Missing data is unavoidable in most empirical work. This can be a problem for any statistical analysis that needs data to be complete. Structural equation modeling and confirmatory factor analysis are such methods that rely on a complete dataset. The following post will give an overview on the background of missing data analysis, how the missingness can be investigated, how the R-package MICE for multiple imputation is applied and how imputed data can be given to the lavaan-package for confirmatory factor a
If you are in a hurry and already know the background of multiple imputation, jump to: How to use multiple imputation with lavaan
What kinds of missing data are there? There are two types of missingness: Unit nonresponse concerns cases in the sample, that didn´t respond to the survey at all, or – more generally spoken – the failure to obtain measurements for a sampled unit. Item nonrespons e occurs, when a person leaves out particular items in the survey, or – more generally spoken – particular measurements of a sampled unit are missing. Here, we will focus on item nonresponse.