Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research Hardcover – March 21, 2003
Author: Visit Amazon's Marjorie A. Pett Page | Language: English | ISBN: 076191949X | Format: PDF, EPUB
Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research – March 21, 2003
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About the Author
Dr. Marge Pett is a Research Professor in the College of Nursing, having been on the faculty since 1980. She is the author of numerous articles and presentations related both to her previously federally funded research on family interaction in married and divorced families, her current interests in developing effective healthy lifestyle interventions for persons with intellectual disabilities and their families, development of patient-ofocused pain care measures, as well as her interests in statistics. Her two textbooks, Nonparametric Statistics for Health Care Research and Making Sense of Factor Analysis, have received much praise from the health care community. Marge has a strong commitment to facilitationg the practical application of statistics in the social, behavioral, and biological sciences, especially among practitioners in health care settings. She has taught various statistics courses to doctoral students from a variety of disciplines, both at the beginning and advanced levels. Marge has been married for more than 40 years to the same person, has two adult children and 2-3/4 grandchildren. When she is not teaching or doing research, Marge can be found on the tennis courts, various and sundry golf courses, studying Italian or wandering the streets of the wonderful cities of Italia.
Dr John Sullivan has been a professor of management for over 26 years at San Francisco State University. His specialty is HR strategy and designing world class HR systems and tools for Fortune 200 firms. He has worked with over 200 different businesses and organizations in more than 30 countries around the world as a speaker or advisor. He has written a weekly column for ERE for over eleven years. Overall, he has written ten books, dozens of white papers and over 700 articles. He was the chief talent officer for Agilent (the 40,000+ employee HP spin off). He has appeared on the CBS and ABC national nightly news, CNN and in various publications including Fortune, the Economist, CIO, BusinessWeek, the WSJ, the Washington Post, Money, Time and every major HR magazine. Fast company called him the Michael Jordan of hiring. He was listed among the 40 most influential people in HR. Tom Peters cites and utilizes his work in his latest book Re-Imagine.
Dr John Sullivan has been a professor of management for over 26 years at San Francisco State University. His specialty is HR strategy and designing world class HR systems and tools for Fortune 200 firms. He has worked with over 200 different businesses and organizations in more than 30 countries around the world as a speaker or advisor. He has written a weekly column for ERE for over eleven years. Overall, he has written ten books, dozens of white papers and over 700 articles. He was the chief talent officer for Agilent (the 40,000+ employee HP spin off). He has appeared on the CBS and ABC national nightly news, CNN and in various publications including Fortune, the Economist, CIO, BusinessWeek, the WSJ, the Washington Post, Money, Time and every major HR magazine. Fast company called him the Michael Jordan of hiring. He was listed among the 40 most influential people in HR. Tom Peters cites and utilizes his work in his latest book Re-Imagine.
Direct download links available for Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research – March 21, 2003
- Hardcover: 368 pages
- Publisher: SAGE Publications, Inc; 1 edition (March 21, 2003)
- Language: English
- ISBN-10: 076191949X
- ISBN-13: 978-0761919490
- Product Dimensions: 9.1 x 6.3 x 1.2 inches
- Shipping Weight: 1.4 pounds (View shipping rates and policies)
- Amazon Best Sellers Rank: #2,693,073 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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