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

  • Paul D. Allison (Contributor)
  • Dean C. Pierides (Contributor)
  • Ali Shamsollahi (Contributor)
  • Ellen L. Hamaker (Contributor)
  • Kristopher J. Preacher (Contributor)
  • Louis Tay (Contributor)
  • Manuel C. Voelkle (Contributor)
  • Peter Koval (Contributor)
  • Zhen Zhang (Arizona State University) (Contributor)
  • Ed Diener (Contributor)
  • Michael J. Zyphur (Contributor)

Dataset

Description

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.
Date made availableJan 1 2020
Publisherfigshare SAGE Publications

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