This chapter focuses on Exploratory Structural Equation Modeling (ESEM), incorporating bifactor-ESEM, which represent an overarching data analytic framework in which classical exploratory factor analysis methods have been integrated into the confirmatory factor analyses (CFA)/structural equation modeling framework. It discusses limitations related to the use of CFA, as well as some myths that maintain the use of this potentially problematic approach. The chapter introduces ESEM and the accompanying notion of psychometric multidimensionality. It discusses the alternative methods that can be used to account for construct-irrelevant and construct-relevant forms of psychometric multidimensionality. The chapter proposes a sequence for the estimation of ESEM and bifactor-ESEM models and guidelines related to sample size determination and power estimation, and to the choice of the estimator and rotation procedure. It presents some limitations related to current implementations of ESEM and bifactor-ESEM and preliminary solutions to some of these limitations.
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