Health Behaviour in School-aged Children; WHO-5 Well-Being Index; adolescents; cross-cultural research; differential item functioning; item response theory; measurement invariance; short scale; well-being
Abstract :
[en] The five-item World Health Organization Well-Being Index (WHO-5) is among the most frequently used brief standard measures to assess hedonic well-being. Numerous studies have investigated different facets of its psychometric properties in adult populations. However, whether these results apply to adolescents is uncertain, and only few psychometric studies employed adolescent populations. Thus, the current study aimed to conduct an in-depth psychometric item response theory analysis of the WHO-5 among adolescents from 43 countries using the Health Behaviour in School-aged Children (HBSC) 2022 data set and investigated its (a) dimensionality and measurement structure, (b) test information values and marginal reliability, (c) cross-country measurement invariance and differential item/test functioning, and (d) convergent validity with other measures related to mental health and well-being across countries. The WHO-5 showed a unidimensional measurement structure and overall high test information values and marginal reliability. Furthermore, although a large proportion of parameters were flagged as non-invariant, differential test functioning of the WHO-5 was only modest. Moreover, the WHO-5 mainly showed a concurring nomological network with the other measures related to mental health and well-being across countries, although with some differences in effect sizes. The WHO-5 Well-Being Index is a psychometrically sound measure that has shown promise for cross-cultural research among adolescents in the included European, Central Asia, and North American countries. The translated versions of the WHO-5 are available at https://osf.io/pbexq.
Disciplines :
Theoretical & cognitive psychology
Author, co-author :
SISCHKA, Philipp ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Behavioural and Cognitive Sciences (DBCS) > Health and Behaviour
Research of Excellence on Digital Technologies and Wellbeing European Union’s Horizon 2020 research and innovation programme Centre for Development, Evaluation, Complexity and Implementation in Public Health Improvement
Adjorlolo S. Anum A. (2021). Positive and negative psychosis risk symptoms among adolescents in Ghana. International Journal of Adolescence and Youth, 26(1), 307–320. https://doi.org/10.1080/02673843.2021.1933110
Aliyev B. Rustamov E. Satici S. A. Zalova Nuriyeva U. (2024). Azerbaijani adaptation of the WHO-5 wellbeing index: Investigating its relationship with psychological distress, resilience, and life satisfaction. BMC Psychology, 12, Article 100. https://doi.org/10.1186/s40359-024-01593-0
Allgaier A.-K. Pietsch K. Frühe B. Prast E. Sigl-Glöckner J. Schulte-Körne G. (2012). Depression in pediatric care: Is the WHO-Five Well-Being Index a valid screening instrument for children and adolescents? General Hospital Psychiatry, 34(3), 234–241. https://doi.org/10.1016/j.genhosppsych.2012.01.007
Asparouhov T. Muthén B. (2014). Multiple-group factor analysis alignment. Structural Equation Modeling, 21(4), 495–508. https://doi.org/10.1080/10705511.2014.919210
Baker F. B. (2001). The basics of item response theory (2nd ed.). ERIC Clearinghouse on Assessment and Evaluation.
Bech P. (2012). Clinical psychometrics. John Wiley and Sons.
Blom E. H. Bech P. Högberg G. Larsson J. O. Serlachius E. (2012). Screening for depressed mood in an adolescent psychiatric context by brief self-assessment scales–testing psychometric validity of WHO–5 and BDI-6 indices by latent trait analyses. Health and Quality of Life Outcomes, 10(1), 149. https://doi.org/10.1186/1477-7525-10-149
Boer D. Hanke K. He J. (2018). On detecting systematic measurement error in cross-cultural research: A review and critical reflection on equivalence and invariance tests. Journal of Cross-Cultural Psychology, 49(5), 713–734. https://doi.org/10.1177/0022022117749042
Boer M. Moreno-Maldonado C. Dierckens M. Lenzi M. Currie C. Residori C...Stevens G. (2023). The implications of the COVID-19 pandemic for the construction of the family affluence scale: Findings from 16 countries. Child Indicators Research, 17, 395–418. https://doi.org/10.1007/s12187-023-10082-6
Borgers N. De Leeuw E. Hox J. (2000). Children as respondents in survey research: Cognitive development and response quality 1. Bulletin of Sociological Methodology/Bulletin de méthodologie sociologique, 66(1), 60–75. https://doi.org/10.1177/07591063000660010
Brown A. (2018). Item response theory approaches to test scoring and evaluating the score accuracy. In Irwing F. P. Booth T. Hughes D. J. (Eds.), The Wiley handbook of psychometric testing: A multidisciplinary reference on survey, scale, and test development (pp. 607–638). Wiley Blackwell.
