Best Practices in Exploratory Factor Analysis (EFA) is a practitioner-oriented look at this popular and often-misunderstood statistical technique. We avoid formulas and matrix algebra, instead focusing on evidence-based best practices so you can focus on getting the most from your data.Each chapter reviews important concepts, uses real-world data to provide authentic examples of analyses, and provides guidance for interpreting the results of these analysis. Not only does this book clarify often-confusing issues like various extraction techniques, what rotation is really rotating, and how to use parallel analysis and MAP criteria to decide how many factors you have, but it also introduces replication statistics and bootstrap analysis so that you can better understand how precisely your data are helping you estimate population parameters. Bootstrap analysis also informs readers of your work as to the likelihood of replication, which can give you more credibility. At the end of each chapter, the author has recommendations as to how to enhance your mastery of the material, including access to the data sets used in the chapter through his web site. Other resources include syntax and macros for easily incorporating these progressive aspects of exploratory factor analysis into your practice. The web site will also include enrichment activities, answer keys to select exercises, and other resources. The fourth "best practices" book by the author, Best Practices in Exploratory Factor Analysis continues the tradition of clearly-written, accessible guides for those just learning quantitative methods or for those who have been researching for decades.NEW in August 2014! Chapters on factor scores, higher-order factor analysis, and reliability.
Chapters:
1 INTRODUCTION TO EXPLORATORY FACTOR ANALYSIS
2 EXTRACTION AND ROTATION
3 SAMPLE SIZE MATTERS
4 REPLICATION STATISTICS IN EFA
5 BOOTSTRAP APPLICATIONS IN EFA
6 DATA CLEANING AND EFA
7 ARE FACTOR SCORES A GOOD IDEA?
8 HIGHER ORDER FACTORS
9 AFTER THE EFA: INTERNAL CONSISTENCY
10 SUMMARY AND CONCLUSIONS
Best Practices in Exploratory Factor Analysis (EFA) is a practitioner-oriented look at this popular and often-misunderstood statistical technique. We avoid formulas and matrix algebra, instead focusing on evidence-based best practices so you can focus on getting the most from your data.Each chapter reviews important concepts, uses real-world data to provide authentic examples of analyses, and provides guidance for interpreting the results of these analysis. Not only does this book clarify often-confusing issues like various extraction techniques, what rotation is really rotating, and how to use parallel analysis and MAP criteria to decide how many factors you have, but it also introduces replication statistics and bootstrap analysis so that you can better understand how precisely your data are helping you estimate population parameters. Bootstrap analysis also informs readers of your work as to the likelihood of replication, which can give you more credibility. At the end of each chapter, the author has recommendations as to how to enhance your mastery of the material, including access to the data sets used in the chapter through his web site. Other resources include syntax and macros for easily incorporating these progressive aspects of exploratory factor analysis into your practice. The web site will also include enrichment activities, answer keys to select exercises, and other resources. The fourth "best practices" book by the author, Best Practices in Exploratory Factor Analysis continues the tradition of clearly-written, accessible guides for those just learning quantitative methods or for those who have been researching for decades.NEW in August 2014! Chapters on factor scores, higher-order factor analysis, and reliability.
Chapters:
1 INTRODUCTION TO EXPLORATORY FACTOR ANALYSIS
2 EXTRACTION AND ROTATION
3 SAMPLE SIZE MATTERS
4 REPLICATION STATISTICS IN EFA
5 BOOTSTRAP APPLICATIONS IN EFA
6 DATA CLEANING AND EFA
7 ARE FACTOR SCORES A GOOD IDEA?
8 HIGHER ORDER FACTORS
9 AFTER THE EFA: INTERNAL CONSISTENCY
10 SUMMARY AND CONCLUSIONS
Jason W. Osborne is Professor and Chair in Educational and Counseling Psychology at the University of Louisville. He has specialized for 20 years in publishing and presenting on best practices in quantitative methods. He is author of four books and over 70 peer reviewed journal articles, and has presented at national and international conferences dozens of time. His books and articles have been cited in scholarly works over CSP twice previously, and has been cited over 6800 times. He is an Accredited Professional Statistician(tm) by the American Statistical Association.
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