Article

From Data to Causes II: Comparing Approaches to Panel Data Analysis

Citation

Zyphur M, Voelkle M, Tay L, Allison P, Preacher K, Zhang Z, Hamaker E, Shamsollahi A, Pierides D, Koval P & Diener E (2020) From Data to Causes II: Comparing Approaches to Panel Data Analysis. Organizational Research Methods, 23 (4), pp. 688-716. https://doi.org/10.1177/1094428119847280

Abstract
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.

Keywords
panel data model; causal inference; cross-lagged model; Granger causality; structural equation model; multilevel model; latent curve model; latent growth model; Arellano-Bond methods

Journal
Organizational Research Methods: Volume 23, Issue 4

StatusPublished
FundersAustralian Research Council
Publication date31/10/2020
Publication date online31/05/2019
Date accepted by journal24/05/2019
URLhttp://hdl.handle.net/1893/29676
ISSN1094-4281
eISSN1552-7425