M
Manuel C. Voelkle
Researcher at Humboldt University of Berlin
Publications - 101
Citations - 2759
Manuel C. Voelkle is an academic researcher from Humboldt University of Berlin. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 23, co-authored 83 publications receiving 1861 citations. Previous affiliations of Manuel C. Voelkle include University of Mannheim & Humboldt State University.
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An SEM Approach to Continuous Time Modeling of Panel Data: Relating Authoritarianism and Anomia
TL;DR: A step-by-step review of the relationship between discrete and continuous time modeling is provided, and it is demonstrated how continuous time parameters can be obtained via structural equation modeling.
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Continuous time structural equation modeling with R package ctsem
TL;DR: In this article, an R package for continuous time structural equation modeling of panel (N > 1) and time series (N = 1) data, using full information maximum likelihood, is presented.
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Toward a Unified Framework for the Study of Between-Person and Within-Person Structures: Building a Bridge Between Two Research Paradigms.
TL;DR: A conceptual framework that distinguishes within-person (WP) and between- person (BP) sources of variation in psychological constructs is proposed and it is shown how to identify possible reasons for nonequivalence of BP and WP structures as well as establishing areas of convergence.
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From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)
Michael J. Zyphur,Paul D. Allison,Louis Tay,Manuel C. Voelkle,Manuel C. Voelkle,Kristopher J. Preacher,Zhen Zhang,Ellen L. Hamaker,Ali Shamsollahi,Dean Pierides,Peter Koval,Ed Diener,Ed Diener +12 more
TL;DR: In this article, the authors synthesize, compare, and extend methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework, which is the first paper in a series of two that synthesizes, compares, and extends methods.
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Let Me Guess How Old You Are: Effects of Age, Gender, and Facial Expression on Perceptions of Age
TL;DR: Investigating possible determinants of accuracy and bias in age estimation across the adult life span finds that age estimation ability decreased with age, and the age of neutral faces was estimated most accurately, while theAge of faces displaying happy expressions was most likely underestimated.