Cai L. Monroe S. (2014). A new statistic for evaluating item response theory models for ordinal data. https://files.eric.ed.gov/fulltext/ED555726.pdf
Cai L. Yang J. S. Hansen M. (2011). Generalized full-information item bifactor analysis. Psychological Methods, 16(3), 221–248. https://doi.org/10.1037/a0023350
Chalmers R. P. (2012). mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1–29. https://doi.org/10.18637/jss.v048.i06
Chalmers R. P. (2018). Model-based measures for detecting and quantifying response bias. Psychometrika, 83(3), 696–732. https://doi.org/10.1007/s11336-018-9626-9
Chen F. F. (2008). What happens if we compare chopsticks with forks? The impact of making inappropriate comparisons in cross-cultural research. Journal of Personality and Social Psychology, 95(5), 1005–1018. https://doi.org/10.1037/a0013193
Clarke A. Friede T. Putz R. Ashdown J. Martin S. Blake A...Stewart-Brown S. (2011). Warwick-Edinburgh Mental Well-being Scale (WEMWBS): Validated for teenage school students in England and Scotland. A mixed methods assessment. BMC Public Health, 11, Article 487. https://doi.org/10.1186/1471-2458-11-487
Conijn J. M. Smits N. Hartman E. E. (2020). Determining at what age children provide sound self-reports: An illustration of the validity-index approach. Assessment, 27(7), 1604–1618. https://doi.org/10.1177/107319111983265
Cooper C. (2019). Psychological testing: Theory and practice. Routledge.
Cosma A. Abdrakhmanova S. Taut D. Schrijvers K. Catunda C. Schnohr C. (2023). A focus on adolescent mental health and wellbeing in Europe, central Asia and Canada. Health Behaviour in School-aged Children international report from the 2021/2022 survey. Volume 1. Copenhagen: WHO Regional Office for Europe; 2023. Licence: CC BY-NC-SA 3.0 IGO
Cosma A. Költő A. Chzhen Y. Kleszczewska D. Kalman M. Martin G. (2022). Measurement invariance of the WHO–5 Well-Being Index: Evidence from 15 European countries. International Journal of Environmental Research and Public Health, 19(16), 9798. https://doi.org/10.3390/ijerph19169798
De Ayala R. J. (2009). The theory and practice of item response theory. Guilford Press.
De Champlain A. F. (2010). A primer on classical test theory and item response theory for assessments in medical education. Medical Education, 44(1), 109–117. https://doi.org/10.1111/j.1365-2923.2009.03425.x
DeMars C. E. (2018). Classical test theory and item response theory. In Irwing F. P. Booth T. Hughes D. J. (eds.), The Wiley handbook of psychometric testing: A multidisciplinary reference on survey, scale, and test development (pp. 49–73). Wiley Blackwell.
DeMars C. E. (2020). Alignment as an alternative to anchor purification in DIF analyses. Structural Equation Modeling, 27(1), 56–72. https://doi.org/10.1080/10705511.2019.1617151
Depaoli S. Tiemensma J. Felt J. M. (2018). Assessment of health surveys: Fitting a multidimensional graded response model. Psychology, Health & Medicine, 23(Suppl. 1), 13–31. https://doi.org/10.1080/13548506.2018.1447136
de Wit M. Pouwer F. Gemke R. J. B. J. Delemarre-van De Waal H. A. Snoek F. J. (2007). Validation of the WHO–5 Well-Being Index in adolescents with type 1 diabetes. Diabetes Care, 30(8), 2003–2006. https://doi.org/10.2337/dc07-0447
de Wit M. Winterdijk P. Aanstoot H. J. Anderson B. Danne T. Deeb L.,.. DAWN Youth Advisory Board. (2012). Assessing diabetes-related quality of life of youth with type 1 diabetes in routine clinical care: The MIND Youth Questionnaire (MY-Q). Pediatric Diabetes, 13(8), 638–646. https://doi.org/10.1111/j.1399-5448.2012.00872.x
Diener E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. American Psychologist, 55(1), 34–43. https://doi.org/10.1037/0003-066X.55.1.34
Edwards M. C. Houts C. R. Cai L. (2018). A diagnostic procedure to detect departures from local independence in item response theory models. Psychological Methods, 23(1), 138–149. https://doi.org/10.1037/met0000121
Elgar F. Xie A. Pförtner T. White J. Pickett K. (2017). Assessing the view from bottom: How to measure socioeconomic position and relative deprivation in adolescents. In Sage research methods cases part 2. Sage. https://doi.org/10.4135/9781526406347
Fox J. Weisberg S. (2019). An R companion to applied regression (3rd ed.). Sage.
Golino H. Christensen A. P. (2024). EGAnet: Exploratory graph analysis – A framework for estimating the number of dimensions in multivariate data using network psychometrics. (R package version 2.0.9). https://CRAN.R-project.org/package=EGAnet
Golino H. F. Epskamp S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLOS ONE, 12(6), Article e0174035. https://doi.org/10.1371/journal.pone.0174035
Golino H. F. Shi D. Christensen A. P. Garrido L. E. Nieto M. D. Sadana R...Martinez-Molina A. (2020). Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychological Methods, 25(3), 292–320. https://doi.org/10.1037/met0000255
González-Carrasco M. Casas F. Malo S. Viñas F. Dinisman T. (2017). Changes with age in subjective well-being through the adolescent years: Differences by gender. Journal of Happiness Studies, 18, 63–88. https://doi.org/10.1007/s10902-016-9717-1
Hallquist M. N. Wiley J. F. (2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus. Structural Equation Modeling, 25(4), 621–638. https://doi.org/10.1080/10705511.2017.1402334
He C. Levis B. Riehm K. E. Saadat N. Levis A. W. Azar M...Benedetti A. (2020). The accuracy of the patient health questionnaire-9 algorithm for screening to detect major depression: An individual participant data meta-analysis. Psychotherapy and Psychosomatics, 89(1), 25–37. https://doi.org/10.1159/000502294
Heinz A. Sischka P. E. Catunda C. Cosma A. García-Moya I. Lyyra N. Ravens-Sieberer U. Pickett W. (2022). Item response theory and differential test functioning analysis of the HBSC-Symptom-Checklist across 46 countries. BMC Medical Research Methodology, 22, Article 253. https://doi.org/10.1186/s12874-022-01698-3
Henrich J. Heine S. J. Norenzayan A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 61–83. https://doi.org/10.1017/S0140525X0999152X
Houts C. R. Savord A. Wirth R. J. (2022). Overview of modern measurement theory and examples of its use to measure execution function in children. Journal of Pediatric Neuropsychology, 8, 1–14. https://doi.org/10.1007/s40817-021-00117-7
Inchley J. Currie D. Samdal O. JÍstad A. Cosma A. Nic Gabhainn S. (Eds.). (2023). Health Behaviour in School-aged Children (HBSC) study protocol: Background, methodology and mandatory items for the 2021/22 survey. MRC/CSO Social and Public Health Sciences Unit, University of Glasgow.
Jacobs P. Power L. Davidson G. Devaney J. McCartan C. McCusker P. Jenkins R. (2024). A scoping review of mental health and wellbeing outcome measures for children and young people: Implications for children in out-of-home care. Journal of Child & Adolescent Trauma, 17, 159–185. https://doi.org/10.1007/s40653-023-00566-6
Jami W. A. Kemmelmeier M. (2020). Assessing well-being across space and time: Measurement equivalence of the WHO–5 in 36 European countries and over 8 years. Journal of Well-Being Assessment, 4(3), 419–445. https://doi.org/10.1007/s41543-021-00042-8
Javeline D. (1999). Response effects in polite cultures: A test of acquiescence in Kazakhstan. Public Opinion Quarterly, 63(1), 1–28. https://doi.org/10.1086/297701
Kang T. Chen T. T. (2011). Performance of the generalized S-X2 item fit index for the graded response model. Asia Pacific Education Review, 12(1), 89–96. https://doi.org/10.1007/s12564-010-9082-4
Kassambara A (2023). ggpubr: ‘ggplot2’ based publication ready plots. (R package version 0.6.0). https://CRAN.R-project.org/package=ggpubr
Kim E. S. Cao C. Wang Y. Nguyen D. T. (2017). Measurement invariance testing with many groups: A comparison of five approaches. Structural Equation Modeling, 24(4), 524–544. https://doi.org/10.1080/10705511.2017.1304822
Kline R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Krause K. R. Jacob J. Szatmari P. Hayes D. (2022). Readability of commonly used quality of life outcome measures for youth self-report. International Journal of Environmental Research and Public Health, 19(15), Article 9555. https://doi.org/10.3390/ijerph19159555
