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Lookup NU author(s): Dr Dean PieridesORCiD
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This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.
Author(s): Zyphur MJ, Voelkle M, Tay L, Allison P, Preacher K, Zhang Z, Hamaker E, Shamsollahi A, Pierides D, Koval P, Diener E
Publication type: Article
Publication status: Published
Journal: Organizational Research Methods
Year: 2020
Volume: 23
Issue: 4
Pages: 688-716
Print publication date: 01/10/2020
Online publication date: 24/05/2019
Acceptance date: 24/05/2019
ISSN (print): 1094-4281
ISSN (electronic): 1552-7425
Publisher: Sage
URL: https://doi.org/10.1177/1094428119847280
DOI: 10.1177/1094428119847280
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