J
John J. McArdle
Researcher at University of Southern California
Publications - 200
Citations - 17911
John J. McArdle is an academic researcher from University of Southern California. The author has contributed to research in topics: Structural equation modeling & Cognition. The author has an hindex of 67, co-authored 200 publications receiving 16342 citations. Previous affiliations of John J. McArdle include University of Virginia & Max Planck Society.
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Latent variable growth within behavior genetic models.
TL;DR: The purpose of this paper is to introduce one kind of latent-variable structural-equation model for multivariate longitudinal data which includes behavioral genetic components and permits hypothesis testing of various biometric models of the sources of these individual differences in latent growth.
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Age changes in processing speed as a leading indicator of cognitive aging.
TL;DR: Comparing the dynamic predictions of 2-component theories of intelligence and the processing speed theory of cognitive aging suggests that processing speed is a leading indicator of age changes in memory and spatial ability, but not verbal ability.
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Neuropsychological Measures in Normal Individuals That Predict Subsequent Cognitive Decline
Deborah Blacker,Hang Lee,Alona Muzikansky,Emily C. Martin,Emily C. Martin,Rudolph E. Tanzi,John J. McArdle,Mark B. Moss,Marilyn S. Albert +8 more
TL;DR: Episodic memory performance amongnormal individuals predicts time to progression to mild impairment while apolipoprotein E epsilon2 status is associated with lower risk of cognitive decline among normal individuals.
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Longitudinal Modeling of Developmental Changes in Psychological Research
Emilio Ferrer,John J. McArdle +1 more
TL;DR: A review of recent advances in longitudinal models for multivariate change is provided in this paper, where the authors claim the need for dynamic modeling approaches as a way to evaluate psychological theories.
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Investigating Ceiling Effects in Longitudinal Data Analysis
TL;DR: The results showed that ceiling effects led to incorrect model selection and biased parameter estimation (shape of the curve and magnitude of the changes) when regular growth curve models were applied and the Tobit growth curve model performed very well in dealing with ceiling effects in longitudinal data analysis.