Evaluating measurement models in clinical research: covariance structure analysis of latent variable models of self-conception.
Indirect measures of psychological constructs are vital to clinical research. On occasion, however, the meaning of indirect measures of psychological constructs is obfuscated by statistical procedures that do not account for the complex relations between items and latent variables and among latent variables. Covariance structure analysis (CSA) is a statistical procedure for testing hypotheses about the relations among items that indirectly measure a psychological construct and relations among psychological constructs. This article introduces clinical researchers to the strengths and limitations of CSA as a statistical procedure for conceiving and testing structural hypotheses that are not tested adequately with other statistical procedures. The article is organized around two empirical examples that illustrate the use of CSA for evaluating measurement models with correlated error terms, higher-order factors, and measured and latent variables.
Hoyle
R H
RH
Department of Psychology, University of Kentucky, Lexington 40506-0044.
D000704:Analysis of Variance; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D010555:Personality Inventory; D011613:Psychotherapy; D012044:Regression Analysis; D012107:Research Design; D012649:Self Concept; D012984:Software