phone:
28 886-5-2720-411 ext 32209.

29 11. Ladislav Csemy: Prague Psychiatric Centre, Laboratory of Social Psychiatry, Ustavni 91, 181
30 03 Praha 8, Prague, Czech Republic. Email: csemy@pcp.lf3.cuni.cz. Phone: 420-266003272.

31 12. Marina M. da Rocha: University Paulista (Unip), Institute of Human Sciences, Rua Francisco
32 Bautista, 300, São Paulo, Brazil, 04182-020. Email: marinamonzani@gmail.com. Phone:
33 11-2332-1300.

34 13. Anca Dobrean: Babes-Bolyai University, Department of Clinical Psychology and
35 Psychotherapy, 400015, Rupublicii st. 37, Cluj Napoca, Romania. Email:
36 AncaDobrean@psychology.ro. Phone: 0040-264-434141.

37 14. Lourdes Ezpeleta: Universitat Autonoma de Barcelona, Departament de Psicologia Clinica i
de la Salut, Edifici B, Bellaterra, Spain 08193. Email: lourdes.ezpeleta@uab.cat. Phone: 34-
39 93 581 2883.

40 15. Yasuko Funabiki: Kyoto University Hospital, Department of Psychiatry, 54 Kawaharacho,
41 Shogoin, Sakyo-ku, Kyoto, Japan 606-8507. Email: funaysk@kuhp.kyoto-u.ac.jp. Phone:
42 +81-75-751-3373.
43 16. Valerie S. Harder: University of Vermont, 1 South Prospect Street, Burlington, VT 05401.
44 Email: [Valerie.Harder@uvm.edu](mailto:Valerie.Harder@uvm.edu). Phone: 802-656-2652.

45 17. Felipe Lecannelier: Faculty of Medical Sciences, Universidad de Santiago, Chile. Email: flecannelier@chileapego.org.

47 18. Marie Leiner de la Cabada: Texas Tech University Health Sciences Center, Department of Pediatrics. P. O. Box 43091 Lubbock, Texas 79409 United States. Email: [Marie.Leiner@ttuhsc.edu](mailto:Marie.Leiner@ttuhsc.edu) Phone: (915) 545-8941.

50 19. Patrick Leung: The Chinese University of Hong Kong, Department of Psychology, Room 356, Sino Building, Shatin, New Territories, Hong Kong, People's Republic of China. Email: [pleung@cuhk.edu.hk](mailto:pleung@cuhk.edu.hk) Phone: 852 3943 6502.

53 20. Jianghong Liu: University of Pennsylvania, School of Nursing and Medicine, 418 Curie Blvd., Room 426, Claire M. Fagin Hall, Philadelphia, Pennsylvania 19104-6096. Email: [jhliu@nursing.upenn.edu](mailto:jhliu@nursing.upenn.edu) Phone: 215-898-8293.

56 21. Safia Mahr: Université Paris Ouest Nanterre la Défense, Departement de Psychologie, Laboratoire EVACLIPSY, Batiment C, 3ᵉ Etage, Salles C.319 & C.321, 200 Avenue de la Republique, Nanterre, France 92001. Email: [safia.mahr@gmail.com](mailto:safia.mahr@gmail.com) Phone: +33(6)-45-14-32-99.

60 22. Sergey Malykh: Psychological Institute of Russian Academy of Education, Mokhovaya str., 9/4, Moscow, Russia 125009. Email: [malykhsb@mail.ru](mailto:malykhsb@mail.ru) Phone: +7-495-695-88-76.

62 23. Jasminka Markovic: Medical Faculty Novi Sad, University of Novi Sad, Clinical Center of Vojvodina, Hajduk Veljkova 1, Novi Sad, Serbia 21000. Email: [markovic09@open.telekom.rs](mailto:markovic09@open.telekom.rs) Phone: 381 65 282 6535.

65 24. David M. Ndetei: Africa Mental Health Foundation, P.O. Box 48423-00100, Nairobi, Kenya. Email: [dmndetei@amhf.or.ke](mailto:dmndetei@amhf.or.ke) Phone: 254-020-271-6315.

67 25. Kyung Ja Oh: Yonsei University, Department of Psychology, 50 Yonsei-ro, Soedaemun-gu, Seoul, South Korea 120-749. Email: [kjoh@yonsei.ac.kr](mailto:kjoh@yonsei.ac.kr)

69 26. Jean-Michel Petot: Université de Paris Ouest, Departement de Psychologie, Laboratoire EVACLIPSY, Batiment C, 3 Etage, Salles C.319 & C.321, 200 Avenue de la Republique, Nanterre, France 92001. Email: [jmpetot@u-paris10.fr](mailto:jmpetot@u-paris10.fr) Phone: 140977070.

72 27. Geylan Riad: Helwan University, Cairo, Egypt. Email: [geylanriad@gmail.com](mailto:geylanriad@gmail.com)

73 28. Direnc Sakarya: Ankara University Faculty of Medicine, Department of Psychiatry, Ankara, Turkey. Email: [direnc@gmail.com](mailto:direnc@gmail.com)

75 29. Virginia C. Samaniego: Pontificia Universidad Católica Argentina, Buenos Aires, Argentina. Email: [corina_samaniego@uca.edu.ar](mailto:corina_samaniego@uca.edu.ar)

77 30. Sandra Sebre: University of Latvia, Department of Psychology, Jurmalas Avenue 74/76, Riga, Latvia LV-1083. Email: [sebre@lanet.lv](mailto:sebre@lanet.lv) Phone: +371 67034017.

79 31. Mimoza Shahini: University Clinical Center of Kosova, Department of Child and Adolescent Psychiatry, 10,000 Prishtine, Kosova. Email: [mimoza.shahini@gmail.com](mailto:mimoza.shahini@gmail.com) Phone: +37744120604.

82 32. Edwiges Silvares: University of São Paulo, Instituto de Psicologia, Av. Prof. Mello Moraes 1721, Cidade Universitária, São Paulo, Brazil 05508-030. Email: [efdmsilv@usp.br](mailto:efdmsilv@usp.br) Phone: 11-30911961.

85 33. Roma Simulioniene: Klaipeda University, Department of Psychology, Herkaus Manto str. 84, Klaipeda, Lithuania 92294. Email: [roma.simulioniene@ku.lt](mailto:roma.simulioniene@ku.lt) Phone: +370 46 398627.

87 34. Elvisa Sokoli: Department of Psychology, University of Tirana, Tirana, Albania. Email: [Elvisa_sokoli@yahoo.com](mailto:Elvisa_sokoli@yahoo.com)
89 35. Joel B. Talcott: Aston Brain Centre, School of Life and Health Sciences, Aston University,
90 Aston Triangle, Birmingham, United Kingdom B4 7ET. Email: j.b.talcott@aston.ac.uk.
91 Phone: +44 121 204 4083.

92 36. Natalia Vazquez: Pontificia Universidad Católica Argentina, Buenos Aires, Argentina.
93 Email: natalia_vazquez@uca.edu.ar.

94 37. Tomasz Wolanczyk, Department of Child Psychiatry, Medical University of Warsaw,
95 Warsaw, Poland. Email: tomasz.wolanczyk@wum.edu.pl.

96 38. Ewa Zasepa: The Maria Grzegorzewska Academy of Special Education, Room 3609,
97 Szczesliwicka 40, 02-353, Warsaw, Poland. Email: zasepa@aps.edu.pl. Phone: +48 22 589
98 3600.

99
100

101

102 Correspondence concerning this article should be addressed to William Copeland, 1 South

103 Prospect Street, Burlington, VT 05401. Email: william.copeland@med.uvm.edu.

104

105 Date of Revision: 10/20/22

106 Abstract: 220

107 Manuscript text (excluding abstract, refs, tables, and figures): 3,274

108 Tables and Figures: 5

109

110

111
**Abstract**

**Background:** It is unknown how much variation in adult mental health problems is associated with differences between societal/cultural groups, over and above differences between individuals.

**Methods:** To test these relative contributions, a consortium of indigenous researchers collected Adult Self-Report (ASR) ratings from 16,906 18- to 59-year-olds in 28 societies that represented 7 culture clusters identified in the Global Leadership and Organizational Behavioral Effectiveness study (e.g., Confucian, Anglo). The ASR is scored on 17 problem scales, plus a personal strengths scale. Hierarchical linear modeling estimated variance accounted for by individual differences (including measurement error), society, and culture cluster. Multi-level ANCOVAs tested age and gender effects.

**Results:** Across the 17 problem scales, the variance accounted for by individual differences ranged from 80.3% for DSM-Oriented Anxiety Problems to 95.2% for DSM-Oriented Avoidant Personality (mean = 90.7%); by society: 3.2% for DSM-Oriented Somatic Problems to 8.0% for DSM-Oriented Anxiety Problems (mean = 6.3%); and by culture cluster: 0.0% for DSM-Oriented Avoidant Personality to 11.6% for DSM-Oriented Anxiety Problems (mean = 3.0%). For strengths, individual differences accounted for 80.8% of variance, societal differences 10.5%, and cultural differences 8.7%. Age and gender had very small effects.

**Conclusions:** Overall, adults' self-ratings of mental health problems and strengths were associated much more with individual differences than societal/cultural differences, although this varied across scales. These findings support cross-cultural use of standardized measures to assess mental health problems, but urge caution in assessment of personal strengths.
134 **Keywords:** psychopathology, Adult Self-Report, syndromes, cross-cultural, international,

135 strengths

136

137
It is assumed that variations in ratings of mental health problems reflect primarily individual differences. But such variations may also reflect societal and cultural differences. The relevant questions are: *How much variation is due to societal and cultural differences?* and *Does the respective variation due to societal and cultural differences vary among different conditions?*

If the variations are largely due to societal and cultural differences, then a particular instrument may be less useful for individual treatment planning within particular societies/cultures as the instrument is not sensitive to individual differences within that society or culture. Arthur Kleinman described this concern over forty years ago (Kleinman, 1977). He questioned research that uncritically exported mental health instruments from one society or culture to another. This question highlights the contrast between *emic* approaches that focus on characteristics of a given culture versus *etic* approaches that focus on more universal aspects of cultures (Pike, 1967). This tension spurred the rise of transcultural psychiatry, the landmark 2001 WHO Report on Mental Health, and the burgeoning field of global mental health (Prince et al., 2007; World Health Organization, 2001).

