Minimum Sample Size Requirements for a Validation Study of The Schizophrenia Quality of Life Scale-Revision 4 SQLS-R4

Abstract

Purpose: The Schizophrenia Quality of Life Scale-Revision 4 SQLS-R4 is a widely used self-report quality of life measure used in a broad range of clinical contexts, from primary research to clinical trials. International use of the measure has led to translated versions validated for local context. Most translation and validation studies of the SQLS-R4 have been conducted with modest N, at the threshold of acceptability of even the most liberal recommendations for validation studies. Given the comparatively large number of items in the SQLS-R4 N=33 , low N studies could potentially be underpowered limiting validity and reliability. Using sample sizes from published studies as a baseline, the current investigation sought to determine a minimum sample size for an SQLS-R4 translation/validation study. Methods: A model specification based on the two-factor structure of the SQLS-R4 was constructed to calculate an acceptable model fit based on the sample size used in most SQLS-R4 translation/validation studies N=100 . A series of Monte Carlo simulations was then conducted to determine the sample size required to offer a good fit to data for an adequately powered study. Results: The series of simulations conducted suggests that a minimum sample size for an adequately powered validation/translation study of the SQLS-R4 to provide a good fit to data is N=160.Conclusion: Sample size determination of SQLS-R4 validation/translation studies should be informed by the intrinsic measurement characteristics of the measure to ensure an adequately powered study

Keywords

• Schizophrenia Quality of Life-Revision 4 SQLS-R4,simulation,sample size,questionnaire

References

1. Basu D. Quality-of-life issues in mental health care: Past, present, and future. Ger J Psychiatry 2004;7:35–43.
2. Lin C-Y, Luh W-M, Cheng C-P, Yang A-L, Su C-T, Ma H-I. Measurement equivalence across child self-reports and parent-proxy reports in the Chinese version of the pediatric quality of life inventory version 4.0. Child Psychiatry Hum Dev 2013;44:583–590. [CrossRef]
3. Su C-T, Yang A-L, Lin C-Y. The Construct of the Schizophrenia Quality of Life Scale Revision 4 for the Population of Taiwan. Occup Ther Int 2017;2017:1–11. [CrossRef]
4. Adelufosi A, Ogunwale A, Abayomi O, Mosanya J. Socio-demographic and clinical correlates of subjective quality of life among Nigerian outpatients with schizophrenia. Psychiatry Res 2013;209:320–325. [CrossRef]
5. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. Washington, DC: American Psychiatric Publishing; 2013.
6. Martin CR, Allan R. Factor structure of the schizophrenia quality of life scale revision 4(SQLS-R4). Psychol Health Med 2007;12:126–134. [CrossRef]
7. WHOQoL Group. Study protocol for the World Health Organization project to develop a Quality of Life assessment instrument (WHOQOL). Qual Life Res 1993;2:153–159. [CrossRef]
8. Kuo P-J, Chen-Sea M-J, Lu R-B, et al. Validation of the Chinese version of the Schizophrenia Quality of Life Scale Revision 4 (SQLS-R4) in Taiwanese patients with schizophrenia. Qual Life Res 2007;16:1533– 1538. [CrossRef]

9. Cutler AJ, Durgam S, Wang Y, et al. Evaluation of the long-term safety and tolerability of cariprazine in patients with schizophrenia: results from a 1-year open-label study. CNS Spectrums 2017;23:39–50. [CrossRef]
10. Wilkinson G, Hesdon B, Wild D, et al. Self-report quality of life measure for people with schizophrenia: the SQLS. Br J Psychiatry 2000;177:42–46. [CrossRef]
11. Oxford Outcomes Ltd. The Revised Schizophrenia Quality of Life Questionnaire (SQLS-R4): User Manual for the SQLS-R4. Oxford Outcomes: Cassington, UK; 2004.
12. Kaneda Y, Imakura A, Fujii A, Ohmori T. Schizophrenia Quality of Life Scale: validation of the Japanese version. Psychiatry Res 2002;113:107–113. [CrossRef]
13. Byrne BM. Structural Equation Modeling with AMOS. Basic Concepts, Applications and Programming, 2nd ed. New York: Routledge/Taylor and Francis Group; 2010.
14. Cattell R, editor. The Scientific Use of Factor Analysis in Behavioral and Life Sciences. New York: Plenum Press; 1978. [CrossRef]
15. Everitt BS. Multivariate analysis: the need for data, and other problems. Br J Psychiatry 1975;126:237–240. [CrossRef]
16. de Winter JCF, Dodou D, Wieringa PA. Exploratory Factor Analysis With Small Sample Sizes. Multivariate Behavioral Research 2009;44:147–181. [CrossRef]
17. Jung S, Lee S. Exploratory factor analysis for small samples. Behav Res Methods 2011;43:701–709. [CrossRef]
18. Kline P. A Psychometrics Primer. London: Free Association Books; 2000.
19. Hutcheson G, Sofroniou N. The Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models. Thousand Oaks, CA. Sage; 1999. [CrossRef]
20. Gorsuch RL. Factor Analysis 2nd ed. Hillsdale, NJ: Erlbaum; 1983.
21. Crothers CE, Dorrian J. Determinants of Nurses’ Attitudes toward the Care of Patients with Alcohol Problems. ISRN Nurs 2011;2011:1–11. [CrossRef]
22. Muthén LK, Muthén BO. How to use a Monte Carlo study to decide on sample size and determine power. Struct Equ Modeling 2002;4:599–620. [CrossRef]
23. Cohen J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed. NJ. Lawrence Erlbaum Associates; 1988.
24. Martin CR, Thompson DR. Design and Analysis of Clinical Nursing Research Studies. London: Routledge; 2000.
25. Kuo P-J, Ma H-I, Kuo C-C, Huang W-C, Chung M-S. Factor analysis of the Schizophrenia Quality of Life Scale Revision 4 (SQLS-R4) Chinese version and related factors. Int J Psychiatry Clin Pract 2009;13:278– 284. [CrossRef]
26. Mundfrom DJ, Shaw DG, Ke TL. Minimum Sample Size Recommendations for Conducting Factor Analyses. International Journal of Testing IJT 2005;5:159–168. [CrossRef]
27. Beaujean AA. Sample size determination for regression models using Monte Carlo methods in R. Practical Assessment Research & Evaluation PARE 2014;19.
28. Bentler PM. Comparative fit indexes in structural models. Psychol Bull 1990;107:238–246. [CrossRef]
29. Hu LT, Bentler PM. Evaluating model fit. In: Hoyle RH, editor. Structural Equation Modelling: Concepts, Issues and Applications. Thousand Oaks, CA. Sage; 1995.
30. Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling 1999;6:1–55. [CrossRef]
31. Browne MW, Cudeck R. Alternate ways of assessing model fit. In: Bollen KA, Long JS, editors. Testing Structural Equation Models. California, USA: Sage Publications; 1993.
32. Schumacker RE, Lomax RG. A Beginner’s Guide to Structural Equation Modelling, 3rd ed. New York: Routledge/Taylor and Francis Group; 2010.
33. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2015.
34. Rosseel Y. Iavaan: An R package for structural equation modeling. Journal of Statistical Software 2002;48:1–36. [CrossRef]
35. Pornprasertmanit S, Miller P, Schoemann A. simsem: SIMulated Structural Equation Modeling (Computer Program) 2015.