Timo von Oertzen
Max Planck Society
Publications 63
Growth models (GM) of the mixed-effects and latent curve varieties have become popular methodological tools in lifespan research. One of the major advantages of GM is their flexibility in studying ...
#1Florian Schmiedek (MPG: Max Planck Society)H-Index: 29
#2Martin Lövdén (KI: Karolinska Institutet)H-Index: 38
Last.Ulman Lindenberger (MPG: Max Planck Society)H-Index: 77
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Connections between interindividual differences and people’s behavior has been widely researched in various contexts, often by using top-down group comparisons to explain interindividual differences. In contrast, in this study, we apply a bottom-up approach in which we identify meaningful clusters in people’s concerns about various areas of life (e.g., their own health, their financial situation, the environment). We apply a novel method, Dirichlet clustering, to large-scale longitudinal data fr...
#1Thomas GlassenH-Index: 1
#2Timo von Oertzen (MPG: Max Planck Society)H-Index: 19
Last.Dmitry A. Konovalov (JCU: James Cook University)H-Index: 12
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Bayesian clustering algorithms, in particular those utilizing Dirichlet Processes (DP), return a sample of the posterior distribution of partitions of a set. However, in many applied cases a single clustering solution is desired, requiring a ’best’ partition to be created from the posterior sample. It is an open research question which solution should be recommended in which situation. However, one such candidate is the sample mean, defined as the clustering with minimal squared distance to all ...
#1Andreas M. Brandmaier (MPG: Max Planck Society)H-Index: 12
#2Timo von Oertzen (MPG: Max Planck Society)H-Index: 19
Last.Christopher Hertzog (MPG: Max Planck Society)H-Index: 57
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Latent Growth Curve Models (LGCM) have become a standard technique to model change over time. Prediction and explanation of inter-individual differences in change are major goals in lifespan research. The major determinants of statistical power to detect individual differences in change are the magnitude of true inter-individual differences in linear change (LGCM slope variance), design precision, alpha level, and sample size. Here, we show that design precision can be expressed as the inverse o...
#1Bommae Kim (Federal Reserve Bank of Kansas City)H-Index: 1
#2Timo von OertzenH-Index: 19
The conventional statistical methods to detect group differences assume correct model specification, including the origin of difference. Researchers should be able to identify a source of group differences and choose a corresponding method. In this paper, we propose a new approach of group comparison without model specification using classification algorithms in machine learning. In this approach, the classification accuracy is evaluated against a binomial distribution using Independent Validati...