It is now clear that certain mental disorders affect people across all regions of the world (Steel et al., 2014; WHO World Mental Health Survey Consortium, 2004). Even so, the construct of mental disorder itself is evolving. The Lancet Commission on Global Mental health recently admitted that “the binary approach to the diagnosis of mental disorder . . . does not adequately reflect the dimensional nature of mental health.”p.4 (Patel et al., 2018) Mental health, like physical health, exists on a continuum from mild, time-limited symptoms to severe, chronic debilitating conditions. To study the role of culture in mental health problems, one needs standardized measures that cover a broad continuum of human behavior and have been adopted and studied across many societies.
One measure that meets these criteria is the Adult Self-Report (ASR) (Achenbach & Rescorla, 2003). The ASR is a 15-20 minute questionnaire for ages 18-59 that assesses dimensions of behavioral, emotional, social, and thought problems, and personal strengths. It has been adopted widely and used in over 250 published studies. It has also been adapted for use across many societies. First, each non-English version of the ASR is the result of a process in which indigenous researchers make a translation of the ASR in their language and then obtain an independent back-translations to insure the accuracy of the initial translation (Achenbach & Rescorla, 2015). Next, confirmatory factor analysis has been used to test whether the ASR syndrome structure was supported across 29 societies (Ivanova et al., 2015). The primary model fit index (Root Mean Square Error of Approximation) showed good model fit for all samples (<0.03) and good to acceptable fit for secondary indices (e.g., CFI, TLI). Only 5 (0.06%) of the 8,598 estimated parameters were outside the admissible parameter space. These findings support similarities in the factor structure and factor loadings across societies. Additional analyses tested similarities in the mean ratings of individual ASR items and scale scores (Rescorla et al., 2016). As indicated by a mean correlation of 0.77 between the mean ratings of items for all pairs of societies, the rank order of item ratings was similar across societies. The mean scale scores of the scales themselves do vary across societies in a manner consistent with a normal distribution. Together, these findings support the ASR as a useful tool for dimensional assessment of mental health problems in diverse societies. It also provides a way to test effects of societal and cultural difference on adults' self-ratings.

We use “society” in reference to geopolitically demarcated populations that include but are not limited to countries. “Culture” is defined as an accumulated set of beliefs, values, and social norms which impact the behavior of a relatively large group of people (Lustig, Koester, &
Halualani, 2006). The Global Leadership and Organizational Behavior Effectiveness study (GLOBE) mapped cultures by analyzing responses by 17,000 participants in 62 societies to questionnaires on cultural dimensions proposed by Hofstede including assertiveness, gender egalitarianism, collectivism, and future orientation (Hofstede, 1984; House, Hanges, Javidan, Dorfman, & Gupta, 2004). Ten distinct “culture clusters” were derived from these dimensions (e.g., Confucian, Anglo, Latin America, Eastern Europe, Sub-Saharan Africa).

Stankov (2011) applied the GLOBE findings to test the effects of individual societies, GLOBE-defined culture clusters, and individual differences on personality measures completed by college students in 45 societies. For Neuroticism, the personality trait most relevant to mental health, individual differences accounted for 95.3% of the variance, societal differences 2.0%, and culture cluster differences 2.7%. The societal findings for personality are similar to those obtained in another study of 130,602 adults in 22 societies (Kajonius & Mac Giolla, 2017). A similar analytic approach was applied in two studies of ASR-related measures of child/youth mental health problems. For 11-18 year-olds’ self-ratings, individual differences accounted for 92.5% of variance across 17 problem scales, societal differences 6.0%, and cultural differences only 1.5%, indicating modest effects of society and culture (Ivanova et al., 2022). For 6-18 year-olds rated by their parents, individual differences accounted for 90% of the variance across problem scales, societal differences 6%, and cultural differences 4% (Rescorla, Althoff, Ivanova, & Achenbach, 2019).

## Purpose of the Present Study

In 28 societies representing 7 GLOBE culture clusters, we tested whether society and culture cluster would account for significant variance in adults’ self-rated mental health problems and personal strengths, over and above individual differences. Prior research on 11-18- and 6-18-
year-olds suggest modest effects of society and culture. But adults have had much longer exposure to the norms and influences of their society and culture than children and youths. To further examine the effects of society and culture cluster on self-rated mental health problems and strengths, we tested society and gender as predictors in one multi-level model and culture cluster and gender in a second multi-level model.

## Method

### Samples

Indigenous researchers independently arranged to have ASRs completed by 16,906 18- to 59-year-olds in the 28 societies listed in **Table 1**. These samples were pooled as part of an international consortium of mental health researchers. Samples averaged 42% male, and Ns ranged from 293 (Egypt) to 2,020 (US). As shown in Table 1, rigorous random sampling methods were used in some societies, resulting in representative population samples. However, in other societies, various methods of convenience sampling were necessary, resulting in samples of unknown representativeness. Follow up analyses tested models in 15 representative samples. Additional details of individual studies are available from cited references and/or listed primary investigators.

Using the GLOBE culture cluster taxonomy (House et al., 2004), we classified the 28 societies into the following 7 culture clusters: Anglo (N = 2362, 2 societies); Confucian Asia (N = 3182, 5 societies); Eastern Europe (N = 4475, 9 societies); Latin America (N = 2094, 4 societies); Latin Europe (N = 2094, 4 societies); Middle East (N = 676, 2 societies); and Sub-Saharan Africa (N = 826, 2 societies) (see Table 1). Following the standard ASR procedure, cases that were missing ratings for > 8 problem items were excluded from the analyses.

Indigenous researchers followed their institutions' procedures for protection of human
participants including obtaining informed consent. The lead authors' university human

participants board approved the overall project. All data were de-identified.

## Instrument and Tested Model

Indigenous mental health workers conducted translations of the ASR to their language and then obtained independent back-translations to insure the accuracy of the initial translation.

