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Predicting Differences in Model Parameters with Individual Parameter Contribution Regression Using the R Package ipcr

1
Psychological Research Methods, Department of Psychology, Humboldt-Universität zu Berlin, 12489 Berlin, Germany
2
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, 14195 Berlin, Germany
3
Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195 Berlin, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Alexander Robitzsch
Psych 2021, 3(3), 360-385; https://doi.org/10.3390/psych3030027
Received: 3 June 2021 / Revised: 26 July 2021 / Accepted: 28 July 2021 / Published: 6 August 2021
Unmodeled differences between individuals or groups can bias parameter estimates and may lead to false-positive or false-negative findings. Such instances of heterogeneity can often be detected and predicted with additional covariates. However, predicting differences with covariates can be challenging or even infeasible, depending on the modeling framework and type of parameter. Here, we demonstrate how the individual parameter contribution (IPC) regression framework, as implemented in the R package ipcr, can be leveraged to predict differences in any parameter across a wide range of parametric models. First and foremost, IPC regression is an exploratory analysis technique to determine if and how the parameters of a fitted model vary as a linear function of covariates. After introducing the theoretical foundation of IPC regression, we use an empirical data set to demonstrate how parameter differences in a structural equation model can be predicted with the ipcr package. Then, we analyze the performance of IPC regression in comparison to alternative methods for modeling parameter heterogeneity in a Monte Carlo simulation. View Full-Text
Keywords: heterogeneity; individual differences; linear regression; R; structural equation modeling; latent variables heterogeneity; individual differences; linear regression; R; structural equation modeling; latent variables
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MDPI and ACS Style

Arnold, M.; Brandmaier, A.M.; Voelkle, M.C. Predicting Differences in Model Parameters with Individual Parameter Contribution Regression Using the R Package ipcr. Psych 2021, 3, 360-385. https://doi.org/10.3390/psych3030027

AMA Style

Arnold M, Brandmaier AM, Voelkle MC. Predicting Differences in Model Parameters with Individual Parameter Contribution Regression Using the R Package ipcr. Psych. 2021; 3(3):360-385. https://doi.org/10.3390/psych3030027

Chicago/Turabian Style

Arnold, Manuel, Andreas M. Brandmaier, and Manuel C. Voelkle. 2021. "Predicting Differences in Model Parameters with Individual Parameter Contribution Regression Using the R Package ipcr" Psych 3, no. 3: 360-385. https://doi.org/10.3390/psych3030027

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