Transcription of Factor Analysis - Harvard University
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Factor Analysis - Harvard University
Factor Analysis Qian-Li Xue Biostatistics Program Harvard Catalyst | The Harvard Clinical & Translational Science Center Short course, October 27, 2016. 1. Well-used latent variable models Latent Observed variable scale variable scale Continuous Discrete Continuous Factor Discrete FA. Analysis IRT (item response). LISREL. Discrete Latent profile Latent class Growth mixture Analysis , regression General software: MPlus, Latent Gold, WinBugs (Bayesian), NLMIXED (SAS). Objectives What is Factor Analysis ? What do we need Factor Analysis for? What are the modeling assumptions? How to specify, fit, and interpret Factor models? What is the difference between exploratory and confirmatory Factor Analysis ? What is and how to assess model identifiability? 3. What is Factor Analysis Factor Analysis is a theory driven statistical data reduction technique used to explain covariance among observed random variables in terms of fewer unobserved random variables named factors 4.
" Exception: adjust R by giving greater weights to correlations with smaller unique variance, i.e. 1- h2 " Advantage: availability of a large sample χ2 significant test for goodness-of-fit (but tends to select more factors for large n!) 19
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