UBC Theses and Dissertations
Documenting the impact of outliers on decisions about the number of factors in exploratory factor analysis Liu, Yan
The overall purpose of this dissertation is to investigate how outliers affect the decisions about the number of factors in exploratory factor analysis (EFA) as determined by four widely used and/or highly recommended methods. Very few studies have looked into this issue in the literature and the conclusions are contradictory— i.e., with studies disagreeing as to whether outliers result in extra factors or a reduced number of factors. For this dissertation I systematically studied the impact of outliers arising from different sources and matched outlier simulation models with different type of outliers. Chapter 1 provides an overview of the gap between statistical theory regarding outliers and researchers’ day-to-day practice and their understanding of the effects of outliers. Chapter 2 presents a review of EFA with an emphasis on the four commonly used or highly recommended decision methods on the number of factors as well as a review of outliers which includes the sources of outliers and problems of outliers in factor analysis. Chapter 3 examines the effects of outliers arising from errors using the deterministic and slippage models. The results revealed that outliers can inflate, deflate, or have no effects on the decisions about the number of factors, which depends on the decision method used and the magnitude and number of outliers. Chapter 4 investigates the effects of outliers arising from an unintended and unknowingly included subpopulation using the mixture contamination model. The general conclusions are similar to chapter 3, but chapter 4 also reveals that symmetric and asymmetric contamination has different effects on different decision methods and the effects of outliers do not depend on sample size. Chapter 5 provides a general discussion of the findings of this dissertation, describes four novel contributions, and points out the limitations of the present research as well as the future research directions. This dissertation aims to bridge the gap from day-to-day researchers’ practice and understanding of the effects of outliers to current outlier research that emphasizes robust statistics. The findings of this dissertation address the contradictory conclusions made in previous studies.
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