The ASR's 120 problem items are rated 0 = *not true*, 1 = *somewhat or sometimes true*, or 2 = *very true or often true*, based on the preceding 6 months. These items tap diverse emotional, behavioral, social, and thought problems, such as *I worry about my family*; *I am stubborn, sullen, or irritable*; *I argue a lot*; and *I have thoughts that other people would think are strange*. The 11 Personal Strengths items (e.g., *I make good use of my opportunities*; *I work up to my ability*; *I am pretty honest*; *I meet my responsibilities to my family*; *I try to be fair to others*; and *I am a happy person*) are rated on the same 0–1–2 scale with high ratings indicating positive characteristics.

Our analyses focused on 17 ASR problem scales and one personal strengths scale. The scales included eight syndromes derived from exploratory and confirmatory factor analyses of ratings by adults in US population and clinical samples (Achenbach & Rescorla, 2003a). The syndromes are *Anxious/Depressed*, *Withdrawn*, *Somatic Complaints*, *Thought Problems*, *Attention Problems*, *Rule-Breaking Behavior*, *Aggressive Behavior*, and *Intrusive Behavior*. We also analyzed three broad-spectrum scales: *Total Problems* (comprised of all problem items); *Internalizing* (Anxious/Depressed, Withdrawn/Depressed, and Somatic Complaints syndrome items); and *Externalizing* (Rule-Breaking Behavior, Aggressive Behavior, and Intrusive syndrome items). Six DSM-Oriented scales comprise ASR problem items identified by an international panel of experts as being very consistent with particular DSM-5 diagnostic
categories (Achenbach, 2013; Achenbach, Bernstein, & Dumenci, 2005). The DSM-Oriented scales are designated as *Depressive Problems*, *Anxiety Problems*, *Somatic Problems*, *Avoidant Personality*, *AD/H Problems*, and *Antisocial Personality*. Adults' ratings of strengths items comprise an 11-item Personal Strengths scale.

For Japan, items assessing illegal behavior (6. *I use drugs* (other than alcohol and nicotine) for nonmedical purposes; 57. *I physically attack people*; 82. *I steal*; and 92. *I do things that may cause me trouble with the law*) were omitted from the ASR because their endorsement by respondents would have legally obligated the investigators to report them to authorities. To account for these excluded items, we re-wrote our scale-scoring syntax from simply taking the sum of items comprising each scale to, instead, taking the mean of the items comprising each scale (when there were valid responses available for at least 50% of such items) and multiplied that value times the total number of items comprising the scale.

Based on US data, Achenbach and Rescorla (Achenbach & Rescorla, 2003a) reported alphas of .89 to .97 for the Internalizing, Externalizing, and Total Problem scales, .51 to .88 for the syndromes, and .68 to .84 for the DSM-Oriented scales. The ASR's 1-week test-retest correlations were .89 to .94 for the broad-band scales, .78 to .91 for the syndromes, and .77 to .86 for the DSM-Oriented scales. ASR items and scales discriminated significantly between demographically similar clinically referred and nonreferred samples of US adults. Additional ASR findings across societies are reported by (Achenbach & Rescorla, 2015; Ivanova et al., 2015; Rescorla et al., 2016).

## Analyses

The effects of individual, society, and culture cluster contributions to differences on ASR scales were tested with hierarchical linear modeling (HLM) estimated using PROC MIXED in
SAS 9.4.(SAS Institute, 2013). Mental health problem scores are positively skewed in general population samples (where many people obtain relatively low scores), but HLM has been found robust to deviations from normality, especially for large samples (Ketelsen, 2014; Man, Schumacker, Morell, & Wang, 2022; Schielzeth et al., 2020). Each ASR scale was tested separately in a multilevel model. Individual differences (i.e., differences between individuals within a society) and unspecified effects (i.e., measurement error) were entered at Level 1. Societal differences were entered at Level 2. Culture cluster differences were entered at Level 3. All multilevel models included intercepts and used the Restricted Maximum Likelihood estimator that provides more robust results. The percent of variance due to predictors at each level was calculated as the ratio of the respective level-specific variance component over total variance. First, we tested the Null model in which no predictors were entered at Level 1, and society and culture cluster were modeled as random effects at Levels 2 and 3. In addition to the random effects model, all results were retested in fixed effects models. For a small number of clusters, the fixed effects model can be more robust than the random effects model that assumes normality in cluster-specific random intercepts (McNeish & Kelley, 2019). Next, we added age and gender as fixed effects at Level 1 and reran the model for each ASR scale. Finally, we tested whether the economic status of societies was a stronger predictor of ASR scale scores than culture cluster. To do this, models were rerun with World Bank income group classification of societies based on Purchasing Power Parity (PPP) included as a Level 2 variable (World Bank, 2020).

To better understand how society and culture cluster interacted with age and gender in their relations to ASR scores, we used ANOVAs to test associations of Internalizing,
Externalizing, and Total Problem scores with society (28 societies) and culture cluster (7 clusters), plus age, gender, and all possible interactions.

## Results

**Figure 1** presents Internalizing and Externalizing scores by culture cluster. The 7 clusters are arranged in ascending order for mean Internalizing Problems score. Significant differences were observed between most culture clusters for both Internalizing and Externalizing Problems using Student-Newman-Keuls (SNK) post hoc tests. For Internalizing, there was only exception. The following clusters that did not differ significantly from each other: Sub-Saharan Africa and Middle East. For Externalizing, exceptions were Eastern Europe and Latin America; Latin America and Sub-Saharan Africa; and Anglo and Confucian. The rank-ordering of culture cluster for Externalizing differed from the rank-ordering for Internalizing.

