Structural Equation Modeling and the Separation of within-Unit Change and between-Unit Variation in Panel Data |
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| Coordinator 1 | Dr Daniel Seddig (University of Cologne & University of Zurich) |
| Coordinator 2 | Professor Elmar Schlueter (University of Giessen) |
| Coordinator 3 | Mr Nico Seifert (Martin Luther University Halle-Wittenberg) |
In many fields of social science, researchers analyze panel data to study change within the units of interest (e.g., persons, groups, organizations, or countries). However, it is not always clear whether the applied statistical models are suitable to identify a causal effect at the within-unit level. Especially the aspect of heterogeneity due to time and due to variation between units may blur statistical estimates and substantive interpretations (Voelkle & Wagner, 2017). In the sociological and econometric literature, between-unit heterogeneity has also been addressed as a problem of omitted unobserved confounders (e.g., Halaby, 2004; Wooldridge, 2002) and fixed-effects regression models (FEM) are recommended as a solution.
Researchers have recently combined the advantages of structural equation modeling (SEM) and FEM by treating the time-invariant unobserved differences between units as latent random variables (e.g., Allison et al., 2017; Bollen & Brand, 2010; Hamaker et al., 2015). The idea of separating within- and between unit variability is not utterly new in SEM (e.g., in latent growth curve modeling) and many features of SEM seem adequate to address particular issues. For example:
(1) The use of lagged dependent and lagged independent variables
(2) Sequential exogeneity and reciprocal causality
(3) Flexible specification and comparison/test of alternative models
(4) Maximum likelihood estimation and handling of missing data
This session aims at presenting studies that use SEM to address unobserved heterogeneity due to time and/or due to differences between units. We welcome (1) applied presentations that make use of survey panel data, and/or that (2) take a methodological approach to address one of the issues listed above or related ones for example by using Monte-Carlo simulations.