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Correction to Tuerlinckx, Maris, Ratcliff, and De Boeck (2001).
Behav Res Methods
March 2025
Faculty of Psychology and Educational Sciences, KU Leuven - University of Leuven, Leuven, Belgium.
Publisher Correction: The representational instability in the generalization of fear learning.
NPJ Sci Learn
January 2025
KU Leuven, Leuven, Belgium.
Data sharing upon request and statistical consistency errors in psychology: A replication of Wicherts, Bakker and Molenaar (2011).
PLoS One
April 2023
Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium.
Sharing research data allows the scientific community to verify and build upon published work. However, data sharing is not common practice yet. The reasons for not sharing data are myriad: Some are practical, others are more fear-related.
View Article and Find Full Text PDFHow large should the next study be? Predictive power and sample size requirements for replication studies.
Stat Med
July 2022
Departments of Epidemiology and Population Health and of Medicine, Stanford University, Stanford, California, USA.
We use information derived from over 40K trials in the Cochrane Collaboration database of systematic reviews (CDSR) to compute the replication probability, or predictive power of an experiment given its observed (two-sided) -value. We find that an exact replication of a marginally significant result with has less than 30% chance of again reaching significance. Moreover, the replication of a result with still has only 50% chance of significance.
View Article and Find Full Text PDFOptimal sampling rates for reliable continuous-time first-order autoregressive and vector autoregressive modeling.
Psychol Methods
December 2021
Research Group of Quantitative Psychology and Individual Differences.
Autoregressive and vector autoregressive models are a driving force in current psychological research. In affect research they are, for instance, frequently used to formalize affective processes and estimate affective dynamics. Discrete-time model variants are most commonly used, but continuous-time formulations are gaining popularity, because they can handle data from longitudinal studies in which the sampling rate varies within the study period, and yield results that can be compared across data sets from studies with different sampling rates.
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