**Table 2** presents the variance components estimated for the multilevel Null model for individual differences (Level 1), societies (Level 2), and culture cluster (Level 3). Averaged across the 17 problem scales (i.e. all scales except Personal Strengths), the percent of variance accounted for by individual differences was 90.7%, by society was 6.3%, and by culture cluster was 3.0%. Results based on fixed-effects models (which are less constrained with a small number of clusters) were similar for problem scales and can be found in Table S1.

For specific problem scales, the variance accounted for by individual differences ranged from 80.3% for DSM-Oriented Anxiety Problems to 95.2% for DSM-Oriented Avoidant Personality; by society: 3.2% for DSM-Oriented Somatic Problems to 8.0% for DSM-Oriented Anxiety Problems; and by culture cluster: 0.0% for DSM-Oriented Avoidant Personality to 11.6% for DSM-Oriented Anxiety Problems. Individual differences explained most of the variance in scores for problem scales, while society explained most of the remaining variance.
The variance accounted for by individual differences (80.8%) in Personal Strengths was substantially smaller than for the 17 problem scales (90.7%). Hence, the variance accounted for by society (10.5%) and by culture cluster (8.7%) was greater for Personal Strengths than for society and culture cluster averaged across the problem scales (6.3% and 3.0%, respectively). In the fixed effects models, the variance accounted for by culture cluster for Personal Strengths was greater still at 15.9%. **Figure 2** displays all ASR scales ranked from lowest to highest for total variance accounted for by society and culture cluster in random effects models. The total variance accounted for by society and culture cluster ranged from close to 5% for DSM Avoidant Personality to near 20% for Personal Strengths and DSM Anxiety. When all models were retested only including 15 representative samples, the results were similar to findings in Tables 2 and S1.

The multilevel models were rerun with age and gender as fixed effects at the individual level. Their addition did not significantly change the variance components for individual differences, society, or culture cluster for any scale. We then added the World Bank's PPP Index as a level 2 variable. In these models, individual differences accounted for 93.2% of the variance, society for 3.7%, culture for 2.1% and the World Bank PPP Index for 1.0%, averaged across 17 problem scales (see **Table S2**). These findings confirm that most of the variance in ASR problem scale scores was associated with individual differences. For personal strengths, individual differences accounted for 75.8% of the variance, society for 7.2%, Culture for 15.5% and the World Bank PPP Index for 1.4%.

**Table 3** presents the variance components from ANOVAs of Internalizing, Externalizing, and Total Problems scales. Predictors were society (28 societies) or culture cluster (7 clusters), plus age, gender, and all possible interactions. Results of ANOVAs for Internalizing,
344 Externalizing, and Total Problems scores indicated that effects of society (7.5%, 5.8%, and 7.4%, respectively) were larger than effects of culture cluster (2.9%, 1.7%, and 2.8%, respectively). In terms of main effects, age had larger effects on Externalizing and Total Problems than on Internalizing Problems (~5.1% vs. 1.8%), whereas gender had larger effects on Internalizing Problems than on Externalizing and Total Problems (~1.8% vs. 0.2%). All of the 24 effects involving age and gender interactions were ≤1%.

350

## Discussion

351 Our goal was to conduct the broadest test to date of the effects of society and culture on differences in adults' self-rated mental health problems and strengths. Several findings are noteworthy. First, most of the variance in adult problem ratings (~90%) was associated with individual differences. Of the remaining variance, society accounted for, on average, double the variance of culture cluster. These estimates, however, varied across constructs: Society and culture accounted for only 5% of the variance in DSM Avoidant Personality but up to 20% of the variance in DSM Anxiety Problems. The effects of society and culture on the Personal Strengths scale were twice as large as for the problem scales. Overall, the effects of society and culture on adults' self-rated problems were small to moderate, but they varied broadly--from small to large-across the different scales (Cohen, 1988).

361 We know of no other studies that have tested the effects of societal and cultural effects on adults' self-ratings of mental health problems. We know of one such study of parent ratings of children (Rescorla et al., 2019) and one of youth self-ratings (Ivanova et al., 2022). The results converge in three ways. Firstly, all three studies found that about 90% of the variance in problem scale scores was associated with individual differences (parent-ratings: 92.5%; youth self-ratings: 89.8%; adult self-ratings: 90.6%). This is striking given that the two child/youth studies
367 included societies and culture clusters not included here. Next, society generally accounted for 368 more of the variance in ratings of mental health problems than culture in all three studies (parent- 369 report: 6.1% vs. 4.2%; youth self-report: 6.0% vs. 1.5%; adult self-report: 6.3% vs. 3.0%).

370 Finally, there were similarities in the rank ordering of results for the individual mental health 371 scales. The DSM Anxiety scale showed the largest combined effects of society and culture in 372 both the adult and the youth self-rating studies (it was 3rd in the child parent-rating study). Also, 373 both the youth study and our study supported larger societal and cultural contributions to 374 Personal Strengths (16.6% in youth self-ratings and 19.2% in adult self-ratings) than to problem 375 scales.

