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Confirmatory Factor Analysis (CFA)
Confirmatory factor analysis is a procedure in statistics that is used by analysts to examine how well the number of constructs is represented. While CFA and exploratory factor analysis are similar techniques, EFA explores data and informs us about the number of factors needed to represent our data. Also, in the EFA method, all the variables that have been measured are associated with the latent variables. Confirmatory factor analysis on the other hand allows you to specify the number of factors that are needed for your data.
Path analysis is a type of statistical regression analysis that is used to analyze causal models. This technique examines if there is an association between a single dependent variable and two or multiple independent variables. Path analysis supports the evaluation of both the significance and magnitude of causal relationships among variables. Path analysis is unique because it forces the researcher to specify existing associations among all of our independent variables.
Multiple imputation is a statistical approach that helps researchers deal with the problem of missing or unavailable data. This method creates a variety of different plausible imputed data and combines all the obtained results appropriately for each. The first step is to curate several copies of your dataset. These copies should have the missing values replaced by imputed values. The copies are then sampled from their estimated distribution while regarding the data that has been observed. For this reason, we can say that the multiple imputation approach is founded on the Bayesian approach.