Using Rasch measurement theory to assess the psychometric properties of a depressive symptoms scale in Norwegian adolescents
Tóm tắt
Scales measuring depressive symptoms in adolescents and adults are widely used for epidemiological purposes. The purpose of this study is to use Rasch measurement theory to examine the psychometric properties of a six-item scale intended to measure depressive symptoms in Norwegian adolescents. The study is based on cross-sectional data from Ungdata, a survey conducted by the Norwegian Social Research Institute in cooperation with Regional Centres for Drug Rehabilitation in 2017. The target group comprised 13- to 19-years olds in Norway. Six items with four response categories, intended to measure depressive symptoms, were analysed. The analysis focused on invariance, including differential item functioning across gender and school levels. In addition, targeting, possible multidimensionality, response dependency, and the categorisation of the items were analysed. The scale measuring depressive symptoms shows good reliability and, on the whole, the items work well. However, one item, ‘had sleep problems’, clearly misfit and another, ‘worried too much about things’, works differently for males and females. The scale has the potential to measure depressive symptoms in adolescents though there is room for improvement. To further improve the scale, the item concerning sleep problems should be rephrased.
Tài liệu tham khảo
Patel V, Flisher AJ, Hetrick S, McGorry P. Mental health of young people: a global public-health challenge. Lancet. 2007;369:1302–13.
Kieling C, Baker-Henningham H, Belfer M, et al. Child and adolescent mental health worldwide: evidence for action. Lancet. 2011;378:1515–25.
Bakken A. Ungdata 2017. Nasjonale resultater [Ungdata 2017. National results]. Oslo: NOVA; 2017.
Fergusson DM, Woodward LJ. Mental health, educational, and social role outcomes of adolescents with depression. Arch Gen Psychiatry. 2002;59:225–31.
Goodman A, Joyce R, Smith JP. The long shadow cast by childhood physical and mental problems on adult life. Proc Natl Acad Sci U S A. 2011;108:6032–7.
McCarty CA, Mason WA, Kosterman R, McGorry P. Adolescent school failure predicts later depression among girls. J Adolesc Health. 2008;43:180–7.
Merikangas KR, He J-P, Burstein M, et al. Lifetime prevalence of mental disorders in US adolescents: results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry. 2010;49:980–9.
Wichstrøm L. The emerge of gender difference in depressed mood during adolecence: the role of intensified gender socialization. Dev Psychol. 1999;35:232–45.
Andrich D. Rasch models for measurement. Newbury Park: Sage Publications; 1988.
Beck AT, Steer RA. Internal consistencies of the original and revised Beck depression inventory. J Clin Psychol. 1984;40:1365–7.
Kandel DB, Davies M. Epidemiology of depressive mood in adolescents: an empirical study. Arch Gen Psychiatry. 1982;39:1205–12.
Derogatis LR, Lipman RS, Richels K, Uhlenhuth EH, Covi L. The Hopkins Symptoms Checklist (HSCL) -a self-report symptom inventory. Behav Sci. 1974;19:1–15.
Abebe DS, Frøyland LR, Bakken A, et al. Municipal-level differences in depressive symptoms among adolescents in Norway: results from the cross-national Ungdata study. Scand J Soc Med. 2016;44:47–54.
Granrud MD, Steffenak AKM, Theander K. Gender differences in symptoms of depression among adolescents in Eastern Norway: results from a cross-sectional study. Scand J Public Health. 2017;47:157–65.
von Soest T, Wichstrøm L. Secular trends in depressive symptoms among Norwegian adolescents from 1992 to 2010. J Abnorm Child Psychol. 2014;42:403–15.
Rasch G. Probabilistic models for some intelligence and attainment tests. (Expanded ed.). Chichago: University of Chicago Press; 1980.
Duncan OD. Rasch measurement: further examples and discussion. In: Turner CF, Martin E, editors. Surveying subjective phenomena. New York: Russel Sage foundation; 1984. p. 367–401.