376 Why did society and culture account for *twice as much* of the variance in ratings of 377 strengths as in mental health problems? A similar discrepancy was observed by Stankov (2011). 378 There, societal and cultural effects on personality scales were smaller than on social attitude and 379 norm scales. Stronger societal/cultural effects on social constructs than on personality scales 380 might be expected. The ASR and YSR strengths scales, however, do not assess social constructs 381 but rather self-ratings of strengths (e.g., *I make good use of my opportunities, I work up to my 382 ability, I am pretty honest*). Our notions of strengths may reflect values that are shared or defined 383 within different societal and cultural groups. Our notions of mental health problems, by contrast, 384 may be more universal because of how these problems impair functioning and cause distress. 385 Self-ratings of strengths may also be more affected by social desirability, but it is not clear why 386 this would be the case. Future research should attempt to clarify why the effects of society and 387 culture on self-ratings of strengths were larger than on self-ratings of problems. In any case, the 388 findings for personal strengths suggest caution in comparing personal strengths across societies 389 and cultures.
390 *Limitations*

391 Our study's strengths included: 1) Use of a standardized mental health measure that has
392 been adopted and tested extensively across many societies; and 2) Data from 16,906 adults
393 across 28 societies that represent 7 culture clusters. There are also limitations to consider. Our
394 samples were collected under varying conditions in diverse societies by indigenous researchers.
395 Some of our samples were of unknown representativeness because they were obtained using
396 convenience rather than random sampling methods. Second, our findings are limited to the
397 specific problems and strengths assessed by the ASR. Many societies and cultural groups were
398 not included in the study. The inclusion of additional societies and cultures might yield different
399 results. Also, other ways of classifying cultures might yield different results. In HLM analyses,
400 effects associated with individual differences included residual variance. The residual variance
401 may be due to other variables such as the adult's family, work, or local community. Third, our
402 work has previously demonstrated similarity in factor structure and factor loadings, but did not
403 formally test other aspects of measurement invariance. Finally, "society" and "culture" may be
404 associated with genetic differences, as well as with socio-cultural demarcations of populations.

405 *Conclusion*

406 Over the past 30 years, the study of mental health problems has been extended to many
407 societies around the world (Prince et al., 2007; World Health Organization, 2001). In this time,
408 the global health burden for mental health conditions has increased (Vos et al., 2020). The unmet
409 need for mental health treatment around the world is large and, sadly, growing. Despite the
410 myriad difference between societies and cultures with respect to geographic location,
411 political/economic systems, history, population, ethnicity/race, and religion, the mental health
412 conditions identified and studied across societies and cultures appear rather similar even if there
413 are differences in the mean levels of those conditions. In previous work, our international

414 consortium has obtained large alpha levels for ASR scales and a good fit for the syndrome

415 structure across societies. The current study suggests that societal and cultural effects on problem

416 scores are modest. Together, our findings suggest that cross-cultural use of standardized

417 measures like the ASR to assess individual mental health problems is warranted but suggest

418 more caution regarding personal strengths.

419
420

Acknowledgements

421

Dr. Copeland had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Copeland, Achenbach, and Ivanova contributed to the concept and design, all authors contributed to the acquisition, analysis and interpretation of the data, Drs. Copeland, Achenbach, and Ivanova contributed to the manuscript, all authors critically revised the manuscript for important intellectual content, and all authors provided final approval for the submitted manuscript.

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This work was funded in part by the nonprofit University of Vermont Research Center for Children, Youth, and Families which publishes the Adult Self-Report and from which authors Copeland, Achenbach, and Ivanova receive funding. The remaining authors have declared that they have no competing or potential conflicts of interest.

