Nearly all contemporary psychometric models make use of latent variables, of one form or another. An important consideration in using these models is the performance of the items in potentially heterogeneous populations of respondents. Many claims about the validity of our measures, and the inferences we make from them, are based on the premise that individuals interpret and respond to sets of items in a consistent manner such that the measurement model parameters are equivalently applicable to all people irrespective of any differences among them in our target population. The purpose of this workshop is to introduce latent class analysis in the context of the analysis of test data. The use of latent variable mixture models will be introduced and demonstrated in the context of examining the extent to which a sample is homogeneous with respect to a specified unidimensional model for categorical data and identify potential sources of sample heterogeneity. In addition to test level analyses, we will also introduce a new set of latent class methods recently introduced by the author to investigate heterogeneity at the item level – a new latent class item bias technique. The workshop is structured to focus on the fundamentals of the methods and demonstrate the techniques with real test data. Prior basic knowledge of structural equation modeling and confirmatory factor analysis, in particular, will be assumed.