Best Practices in Exploratory Factor Analysis
Best Practices in Exploratory Factor Analysis by Jason W. Osborne, published by CreateSpace Independent Publishing Platform in 2014, offers a practitioner-oriented examination of exploratory factor analysis (EFA), a statistical technique often misunderstood. This 150-page book emphasizes evidence-based best practices, steering clear of complex formulas and matrix algebra to help readers effectively analyze their data. Each chapter presents key concepts, utilizes real-world data for authentic examples, and provides guidance on interpreting analysis results.
Readers will find a comprehensive exploration of various extraction techniques, rotation methods, and tools like parallel analysis and MAP criteria for determining the number of factors. The book also introduces replication statistics and bootstrap analysis, enhancing the understanding of how data estimates relate to population parameters. At the end of each chapter, Osborne includes recommendations for mastering the material, along with access to data sets and additional resources on his website. This edition also features new chapters on factor scores, higher-order factor analysis, and reliability, making it a valuable resource for both beginners and seasoned researchers in psychology and statistics.
Official synopsis Publisher
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
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