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<table><caption>Table 1. ASR samples</caption><thead><tr><th>Society</th><th>Reference</th><th>N</th><th>Age Range</th><th>Mean Age (SD)<sup>c</sup></th><th>% Male</th><th>Sample</th></tr></thead><tbody><tr><td>Sub-Saharan Africa (826)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1. Angola</td><td>Caldas (2012a)<sup>b</sup></td><td>399</td><td>18-59</td><td>18-25: 43%<br>26-39: 34%<br>40-49: 12%<br>50-59: 11%</td><td>63</td><td>Community sample.</td></tr><tr><td>2. Kenya</td><td>Harder & Ndetei (2013)<sup>b</sup></td><td>427</td><td>18-59</td><td>38.9 (8.5)</td><td>40</td><td>Regional sample of parents of school-aged children, with children's names randomly drawn from class rosters.</td></tr><tr><td>Anglo (2,363)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3. UK</td><td>Talcott, Nakubulwa, Virk, (2012)<sup>b</sup></td><td>343</td><td>18-59</td><td>34.0 (12.5)</td><td>35</td><td>Community sample.</td></tr><tr><td>4. US</td><td>Achenbach and Rescorla (2003)</td><td>2020</td><td>18-59</td><td>39.1 (12.0)</td><td>41</td><td>Recruited by stratified random sampling via households in 40 states using national statistics/census information; participants interviewed at home; representative sample stratified by age, gender, and</td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>urban-suburban-rural residence to be representative of the U.S. population.</td></tr><tr><td>Confucian Asia (3,182)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5. China</td><td>Liu (2012)<sup>b</sup></td><td>558</td><td>18-59</td><td>33.1 (9.6)</td><td>38</td><td>Community sample drawn from regions of mainland China.</td></tr><tr><td>6. Hong Kong</td><td>Au & Leung (2012)<sup>b</sup></td><td>324</td><td>18-59</td><td>29.4 (12.7)</td><td>39</td><td>Community sample stratified by age and gender to be representative of the Hong Kong population.</td></tr><tr><td>7. Japan<sup>a</sup></td><td>Funabiki (2012)<sup>b</sup></td><td>1,000</td><td>18-59</td><td>38.2 (10.7)</td><td>47</td><td>Community sample recruited by a research company.</td></tr><tr><td>8. Korea<sup>a</sup> (South)</td><td>Kim, Kim, & Oh (2009)</td><td>1,000</td><td>18-59</td><td>37.9 (9.8)</td><td>51</td><td>Representative national sample, randomly drawn from the national registry, with stratification by age, gender, and educational attainment.</td></tr><tr><td>9. Taiwan</td><td>Chen (2012)<sup>b</sup></td><td>300</td><td>18-59</td><td>37.0 (11.9)</td><td>50</td><td>Community sample stratified by region, gender, and age to be representative of the national population.</td></tr></table>
<table><tr><td>Eastern Europe (4475)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>10. Albania</td><td>Sokoli et al. (2016)</td><td>750</td><td>18-59</td><td>37.3 (12.8)</td><td>50</td><td>Nationally representative.</td></tr><tr><td>11. Czech Republic</td><td>Csemy (2012)<sup>b</sup></td><td>588</td><td>18-59</td><td>37.8 (12.4)</td><td>51</td><td>Community sample stratified by region, age, gender, and educational attainment to be representative of the national population.</td></tr><tr><td>12. Kosovo</td><td>Shahini & Ahmeti-Pronaj (2012)<sup>b</sup></td><td>571</td><td>18-59</td><td>30.6 (10.5)</td><td>40</td><td>Community sample.</td></tr><tr><td>13. Latvia</td><td>Sebre (2012)<sup>b</sup></td><td>302</td><td>18-59</td><td>33.9 (12.7)</td><td>43</td><td>Community sample stratified by age, gender, educational attainment, and region to be representative of the national population.</td></tr><tr><td>14. Lithuania</td><td>Šimulionienė et al. (2010)</td><td>573</td><td>18-59</td><td>35.3 (11.1)</td><td>48</td><td>Representative national sample randomly drawn from the national registry, with stratification by gender, age, and educational attainment.</td></tr><tr><td>15. Poland</td><td>Zasepa & Wolanczyk (2011)</td><td>310</td><td>18-59</td><td>36.7 (11.9)</td><td>37</td><td>Community sample stratified by age, gender, residence, and</td></tr></table>
<table><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>educational attainment to be representative of the national population.</td></tr><tr><td>16. Romania</td><td>Dobrean (2011)<sup>b</sup></td><td>638</td><td>20-56</td><td>24.2 (6.1)</td><td>15</td><td>University students.</td></tr><tr><td>17. Russia</td><td>Malykh (2012)<sup>b</sup></td><td>429</td><td>18-55</td><td>20.6 (4.3)</td><td>33</td><td>University students.</td></tr><tr><td>18. Serbia</td><td>Markovic (2012)<sup>b</sup></td><td>314</td><td>18-59</td><td>35.7 (10.6)</td><td>42</td><td>Representative sample of the Novi Sad metropolitan area randomly drawn from the population registry, with stratification by age.</td></tr><tr><td>Latin America (2094)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>19. Argentina</td><td>Samaniego & Vázquez (2012)</td><td>679</td><td>18-59</td><td>35.7 (12.0)</td><td>48</td><td>Community sample stratified by level of educational attainment to be representative of the greater Buenos Aires area.</td></tr><tr><td>20. Brazil</td><td>Silvares & da Rocha (2012)<sup>b</sup></td><td>813</td><td>18-59</td><td>34.5 (11.7)</td><td>41</td><td>Community sample stratified by region, age, gender, and socioeconomic status to be representative of the national population.</td></tr></table>
<table><tr><td>21. Chile</td><td>Lecannelier (2013)<sup>b</sup></td><td>294</td><td>18-58</td><td>25.0 (10.1)</td><td>34</td><td>Community sample from 2 Chilean cites.</td></tr><tr><td>22. Mexico</td><td>Leiner de la Cabada &amp; Avila Maese (2013)<sup>b</sup></td><td>308</td><td>18-59</td><td>27.3 (9.8)</td><td>59</td><td>Community sample.</td></tr><tr><td>Latin Europe (3290)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>23. France</td><td>Mahr et al. (2018)</td><td>1,238</td><td>18-59</td><td>24.5 (7.4)</td><td>29</td><td>University students.</td></tr><tr><td>24. Italy</td><td>Bellina (2012)<sup>b</sup></td><td>519</td><td>18-59</td><td>38.0 (12.4)</td><td>46</td><td>Representative sample of the Lecco province randomly drawn from the electoral roll.</td></tr><tr><td>25. Portugal</td><td>Caldas (2012b)<sup>b</sup></td><td>397</td><td>18-59</td><td>35.4(12.0)</td><td>49</td><td>Community sample stratified by age and gender to be representative of the national population.</td></tr><tr><td>26. Spain</td><td>Ezpeleta et al. (2014)</td><td>1,136</td><td>18-58</td><td>37.6(5.3)</td><td>48</td><td>Community sample of parents of preschoolers in the greater Barcelona metropolitan area randomly drawn from the registry of parents of preschoolers.</td></tr><tr><td>Middle East (676)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>27. Egypt</td><td>Riad (2012)<sup>b</sup></td><td>293</td><td>18-59</td><td>25.7(8.2)</td><td>29</td><td>Community sample.</td></tr></table>
<table><tr><td>28. Turkey</td><td>Sakarya (2012)<sup>b</sup></td><td>383</td><td>18-58</td><td>25.6(8.2)</td><td>24</td><td>Community sample.</td></tr></table>

