On the Importance of Considering Concurrent Effects in Random-Intercept Cross-Lagged Panel Modelling
Example Analysis of Bullying and Internalising Problems
Bibliographic Data
| ID | 19291925 |
|---|---|
| Authors | Lydia Gabriela Speyer (0000-0001-9071-4020, Lancaster University, corresponding author), Xinxin Zhu (0000-0001-6229-5615, University of Edinburgh), Yi Yang (0000-0002-9729-8522, University of Edinburgh), Denis Ribeaud (0000-0002-1668-8319, University of Zurich), Manuel P Eisner (0000-0001-5436-9282, University of Zurich) |
| Year | 2025 |
| Volume | 60 |
| Issue | 2 |
| Pages | 328-344 |
| Publication date | 2025-03-04 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2024.2428222 |
| PMID | 39588794 |
| OpenAlex | W4404718634 |
| Language | EN |
| Citations received | 3 |
| References cited | 34 |
Random-intercept cross-lagged panel models (RI-CLPMs) are increasingly used to investigate research questions focusing on how one variable at one time point affects another variable at the subsequent time point. Due to the implied temporal sequence of events in such research designs, interpretations of RI-CLPMs primarily focus on longitudinal cross-lagged paths while disregarding concurrent associations and modeling these only as residual covariances. However, this may cause biased cross-lagged effects. This may be especially so when data collected at the same time point refers to different reference timeframes, creating a temporal sequence of events for constructs measured concurrently. To examine this issue, we conducted a series of empirical analyses in which the impact of modeling or not modeling of directional within-time point associations may impact inferences drawn from RI-CLPMs using data from the longitudinal z-proso study. Results highlight that not considering directional concurrent effects may lead to biased cross-lagged effects. Thus, it is essential to carefully consider potential directional concurrent effects when choosing models to analyze directional associations between variables over time. If temporal sequences of concurrent effects cannot be clearly established, testing multiple models and drawing conclusions based on the robustness of effects across all models is recommended
Econometrics · Meta-analysis · Panel analysis · Panel data · Random effects model · Statistics · Computer Science · Crime Patterns and Interventions · Mathematics · Medicine · Psychology · Qualitative Comparative Analysis Research · Social Power and Status Dynamics
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| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |