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Factor Analysis (Statistical Associates "Blue Book" Series Book 15)By G. David Garson
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FACTOR ANALYSIS
Factor analysis is used to uncover the latent structure (dimensions) of a set of variables. It reduces attribute space from a larger number of variables to a smaller number of factors and as such is a "non-dependent" procedure (that is, it does not assume a dependent variable is specified). Illustrated with numerous how-to figures, output tables, and examples for SPSS, SAS, and Stata.
New in the 2013 edition: 50% additional coverage; now covers categorical (nonlinear) categorical principal components analysis as well as principal components and principal factor analysis for continuous variables; thorough restructuring for better readability; new coverage for Stata as well as SPSS and SAS; coverage of factor loading plots; expanded discussion of rotation methods; hyperlinking of FAQs to key words in text; numerous other updates.
FACTOR ANALYSIS
Table of Contents
Overview8
Data10
Key Concepts and Terms10
Exploratory factor analysis (EFA)10
Exploratory vs. confirmatory factor analysis (CFA)10
Factor Analytic Data Modes11
R-mode factor analysis11
Q-mode factor analysis11
Other rarer modes of factor analysis12
Types of factor extraction13
Principal components analysis (PCA)13
Principal factor analysis (PFA)14
PCA and PFA compared14
Other Extraction Methods16
Types of factor rotation17
Rotation methods17
No rotation18
Varimax rotation19
Quartimax rotation20
Equamax rotation21
Direct oblimin (oblique) rotation22
Promax rotation23
Other rotation methods24
Summary24
Factor analysis in SPSS24
Data setup24
The "Factor" dialog24
Descriptives and Options25
Extraction26
Rotation27
Factor Scores28
Statistical output in SPSS29
Factor loadings29
Plot of factor loadings (factor space plot)31
Factor, component, pattern, and structure matrices33
Communality34
Uniqueness36
Eigenvalues36
Extraction sums of squared loadings37
Trace37
Factor scores38
Bartlett scores39
Saving factor scores40
Criteria for number of factors to model40
Parallel analysis42
Other Criteria43
Using reproduced correlation residuals to validate the choice of number of factors43
Summary44
Factor analysis in SAS44
SAS interface44
SAS syntax45
Rotation methods in SAS47
Statistical output in SAS48
Factor loadings in SAS output48
SAS output for communalities48
SAS output for eigenvalues48
SAS scree plot output49
SAS factor loadings plots51
Factor analysis in Stata52
Stata interface52
Importing data into Stata53
Stata syntax55
Statistical output in Stata58
Stata output for eigenvalues58
Factor loadings in Stata output59
Stata output for communalities59
Stata scree plot output59
Stata loading plots60
Categorical principal components analysis (CATPCA)62
Overview62
SPSS categorical principal components analysis63
Data considerations63
CATPCA user interface in SPSS63
The “Optimal Scaling” dialog63
The main CATPCA dialog64
The “Discretize” button dialog66
The “Missing” button dialog67
The “Options” button dialog68
The “Output” button dialog70
The “Save” button dialog72
The “Object” button dialog72
The “Category” button dialog73
The “Loading” button dialog75
SPSS CATPCA statistical output75
The “Model Summary” table75
The “Component Loadings” table77
The “Component Loadings” plot79
The “Variance Accounted For” table80
The “Object Points Labeled by Casenumbers” plot81
The “Object Scores” table82
The “Biplot Component Loadings and Objects” plot83
The “Quantifications” table84
The “Category Points” plot85
The “Projected Centroids” table and plot87
SAS categorical principal components analysis89
Overview89
SAS syntax89
The PROC PRINQUAL procedure90
SAS PROC PRINQUAL output92
Principal components analysis of transformed data94
Stata categorical principal components analysis97
Overview97
Example98
The polychoric correlation matrix98
The “Principal component analysis” table99
The “Scoring Coefficients” table100
and 31 more pages of topics
Pagecount: 131
- Sales Rank: #198218 in eBooks
- Published on: 2013-01-19
- Released on: 2013-01-19
- Format: Kindle eBook
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