Are fit indices really fit to estimate the number of factors with categorical variables? Some cautionary findings via Monte Carlo simulation

Abstract

An early step in the process of construct validation consists of establishing the fit of an unrestricted “exploratory” factorial model for a prespecified number of common factors. For this initial unrestricted model, researchers have often recommended and used fit indices to estimate the number of factors to retain. Despite the logical appeal of this approach, little is known about the actual accuracy of fit indices in the estimation of data dimensionality. The present study aimed to reduce this gap by systematically evaluating the performance of 4 commonly used fit indices—the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR)—in the estimation of the number of factors with categorical variables, and comparing it with what is arguably the current golden rule, Horn’s (1965) parallel analysis. The results indicate that the CFI and TLI provide nearly identical estimations and are the most accurate fit indices, followed at a step below by the RMSEA, and then by the SRMR, which gives notably poor dimensionality estimates. Difficulties in establishing optimal cutoff values for the fit indices and the general superiority of parallel analysis, however, suggest that applied researchers are better served by complementing their theoretical considerations regarding dimensionality with the estimates provided by the latter method. (APA PsycInfo Database Record (c) 2016 APA, all rights reserved)