In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social science research. It is used to test whether measures of a construct are consistent with a researcher's … See more

Although numerous algorithms have been used to estimate CFA models, maximum likelihood (ML) remains the primary estimation … See more

Structural equation modeling software is typically used for performing confirmatory factor analysis. LISREL, EQS, AMOS, Mplus and LAVAAN package in R are popular software … See more

To estimate the parameters of a model, the model must be properly identified. That is, the number of estimated (unknown) parameters (q) must be less than or equal to the number of unique variances and covariances among the measured variables; p(p + … See more

In confirmatory factor analysis, researchers are typically interested in studying the degree to which responses on a p x 1 vector of observable random variables can be used to assign a value to one or more unobserved variable(s) $${\displaystyle \xi }$$ See more

Both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are employed to understand shared variance of measured … See more

In CFA, several statistical tests are used to determine how well the model fits to the data. Note that a good fit between the model and the data does not mean that the model is “correct”, or even that it explains a large proportion of the covariance. A … See more