Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29676
Appears in Collections:Management, Work and Organisation Journal Articles
Peer Review Status: Refereed
Title: From Data to Causes II: Comparing Approaches to Panel Data Analysis
Author(s): Zyphur, Michael
Voelkle, Manuel
Tay, Louis
Allison, Paul
Preacher, Kristopher
Zhang, Zhen
Hamaker, Ellen
Shamsollahi, Ali
Pierides, Dean
Koval, Peter
Diener, Ed
Contact Email: d.c.pierides@stir.ac.uk
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
Issue Date: 1-Oct-2020
Date Deposited: 10-Jun-2019
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.
DOI Link: 10.1177/1094428119847280
Rights: Zyphur M, Voelkle M, Tay L, Allison P, Preacher K, Zhang Z, Hamaker E, Shamsollahi A, Pierides D, Koval P & Diener E (2019) 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 Copyright © The Author(s) 2019. Reprinted by permission of SAGE Publications.

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