Krieger T. Zimmermann J. Huffziger S. Ubl B. Diener C. Kuehner C. Grosse Holtforth M. (2014). Measuring depression with a well-being index: Further evidence for the validity of the WHO Well-Being Index (WHO–5) as a measure of the severity of depression. Journal of Affective Disorders, 156, 240–244. https://doi.org/10.1016/j.jad.2013.12.015
Kusier A. O. Folker A. P. (2020). The Well-Being Index WHO–5: Hedonistic foundation and practical limitations. Medical Humanities, 46(3), 333–339. https://doi.org/10.1136/medhum-2018-011636
Lambert M. Fleming T. Ameratunga S. Robinson E. Crengle S. Sheridan J...Merry S. (2014). Looking on the bright side: An assessment of factors associated with adolescents’ happiness. Advances in Mental Health, 12(2), 101–109. https://doi.org/10.1080/18374905.2014.11081888
Lara-Cabrera M. L. Betancort M. Muñoz-Rubilar A. Rodríguez-Novo N. Bjerkeset O. Cuevas C. D. L. (2022). Psychometric properties of the WHO–5 Well-Being Index among nurses during the COVID-19 pandemic: A cross-sectional study in three countries. International Journal of Environmental Research and Public Health, 19(16), 10106. https://doi.org/10.3390/ijerph191610106
Larmarange J. (2024). labelled: Manipulating labelled data. (R package version 2.13.0). https://CRAN.R-project.org/package=labelled
Lim H. (2024). irtQ: Unidimensional item response theory modeling. (R package version 0.2.1). https://CRAN.R-project.org/package=irtQ
Little T. D. (2013). Longitudinal structural equation modeling. Guilford Press.
Low K.-Y. Pheh K.-S. Tan C.-S. (2023). Validation of the WHO–5 as a screening tool for depression among young adults in Malaysia. Current Psychology, 42(10), 7841–7844. https://doi.org/10.1007/s12144-021-02152-1
Lüdecke D. (2022). sjlabelled: Labelled data utility functions. (R package version 1.2.0). https://CRAN.R-project.org/package=sjlabelled
Maassen E. D’Urso E. D. Van Assen M. A. L. M. Nuijten M. B. De Roover K. Wicherts J. M. (2023). The dire disregard of measurement invariance testing in psychological science. Psychological Methods. https://doi.org/10.1037/met0000624
Marsh H. W. Guo J. Parker P. D. Nagengast B. Asparouhov T. Muthén B. Dicke T. (2018). What to do when scalar invariance fails: The extended alignment method for multi-group factor analysis comparison of latent means across many groups. Psychological Methods, 23(3), 524–545. https://doi.org/10.1037/met0000113
McDowell I. (2010). Measures of self-perceived well-being. Journal of Psychosomatic Research, 69(1), 69–79. https://doi.org/10.1016/j.jpsychores.2009.07.002
McNeish D. Wolf M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods, 28(1), 61–88. https://doi.org/10.1037/met0000425
Michel G. Bisegger C. Fuhr D. C. Abel T. (2009). Age and gender differences in health-related quality of life of children and adolescents in Europe: A multilevel analysis. Quality of Life Research, 18(9), 1147–1157. https://doi.org/10.1007/s11136-009-9538-3
Millsap R. E. (2011). Statistical approaches to measurement invariance. Routledge.
Monroe S. Cai L. (2015). Evaluating structural equation models for categorical outcomes: A new test statistic and a practical challenge of interpretation. Multivariate Behavioral Research, 50(6), 569–583. https://doi.org/10.1080/00273171.2015.1032398
Muthén B. Asparouhov T. (2014). IRT studies of many groups: The alignment method. Frontiers in Psychology, 5, Article 978. https://doi.org/10.3389/fpsyg.2014.00978
Muthén L. K. Muthén B. O. (1998-2017). Mplus user’s guide, 8th edition. Muthén and Muthén.
Nguyen T. H. Han H.-R. Kim M. T. Chan K. S. (2014). An introduction to item response theory for patient-reported outcome measurement. The Patient: Patient-Centered Outcomes Research, 7, 23–35. https://doi.org/10.1007/s40271-013-0041-0
O’Connor B. P. (2018). An illustration of the effects of fluctuations in test information on measurement error, the attenuation of effect sizes, and diagnostic reliability. Psychological Assessment, 30(8), 991–1003. https://doi.org/10.1037/pas0000471
Ostini R. Finkelman M. Nering M. (2015). Selecting among polytomous IRT models. In Reise S. P. Revicki D. A. (Eds.), Handbook of item response theory modeling: Applications to typical performance assessment (pp. 285–304). Routledge, Taylor & Francis Group.
