NDLI: On the number of factors to retain in exploratory factor analysis for ordered categorical data

Content Provider	Springer Nature Link
Author	Yang, Yanyun Xia, Yan
Copyright Year	2014
Abstract	Conducting exploratory factor analysis (EFA) using statistical extraction methods has been recommended, but little is known about the accuracy of the decisions regarding the number of factors to retain for ordered categorical item data by considering a chi-square test, fit indices, and conventional criteria, such as eigenvalue >1 and parallel analysis. With computer-generated data, the authors examined the accuracy of decisions regarding the number of factors to retain for categorical item data, by combining these pieces of information using weighted least-square with mean and variance adjustment estimation methods based on polychoric correlations. A chi-square difference test was also conducted to compare nested EFA models. The results showed that the eigenvalue >1 criterion resulted in too many factors, in general. The chi-square test, chi-square difference test, fit indices, and parallel analysis performed reasonably well when the number of scale points was four, the number of items was 24, the sample size was at least 200, and the categorical distributions were similar across items. However, parallel analysis had a tendency toward factor underextraction when the correlation among factors was .50, particularly for two-point and 12-item scales.
Starting Page	756
Ending Page	772
Page Count	17
File Format	PDF
ISSN	07433808
Journal	Behavior Research Methods
Volume Number	47
Issue Number	3
e-ISSN	15543528
Language	English
Publisher	Springer US
Publisher Date	2014-06-20
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Exploratory factor analysis Polychoric correlations Eigenvalue >1 Parallel analysis Chi-square test Fit indices Cognitive Psychology
Content Type	Text
Resource Type	Article
Subject	Experimental and Cognitive Psychology 3200/3201 Psychology

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Using Fit Statistic Differences to Determine the Optimal Number of Factors to Retain in an Exploratory Factor Analysis

A modified procedure for parallel analysis of ordered categorical data

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On the number of factors to retain in exploratory factor analysis for ordered categorical data

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