title: CFA with binary variables in small samples: a comparison of two methods.
abstract: Asymptotically optimal correlation structure methods with binary data can break down in small samples. A new correlation structure methodology based on a recently developed odds-ratio (OR) approximation to the tetrachoric correlation coefficient is proposed as an alternative to the LPB approach proposed by Lee et al. (1995). Unweighted least squares (ULS) estimation with robust standard errors and generalized least squares (GLS) estimation methods were compared. Confidence intervals and tests for individual model parameters exhibited the best performance using the OR approach with ULS estimation. The goodness-of-fit chi-square test exhibited the best Type I error control using the LPB approach with ULS estimation.
authors.affiliation: Department of Psychology, University of British Columbia Vancouver, BC, Canada.
authors.forename: Victoria
authors.initials: V
authors.lastname: Savalei
authors.affiliation: Department of Psychology, University of California, Santa Cruz Santa Cruz, CA, USA.
authors.forename: Douglas G
authors.initials: DG
authors.lastname: Bonett
authors.affiliation: Department of Psychology, University of California, Los Angeles Los Angeles, CA, USA.
authors.forename: Peter M
authors.initials: PM
authors.lastname: Bentler
keywords: correlation structure models; dichotomous variables; factor analysis; odds ratio; tetrachoric correlation