You can preview and download the dataset from this tab. The dataset is available in multiple file formats, compatible with most common software packages. You can also view and download the Codebook, which provides information on the structure, contents, and layout of the dataset.
This example introduces readers to confirmatory factor analysis (CFA). CFA is used to model how well latent variables are related to multiple observed variables that serve as measurements of the latent variables. In contrast to exploratory factor analysis (EFA), the links of particular latent variables to particular observed variables are specified in advance and tested statistically, not derived from the data. CFA is a type of structural equation model (SEM) used for measurement of concepts. These measurement models can be components of larger SEM models with latent variables being predictors of other variables or outcomes. This example introduces readers to the basic theory and assumptions associated with CFA, estimators and the interpretation of estimates, the associated hypothesis tests, results production, and reporting. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata.