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.
Least-squares method (LS) (e.g. principal axis factoring with iterated communalities) " Goal: minimize the sum of squared differences between observed and estimated corr. matrices " Fitting steps: a) Obtain initial estimates of communalities (h2) e.g. squared correlation between a variable and the remaining variables
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