Note. <sup>a</sup>The identical sample sizes for Japan and Korea are coincidental, not errors. <sup>b</sup>Unpublished data. <sup>c</sup>Only age ranges were available for Angola.
<table><caption>Table 2. Percent of variance accounted for by individual, society, and GLOBE culture cluster effects in hierarchical linear models of adult self-ratings on the ASR</caption><thead><tr><th>ASR scale</th><th>Individual differences</th><th>Society</th><th>Culture cluster</th></tr></thead><tbody><tr><td><strong>Broad-spectrum scales</strong></td><td></td><td></td><td></td></tr><tr><td>Internalizing Problems</td><td>89.7</td><td>6.2</td><td>4.1</td></tr><tr><td>Externalizing Problems</td><td>91.1</td><td>7.1</td><td>1.8</td></tr><tr><td>Total Problems</td><td>88.4</td><td>7.8</td><td>3.9</td></tr><tr><td><strong>Syndromes</strong></td><td></td><td></td><td></td></tr><tr><td>Anxious/Depressed</td><td>91.9</td><td>6.6</td><td>1.5</td></tr><tr><td>Withdrawn</td><td>88.6</td><td>6.2</td><td>5.1</td></tr><tr><td>Somatic Complaints</td><td>92.3</td><td>4.1</td><td>3.6</td></tr><tr><td>Thought Problems</td><td>87.6</td><td>10.1</td><td>2.3</td></tr><tr><td>Attention Problems</td><td>93.1</td><td>6.3</td><td>0.6</td></tr><tr><td>Rule-breaking Behavior</td><td>92.6</td><td>5.9</td><td>1.5</td></tr><tr><td>Aggressive Behavior</td><td>92.2</td><td>6.0</td><td>1.8</td></tr><tr><td>Intrusive Behavior</td><td>90.4</td><td>7.9</td><td>1.6</td></tr><tr><td><strong>DSM-Oriented scales</strong></td><td></td><td></td><td></td></tr><tr><td>Depressive Problems</td><td>93.8</td><td>4.1</td><td>2.1</td></tr><tr><td>Anxiety Problems</td><td>80.4</td><td>8.0</td><td>11.6</td></tr><tr><td>Somatic Problems</td><td>91.1</td><td>3.2</td><td>5.6</td></tr><tr><td>Avoidant Personality</td><td>95.2</td><td>4.8</td><td>0.0</td></tr><tr><td>AD/H Problems</td><td>91.9</td><td>5.4</td><td>2.7</td></tr><tr><td>Antisocial Personality</td><td>91.3</td><td>7.2</td><td>1.5</td></tr><tr><td>Averaged Across Problem scales</td><td>90.7</td><td>6.3</td><td>3.0</td></tr><tr><td>Personal Strengths</td><td>80.8</td><td>10.5</td><td>8.7</td></tr></tbody></table>

All effects of individual differences and society were significant (p < .001). Effects of culture cluster did not reach the p < .05 level of significance.
<table><caption>Table 3 Percent of variance accounted for by individual and society or GLOBE culture cluster in ANOVAs of ASR Internalizing, Externalizing, and Total Problems scores</caption><thead><tr><th>Predictor</th><th>Internalizing</th><th>Externalizing</th><th>Total Problems</th></tr></thead><tbody><tr><td>Age</td><td>1.8<sup>a</sup></td><td>5.2<sup>a</sup></td><td>5.1<sup>a</sup></td></tr><tr><td>Gender</td><td>1.7<sup>a</sup></td><td>0.1<sup>a</sup></td><td>0.3<sup>a</sup></td></tr><tr><td>Society</td><td>7.5<sup>a</sup></td><td>5.8<sup>a</sup></td><td>7.4<sup>a</sup></td></tr><tr><td>Age*Gender</td><td>0.1<sup>a</sup></td><td>0.01</td><td>0.04<sup>a</sup></td></tr><tr><td>Age*Society</td><td>0.9<sup>a</sup></td><td>0.6<sup>a</sup></td><td>0.9<sup>b</sup></td></tr><tr><td>Gender*Society</td><td>0.3<sup>a</sup></td><td>0.4<sup>a</sup></td><td>0.2<sup>b</sup></td></tr><tr><td>Age*Gender*Society</td><td>0.2</td><td>0.2</td><td>0.2</td></tr><tr><td>Age</td><td>1.8<sup>a</sup></td><td>5.3<sup>a</sup></td><td>5.1<sup>a</sup></td></tr><tr><td>Gender</td><td>1.7<sup>a</sup></td><td>0.1<sup>a</sup></td><td>0.3<sup>a</sup></td></tr><tr><td>Culture Cluster</td><td>2.9<sup>a</sup></td><td>1.7<sup>a</sup></td><td>2.8<sup>a</sup></td></tr><tr><td>Age*Gender</td><td>0.1<sup>a</sup></td><td>0.01</td><td>0.04<sup>c</sup></td></tr><tr><td>Age* Culture Cluster</td><td>0.5<sup>a</sup></td><td>0.3<sup>a</sup></td><td>0.4<sup>a</sup></td></tr><tr><td>Gender* Culture Cluster</td><td>0.1<sup>a</sup></td><td>0.1<sup>c</sup></td><td>0.06</td></tr><tr><td>Age*Gender* Culture Cluster</td><td>0.04</td><td>0.03</td><td>0.03</td></tr></tbody></table>

<footnote><sup>a</sup>p < .0001; <sup>b</sup>p < .01; <sup>c</sup>p < .05.</footnote>