Making Sense of Factor Analysis

Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research [Paperback]

Author: Marjorie A. Pett | Language: English | ISBN: 0761919503 | Format: PDF, EPUB

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Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research
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Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research presents a straightforward explanation of the complex statistical procedures involved in factor analysis. Authors Marjorie A. Pett, Nancy M. Lackey, and John J. Sullivan provide a step-by-step approach to analyzing data using statistical computer packages like SPSS and SAS. Emphasizing the interrelationship between factor analysis and test construction, the authors examine numerous practical and theoretical decisions that must be made to efficiently run and accurately interpret the outcomes of these sophisticated computer programs.

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• Paperback: 368 pages
• Publisher: SAGE Publications, Inc; 1 edition (March 21, 2003)
• Language: English
• ISBN-10: 0761919503
• ISBN-13: 978-0761919506
• Product Dimensions: 9 x 6.1 x 0.8 inches
• Shipping Weight: 1 pounds (View shipping rates and policies)
• Amazon Best Sellers Rank: #178,312 in Books (See Top 100 in Books)

Many of us who have used factor analysis had only a vague notion of what we were doing, namely trying to reduce a large number of items into a smaller, less unwieldy, more readily interpretable set of variables. With user-friendly software such as SPSS, the mechanics -- entering items, extracting factors, rotation of factors, saving factor scores if needed, and calculating reliability coefficients -- are sufficiently obvious to permit rough and ready, sometimes quite useful factor solutions that provide insights that otherwise would not have been available.

Without studying factor analysis as such, however, such quick and dirty applications often yield misleading results, something that anonymous reviewers of submitted manuscripts will be only too happy to acerbically explain. In my own work, I stared off routinely using principal components analysis, the SPSS default option for factor analysis, but had no notion that principal components analysis and factor analysis in its various forms are mathematically distinct. Principal components uses all three sources of variance -- shared, random, and error variance in formulating components, while factor analysis uses only shared variance. One common outcome is that principal components will typically yield a misleadingly clear-cut solution, while factor analysis rightly yields a solution that requires more interpretative effort.

Furthermore, when trying to reduce a comparatively large number of items to a small set of themes or variables, we get our most informative results when the analysis is limited to shared variance. Thus, while principal components has its uses, one of the many forms of factor analysis, say alpha factoring, is usually better suited to the task at hand.

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