Changes in Size and Interpretation of Parameter Estimates in Within-Person Models in the Presence of Time-Invariant and Time-Varying Covariates
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For several decades, cross-lagged panel models (CLPM) have been the dominant statistical model in relationship research for investigating reciprocal associations between two (or more) constructs over time. However, recent methodological research has questioned the frequent usage of the CLPM because, amongst other things, the model commingles within-person associations with between-person associations, while most developmental research questions pertain to within-person processes. Furthermore, the model presumes that there are no third variables that confound the relationships between the longitudinally assessed variables. Therefore, the usage of alternative models such as the Random-Intercept Cross-Lagged Panel Model (RI-CLPM) or the Latent Curve Model with Structured Residuals (LCM-SR) has been suggested. These models separate between-person from within-person variation and they also control for time constant covariates. However, there might also be third variables that are not stable but rather change across time and that can confound the relationships between the variables studied in these models. In the present article, we explain the differences between the two types of confounders and investigate how they affect the parameter estimates of within-person models such as the RI-CLPM and the LCM-SR.
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RI-CLPM effectively distinguish between-person and within-person variation.
“while these models effectively distinguish between-person and within-person variation and attempt to account for time-invariant confounding, they do not address the potential impact of time-varying confounders (Mund et al., 2021).”
From Mediators during a Multimodal intervention for stress-induced exhaustion disorder (2024) · cites this paper as Mund et al., 2021
RI-CLPM attempt to account for time-invariant confounding.
“while these models effectively distinguish between-person and within-person variation and attempt to account for time-invariant confounding, they do not address the potential impact of time-varying confounders (Mund et al., 2021).”
From Mediators during a Multimodal intervention for stress-induced exhaustion disorder (2024) · cites this paper as Mund et al., 2021
RI-CLPM do not address the potential impact of time-varying confounders.
“while these models effectively distinguish between-person and within-person variation and attempt to account for time-invariant confounding, they do not address the potential impact of time-varying confounders (Mund et al., 2021).”
From Mediators during a Multimodal intervention for stress-induced exhaustion disorder (2024) · cites this paper as Mund et al., 2021
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- Authors
- Mund, M., Johnson, M. D., & Nestler, S.
- Affiliations
- Friedrich Schiller University Jena (DE)
- University of Alberta (CA)
- University of Münster (DE)
- Corresponding author
- Mund, Marcus
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2021-09-01
- Volume
- 12
- Pages
- 666928-666928
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC8441132
- OpenAlex
- W3197853676
- Cited by
- 80
- References
- 71
- Citations by year
- 2026: 15 · 2025: 13 · 2024: 15 · 2023: 18 · 2022: 17 · 2021: 2
- FWCI
- 9.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Common Library Network (Der Gemeinsame Bibliotheksverbund) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- cross-lagged panel model, random intercept cross-lagged panel model, within-person associations, between-person associations, time-varying covariates, bidirectional relationship, confounding, parameter estimates, longitudinal analysis
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