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Keith F. Widaman
Researcher at University of California, Riverside
Publications - 259
Citations - 35391
Keith F. Widaman is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Cognition & Medicine. The author has an hindex of 70, co-authored 240 publications receiving 31852 citations. Previous affiliations of Keith F. Widaman include University of California, Berkeley & University of California.
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Journal ArticleDOI
To Parcel or Not to Parcel: Exploring the Question, Weighing the Merits
TL;DR: In this article, the authors examine the controversial practice of using parcels of items as manifest variables in structural equation modeling (SEM) procedures and conclude that the unconsidered use of parcels is never warranted, while, at the same time, the considered use of items cannot be dismissed out of hand.
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Sample size in factor analysis.
TL;DR: A fundamental misconception about this issue is that the minimum sample size required to obtain factor solutions that are adequately stable and that correspond closely to population factors is not the optimal sample size.
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Factor analysis in the development and refinement of clinical assessment instruments.
Frank J. Floyd,Keith F. Widaman +1 more
TL;DR: The goals of exploratory and confirmatory factor analysis are described and procedural guidelines for each approach are summarized in this article, emphasizing the use of factor analysis in developing and refining clinical measures for assessing the invariance of measures across samples and for evaluating multitrait-multimethod data.
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Confirmatory factor analysis and item response theory : two approaches for exploring measurement invariance
TL;DR: This study investigated the utility of confirmatory factor analysis (CFA) and item response theory (IRT) models for testing the comparability of psychological measurements to investigate whether mood ratings collected in Minnesota and China were comparable.
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Hierarchically nested covariance structure models for multitrait-multimethod data
TL;DR: In this paper, a taxonomy of covariance structure models for multiretrait-multimethod data is presented, which can be used to test the significance of the convergent and the discriminant validity shown by a set of measures as well as the ex tent of method variance.