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On the Importance of Considering Concurrent Effects in Random-Intercept Cross-Lagged Panel Modelling: Example
Lydia G Speyer1, Xinxin Zhu2, Yi Yang2
1Department of Psychology, Lancaster University, Lancaster, UK.
Multivariate Behavioral Research
|November 26, 2024
Summary
Random-intercept cross-lagged panel models (RI-CLPMs) can produce biased results if concurrent effects are ignored. Properly accounting for directional within-time associations is crucial for accurate longitudinal analysis and understanding variable relationships over time.
Area of Science:
- Psychology
- Statistics
- Social Sciences
Background:
- Random-intercept cross-lagged panel models (RI-CLPMs) are common for analyzing temporal relationships between variables.
- RI-CLPM interpretations often prioritize cross-lagged paths, potentially overlooking concurrent associations.
- Ignoring concurrent effects can lead to biased estimates of longitudinal associations.
Purpose of the Study:
- To investigate the impact of modeling directional within-time associations on RI-CLPM inferences.
- To determine if disregarding concurrent effects biases cross-lagged effects in RI-CLPMs.
- To provide guidance on model selection for analyzing directional associations over time.
Main Methods:
- Empirical analyses using data from the longitudinal z-proso study.
- Comparison of RI-CLPMs with and without modeling directional concurrent effects.
- Assessment of the influence of concurrent effect modeling on cross-lagged effect estimates.
Main Results:
- Failure to model directional concurrent effects can significantly bias cross-lagged effects in RI-CLPMs.
- The temporal sequencing of concurrently measured variables can exacerbate bias when not explicitly modeled.
- Results underscore the importance of considering concurrent associations for accurate longitudinal inferences.
Conclusions:
- Carefully consider potential directional concurrent effects when using RI-CLPMs.
- If temporal sequences of concurrent effects are unclear, test multiple models.
- Draw conclusions based on the robustness of findings across different model specifications.
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