Confirmatory factor analysis (CFA) is a quantitative data analysis method that belongs to the family of structural equation modeling (SEM) techniques. CFA allows for the assessment of fit between observed data and an a priori conceptualized, theoretically grounded model that specifies the hypothesized causal relations between latent factors and their observed indicator (manifest) variables. Because population-level equivalence between data and model cannot be shown with sample data, CFA should be viewed as a mainly disconfirmatory technique. That is, CFA facilitates the statistical rejection – or, at best, a tentative retention – of a specific theory regarding the factor(s) responsible for the observed relations in the data. If, on the other hand, the investigator’s intentions are a mostly ungrounded exploration of relations suggested by the data, classical exploratory factor analysis is the more appropriate approach. In this article, typical steps in a CFA are introduced theoretically and via example: from model specification and identification, to parameter estimation, data-model fit assessment, and potential model modification. Didactic references are provided for a more in-depth study of CFA and SEM techniques in the social and behavioral sciences.