Assessing the Unidimensionality of Psychological Scales: Using Multiple Criteria from Factor Analysis |
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Authors: | Suzanne L Slocum-Gori Bruno D Zumbo |
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Institution: | (1) School of Population and Public Health, Faculty of Medicine, University of British Columbia, 2206 East Mall, Vancouver, BC, V6T 1Z3, Canada;(2) Measurement, Evaluation, & Research Methodology Program, Department of Statistics, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada |
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Abstract: | Whenever one uses a composite scale score from item responses, one is tacitly assuming that the scale is dominantly unidimensional.
Investigating the unidimensionality of item response data is an essential component of construct validity. Yet, there is no
universally accepted technique or set of rules to determine the number of factors to retain when assessing the dimensionality
of item response data. Typically factor analysis is used with the eigenvalues-greater-than-one rule, the ratio of first-to-second
eigenvalues, parallel analysis, root-mean-square-error-of-approximation, or hypothesis testing approaches involving chi-square
tests from Maximum Likelihood or Generalized Least Squares estimation. The purpose of this study was to investigate how these
various procedures perform individually, and in combination, when assessing the unidimensionality of item response data via
a computer simulated design. Conditions such as sample size, magnitude of communality, distribution of item responses, proportion
of communality on second factor, and the number of items with non-zero loadings on the second factor were varied. Results
indicate that there was no one individual decision-making method that identified unidimensionality under all conditions manipulated.
Given the low communalities, all individual decision-making methods failed to detect unidimensionality for the combination
where sample size was small, magnitude of communality was low, and item distributions were skewed. A set of guidelines and
a new statistical methodology are provided for researchers. |
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Keywords: | |
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