From Data to Causes II: Comparing Approaches to Panel Data Analysis
Lebenswissenschaftliche Fakultät
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.
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Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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