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Learn to perform confirmatory factor analysis in Stata with data from the General Social Survey (2016)

Author: Catherine Zimmer
Publisher: London : SAGE Publications, Ltd., 2019.
Edition/Format:   eBook : Document : English
Summary:
This example introduces readers to confirmatory factor analysis (CFA). CFA is used to model how well latent variables are related to multiple observed variables that serve as measurements of the latent variables. In contrast to exploratory factor analysis (EFA), the links of particular latent variables to particular observed variables are specified in advance and tested statistically, not derived from the data. CFA  Read more...
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Genre/Form: Handbooks and manuals
Handbooks, manuals, etc
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Catherine Zimmer
ISBN: 9781529700091 1529700094
OCLC Number: 1100461728
Description: 1 online resource : illustrations
Responsibility: Catherine Zimmer.

Abstract:

This example introduces readers to confirmatory factor analysis (CFA). CFA is used to model how well latent variables are related to multiple observed variables that serve as measurements of the latent variables. In contrast to exploratory factor analysis (EFA), the links of particular latent variables to particular observed variables are specified in advance and tested statistically, not derived from the data. CFA is a type of structural equation model (SEM) used for measurement of concepts. These measurement models can be components of larger SEM models with latent variables being predictors of other variables or outcomes. This example introduces readers to the basic theory and assumptions associated with CFA, estimators and the interpretation of estimates, the associated hypothesis tests, results production, and reporting. The dataset file is accompanied by a Teaching Guide, a Student Guide, and a How-to Guide for Stata.

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