Sage Journals: Discover world-class research

Abstract

Medical research studies utilize survey instruments consisting of responses to multiple items combined into one or more scales. These studies can benefit from methods for evaluating those scales. Such an approach is presented for evaluating exploratory and confirmatory factor analysis models with decisions about covariance structure, including the number of factors, the factor extraction procedure, the allocation of survey items to summated scales and the extent of inter-scale dependence, made objectively using a likelihood-based form of cross-validation. This approach is demonstrated through example analyses using baseline data for three survey instruments from a clinical trial involving adolescents with type 1 diabetes.

Get full access to this article

View all access options for this article.

References

Hatcher L. A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. SAS Institute, 1994 .

Sclove LS Application of model-selection criteria to some problems in multivariate analysis . Psychometrika 1987; 52: 333—43.

Spector PE Summated rating scale construction: an introduction. SAGE , 1992.

DeVellis RF Scale development: theory and applications. SAGE, 1991.

Browne MW , Cudeck R. Alternative ways of assessing model fit. In Bollen KA , Long JS eds. Testing structural equation models. SAGE, 1993: 136—62.

Burman P. A comparative study of ordinary cross-validation, ν -fold cross-validation and the repeated learning—testing methods. Biometrika 1989; 76: 503—14.

Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Mellish CS ed. Proceedings of the 14th international joint conference on arti ficial intelligence. Morgan Kaufman, 1995:1137-43.

Lee JC Tests and model selection for the general growth curve model. Biometrics 1981; 47: 147—59.

Smyth P. Model selection for probabilistic clustering using cross-validated likelihood . Statistics and Computing 2000; 10: 63—72.

10.

Krzanowski WJ Cross-validation in principal components analysis . Biometrics 1987; 43: 575—84.

11.

Azzalini A. , Capitanio A. Statistical applications of the multivariate skew normal distribution. Journal of the Royal Statistical Society, Series B 1999; 61: 589—602.

12.

Shapiro A. , Browne MW Analysis of covariance structures under elliptical distributions. Journal of the American Statistical Association 1987; 82: 1093—97.

13.

SAS Institute Inc. SAS/STAT 9.1 user's guide. SAS Institute, 2004.

14.

Grey M. , Davidson M. , Boland EA , Tamborlane WV Clinical and psychosocial factors associated with achievement of treatment goals in adolescents with diabetes mellitus. Journal of Adolescent Health 2001; 28: 377—85.

15.

Kovacs M. The children's depression inventory (CDI). Psychopharmacology Bulletin 1985; 21: 995—8.

16.

Olsen DH , McCubbin HI , Barnes H. , Larsen A. , Larsen A. , Muzen M. , Wilson M. Family inventories. Family Social Science, 1982.

17.

Ingersoll GM , Marrero DG A modified quality of life measure for youths: psychometric properties. The Diabetes Educator 1991; 17: 114—8.

18.

Johnson RA , Wichern DW Applied multivariate statistical analysis. Prentice-Hall , 1992.

19.

Ferketich S. , Muller M. Factor analysis revisited. Nursing Research 1990; 39: 59—62.

Factor analysis model evaluation through likelihood cross-validation

Abstract

Get full access to this article

References