Course Text. This course is divided into three Sessions, each of which contains Lessons that draw on material from the Course Text. The course text can be accessed as a .pdf in the Materials section.
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Overview. Latent class (LC) modeling is a technique for analyzing data with the goal of identifying “latent classes,” or segments of the data that characterize similar (homogeneous) cases (e.g. customer segments, persons with similar medical diagnoses, types of survey respondents, etc.) based on categorical or continuous response variables or a combination of these. In this course we introduce LC as a probability model and describe various applications using the demo version of Latent GOLD® 6.0. Initial lessons focus on model fitting strategies and the interpretation of output. Subsequent lessons consider advanced topics such as model identification, LC models for longitudinal data, data sparseness, and use of bivariate residuals.
Course Schedule. The materials for Session 1 will be made available on Friday, October 8, and material for Sessions 2 and 3 will be posted on October 15 and 22 respectively. Materials for each Section are organized into Lessons, which contain additional text and videos, as well as optional Exercises. Answers for the Exercises are posted at the end of each week.
Discussion Board. A discussion board is used to post questions for the instructor, or to comment on other postings. The discussion board can be accessed for each topic.
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