Parchami A. (2016). Weighted.Desc.Stat: Weighted descriptive statistics. (R package version 1.0.4). https://CRAN.R-project.org/package=Weighted.Desc.Stat
Pasek J. (2021). weights: Weighting and weighted statistics. (R package version 1.0.4). https://CRAN.R-project.org/package=weights
Patton G. C. Sawyer S. M. Santelli J. S. Ross D. A. Afifi R. Allen N. B...Viner R. M. (2016). Our future: A Lancet commission on adolescent health and wellbeing. The Lancet, 387(10036), 2423–2478. https://doi.org/10.1016/S0140-6736(16)00579-1
Peter S. C. Whelan J. P. Pfund R. A. Meyers A. W. (2018). A text comprehension approach to questionnaire readability: An example using gambling disorder measures. Psychological Assessment, 30(12), 1567–1580. https://doi.org/10.1037/pas0000610
Quansah F. Hagan J. E. Ankomah F. Agormedah E. K. Nugba R. M. Srem-Sai M. Schack T. (2022). Validation of the WHO-5 Well-Being Scale among Adolescents in Ghana: Evidence-Based Assessment of the Internal and External Structure of the Measure. Children, 9(7), 991. https://doi.org/10.3390/children9070991
R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Rees G. Main G. (2016). Subjective well-being and mental health. In Bradshaw J. (ed.), The well-being of children in the UK (pp. 123–148). Policy Press.
Rose T. Joe S. Williams A. Harris R. Betz G. Stewart-Brown S. (2017). Measuring mental wellbeing among adolescents: A systematic review of instruments. Journal of Child and Family Studies, 26(9), 2349–2362. https://doi.org/10.1007/s10826-017-0754-0
Salk R. H. Hyde J. S. Abramson L. Y. (2017). Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychological Bulletin, 143(8), 783–822. https://doi.org/10.1037/bul0000102
Schnohr C. W. Gobina I. Santos T. Mazur J. Alikasifuglu M. Välimaa R...Torsheim T. (2016). Semantics bias in cross-national comparative analyses: Is it good or bad to have “fair” health? Health and Quality of Life Outcomes, 14, 70. https://doi.org/10.1186/s12955-016-0469-8
Schnohr C. W. Molcho M. Rasmussen M. Samdal O. de Looze M. Levin K...Torsheim T. (2015). Trend analyses in the Health Behaviour in School-aged Children study: Methodological considerations and recommendations. The European Journal of Public Health, 25(Suppl. 2), 7–12. https://doi.org/10.1093/eurpub/ckv010
Sellbom M. Tellegen A. (2019). Factor analysis in psychological assessment research: Common pitfalls and recommendations. Psychological Assessment, 31(12), 1428–1441. https://doi.org/10.1037/pas0000623
Shaffer-Hudkins E. Suldo S. Loker T. March A. (2010). How adolescents’ mental health predicts their physical health: Unique contributions of indicators of subjective well-being and psychopathology. Applied Research in Quality of Life, 5(3), 203–217. https://doi.org/10.1007/s11482-010-9105-7
Shi D. Maydeu-Olivares A. (2020). The effect of estimation methods on SEM fit indices. Educational and Psychological Measurement, 80(3), 421–445. https://doi.org/10.1177/00131644198851642
Sischka P., E. Albert I. Kornadt A. E. (2024). Validation of the 10-item Social Provision Scale (SPS-10): Evaluating factor structure, reliability, measurement invariance, and nomological network across 38 countries. Assessment. Advance online publication. https://doi.org/10.1177/10731911241283609
Sischka P. E. Costa A. P. Steffgen G. Schmidt A. F. (2020). The WHO–5 Well-Being Index–validation based on item response theory and the analysis of measurement invariance across 35 countries. Journal of Affective Disorders Reports, 1, 100020. https://doi.org/10.1016/j.jadr.2020.100020
Sischka P. E. Grübbel L. Reisinger C. Neufang K. M. Schmidt A. F. (2024). On the dimensionality, suitability of sum/mean scores, and cross-country measurement invariance of the Perceived Stress Scale 10 (PSS-10)—Evidence from 41 countries. International Journal of Stress Management, 31, 375–391.
