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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
On the Merits of Orthogonalizing Powered and Product Terms: Implications for Modeling Interactions Among Latent Variables
TL;DR: In this paper, the authors highlight the merits of residual centering for representing interaction and powered terms in standard regression contexts (e.g., Lance, 1988), and propose an orthogonalizing approach to represent latent variable interactions.
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Life-span development of self-esteem and its effects on important life outcomes
TL;DR: The results suggest that self-esteem has a significant prospective impact on real-world life experiences and that high and low self- esteem are not mere epiphenomena of success and failure in important life domains.
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Effects of extensive temporal lobe damage or mild hypoxia on recollection and familiarity.
Andrew P. Yonelinas,Andrew P. Yonelinas,Neal E. A. Kroll,Neal E. A. Kroll,Joel R. Quamme,Michele M. Lazzara,Mary-Jane Sauvé,Keith F. Widaman,Robert T. Knight,Robert T. Knight +9 more
TL;DR: It is found that the regions disrupted in mild hypoxia, such as the hippocampus, are centrally involved in conscious recollection, whereas the surrounding temporal lobe supports familiarity-based memory discrimination.
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Common factor analysis versus principal component analysis: Differential bias in representing model parameters?
TL;DR: It is demonstrated that the difference between common factor and principal component pattern loadings is inversely related to the number of indicators per factor, not to the total number of observed variables in the analysis, countering claims by both Snook and Gorsuch and Velicer and Jackson.
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Multidimensionality and Structural Coefficient Bias in Structural Equation Modeling: A Bifactor Perspective
TL;DR: In this article, the authors consider several indices to indicate whether multidimensional data are "unidimensional enough" to fit with a unidimensional measurement model, especially when the goal is to avoid excessive bias in structural parameter estimates.