Tennant A, Conaghan PG. The Rasch measurement model in rheumatology: what is it and why use it? When should it be applied, and what should one look for in a Rasch paper? Arthritis Care Res. 2007;57:1358–62.
Stenner AJ. Specific objectivity - local and general. Rasch Meas Trans. 1994;8(3):374.
Perline R, Wright BD, Wainer H. The Rasch model as additive conjoint measurement. Appl Psychol Meas. 1979;3(2):237–55.
Andrich D. Distinctions between assumptions and requirements in measurement in the social sciences. In: Keats JA, Taft R, Heath RA, Lovibond SH, editors. Mathematical and theoretical systems. North-Holland: Elsevier Science Publishers BV; 1989. p. 7–16.
Andersen EB. Sufficient statistics and latent trait models. Psychometrika. 1977;42(1):69–81.
Andrich D, Marais I. A course in Rasch measurement theory: measuring in the educational, social and health sciences. Singapore: Springer; 2019.
Hagquist C, Bruce M, Gustavsson JP. Using the Rasch model in nursing research: an introduction and illustrative example. Int J Nurs Stud. 2009;46:380–93.
Hagquist C. Explaining differential item functioning focusing on the crucial role of external information–an example from the measurement of adolescent mental health. BMC Med Res Methodol. 2019;19(1):185.
Tennant A, McKenna SP, Hagell P. Application of Rasch analysis in the development and application of quality of life instruments. Value Health. 2004;7:S22–6.
Salzberger T, Sinkovics RR. Reconsidering the problem of data equivalence in international marketing research: contrasting approaches based on CFA and the Rasch model for measurement. Int Mark Rev. 2006;23:390–417.
Kleppang AL, Hagquist C. The psychometric properties of the Hopkins Symptom Checklist-10: a Rasch analysis based on adolescent data from Norway. Fam Pract. 2016;33:740–5.
NOVA. Ungdata. 2019 [11.02.2019]; Available from: http://www.ungdata.no/English.
Masters GN. A Rasch model for partial credit scoring. Psychometrika. 1982;47:149–74.
Hagell P. Testing rating scale unidimensionality using the principal component analysis (PCA)/t-test protocol with the Rasch model: the primacy of theory over statistics. Open J Stat. 2014;4:456–65.
Smith EV. Understanding Rasch measurement: detecting and evaluating the impact of multidimensionality using item fit statistics and principal component analysis of residuals. J Appl Meas. 2002;3:205–31.
Andrich D, Humphry SM, Marais I. Quantifying local, response dependence between two polytomous items using the Rasch model. Appl Psychol Meas. 2012;36:309–24.
Lantz B. The large sample size fallacy. Scand J Caring Sci. 2013;27:487–92.
Bergh D. Chi-squared test of fit and sample size-a comparison between a random sample approach and a chi-square value adjustment method. J Appl Meas. 2015;16:204–17.
Hagquist C, Andrich D. Recent advances in analysis of differential item functioning in health research using the Rasch model. Health Qual Life Outcomes. 2017;15:1–8.
Andrich D, Hagquist C. Real and artificial differential item functioning in polytomous items. Educ Psychol Meas. 2014;75(2):185–207.
Andrich D, Sheridan B, Luo G. RUMM2030: a windows program for the Rasch unidimensional measurement model [computer software]. Perth: RUMM Laboratory; 2013.
Cavanagh A, Caputi P, Wilson CJ, Kavanagh DJ. Gender differences in self-reported depression and co-occurring anxiety and stress in a vulnerable community population. Aust Psychol. 2016;51(6):411–21.
Masters GN. Item discrimination: when more is worse. J Educ Meas. 1988;25(1):15–29.
Gradisar M, Gardner G, Dohnt H. Recent worldwide sleep patterns and problems during adolescence: a review and meta-analysis of age, region, and sleep. Sleep Med. 2011;12(2):110–8.
Robichaud M, Dugas MJ, Conway M. Gender differences in worry and associated cognitive-behavioral variables. J Anxiety Disord. 2003;17:501–16.