Abstract
Confirmatory factor analysis (CFA) is a statistical strategy specifically designed to identify and explore hypothetical constructs as manifest in fallible indicators. The allure of CFA over other approaches to the study of hypothetical constructs is the capacity for testing detailed hypotheses in a deductive mode. Moreover, CFA models can be incorporated directly into general structural equation modeling (SEM) that includes directional relations among hypothetical constructs. It is noted that the central concern of CFA is modeling factors, sometimes referred to as latent variables. Factors are influences that are not directly measured, but account for commonality among a set of measurements. Recent developments in technical aspects of estimation and testing coupled with widely accessible and user-friendly software have rendered CFA more popular and appealing than ever. However, with the increased accessibility of CFA comes a responsibility to understand the conditions under which CFA is appropriately applied and the factors relevant to interpretation of CFA results.
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