Sischka P. E. Schmidt A. F. Steffgen G. (2020). Further evidence for criterion validity and measurement invariance of the Luxembourg Workplace Mobbing Scale. European Journal of Psychological Assessment, 36(1), 32–43. https://doi.org/10.1027/1015-5759/a000483
Steinberg D. M. Anderson B. J. de Wit M. Hilliard M. E. (2017). Positive Well-Being in Youth With Type 1 Diabetes During Early Adolescence. The Journal of Early Adolescence, 38(9), 1215–1235. https://doi.org/10.1177/0272431617692444
Sweeting H. Hunt K. (2014). Adolescent socio-economic and school-based social status, health and well-being. Social Science and Medicine, 121, 39–47. https://doi.org/10.1016/j.socscimed.2014.09.037
Taber S. M. (2010). The veridicality of children’s reports of parenting: A review of factors contributing to parent–child discrepancies. Clinical Psychology Review, 30(8), 999–1010. https://doi.org/10.1016/j.cpr.2010.06.014
Tejada-Gallardo C. Blasco-Belled A. Torrelles-Nadal C. Alsinet C. (2020). Effects of school-based multicomponent positive psychology interventions on well-being and distress in adolescents: A systematic review and meta-analysis. Journal of Youth and Adolescence, 49(10), 1943–1960. https://doi.org/10.1007/s10964-020-01289-9
Tittel S. R. Kulzer B. Warschburger P. Merz U. Galler A. Wagner C...Holl R. W. (2023). The WHO-5 well-being questionnaire in type 1 diabetes: Screening for depression in pediatric and young adult subjects. Journal of Pediatric Endocrinology and Metabolism, 36(4), 384–392. https://doi.org/10.1515/jpem-2023-0013
Toland M. D. (2014). Practical guide to conducting an item response theory analysis. The Journal of Early Adolescence, 34(1), 120–151. https://doi.org/10.1177/0272431613511332
Tomás J. M. Gutiérrez M. Pastor A. M. Sancho P. (2020). Perceived social support, school adaptation and adolescents’ subjective well-being. Child Indicators Research, 13, 1597–1617. https://doi.org/10.1007/s12187-020-09717-9
Topp C. W. Østergaard S. D. Søndergaard S. Bech P. (2015). The WHO–5 Well-Being Index: A systematic review of the literature. Psychotherapy and Psychosomatics, 84(3), 167–176. https://doi.org/10.1159/000376585
Torsheim T. Cavallo F. Levin K. A. Schnohr C. Mazur J. Niclasen B.,.. The FAS Development Study Group. (2016). Psychometric validation of the revised family affluence scale: A latent variable approach. Child Indicators Research, 9(3), 771–784. https://doi.org/10.1007/s12187-015-9339-x
von Glischinski M. von Brachel R. Hirschfeld G. (2019). How depressed is “depressed”? A systematic review and diagnostic meta-analysis of optimal cut points for the Beck Depression Inventory revised (BDI-II). Quality of Life Research, 28, 1111–1118. https://doi.org/10.1007/s11136-018-2050-x
Watkins M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807
Wells C. S. Hambleton R. K. (2016). Model fit with residual analyses. In van der Linden W. J. (Ed.), Handbook of item response theory, volume 2: Statistical tools (pp. 395–413). CRC Press.
Wickham H. (2016). ggplot2. Elegant graphics for data analysis. Springer.
Wickham H. Averick M. Bryan J. Chang W. McGowan L. D. A. Francxois R...Yutani H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Widaman K. F. Ferrer E. Conger R. D. (2010). Factorial invariance within longitudinal structural equation models: Measuring the same construct across time. Child Development Perspectives, 4(1), 10–18. https://doi.org/10.1111/j.1750-8606.2009.00110.x
Winzer R. Vaez M. Lindberg L. Sorjonen K. (2021). Exploring associations between subjective well-being and personality over a time span of 15–18 months: A cohort study of adolescents in Sweden. BMC Psychology, 9, Article 173. https://doi.org/10.1186/s40359-021-00673-9
Wirth R. J. Edwards M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12(1), 58–79. https://doi.org/10.1037/1082-989x.12.1.58
World Health Organization. (1998). Wellbeing measures in primary health care/the DepCare Project: Report on a WHO meeting: Stockholm, Sweden, 12–13 February 1998. WHO Regional Office for Europe. https://iris.who.int/handle/10665/349766