Principal Components
						Analysis and Exploratory Factor Analysis

Lawrence S. Meyers; Glenn Gamst; A.J. Guarino

doi:10.4135/9781071802687

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Using a conceptual, non-mathematical approach, the updated Third Edition provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter. Authors Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino integrate innovative multicultural topics in examples throughout the book, which include both conceptual and practical coverage of: statistical techniques of data screening; multiple regression; multilevel modeling; exploratory factor analysis; discriminant analysis; structural equation modeling; structural equation modeling invariance; survival analysis; multidimensional scaling; and cluster analysis.

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Principal Components Analysis and Exploratory Factor Analysis

By:
Lawrence S. Meyers
,
Glenn Gamst
&
A.J. Guarino
In:Applied Multivariate Research: Design and Interpretation
Chapter DOI:https://doi.org/10.4135/9781071802687.n17
Subject:Sociology, Criminology and Criminal Justice, Education, Psychology, Health, Anthropology, Social Policy and Public Policy, Social Work, Political Science and International Relations

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Principal Components Analysis and Exploratory Factor Analysis

10A.1 Orientation and Terminology

The informal term data crunching generally refers to a process in which a considerable quantity of data is reduced down to a more manageable or consolidated whole. Principal components analysis and exploratory factor analysis are the quintessential data-crunching procedures. Their general purpose is to identify a relatively small number of themes, dimensions, components, or factors underlying a relatively large set of variables. They do this by distinguishing sets of variables that have more in common with each other than with the other variables in the analysis. What the subsets of variables have in common are the underlying components or factors.

Both principal components analysis and factor analysis are exploratory procedures. In this context, that means we are not testing a null hypothesis about the population value of ...

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Summary

Contents

Subject index

Principal Components Analysis and Exploratory Factor Analysis

Principal Components Analysis and Exploratory Factor Analysis

10A.1 Orientation and Terminology

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