Longitudinal Data Analysis Using Structural Equation Models

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Comparison of Inverse Wishart and Separation-Strategy Priors for Bayesian Estimation of Covariance Parameter Matrix in Growth Curve Analysis (2016, methodology): “Both the trajectory plot and previous data analysis (e.g., McArdle & Nesselroade, 2014) suggested that an LGCM is plausible for the current data and, therefore, we fit the LGCM to the data.”
Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects (2017, unknown): “…has long been the cross-lagged panel model, originating with the two-wave, two-variable model proposed by Duncan (1969) and elaborated by many others (e.g., Finkel 1995; Hamaker, Kuiper, and Grasman 2015; Kenny and Judd 1996; Kessler and Greenberg 1981; Markus 1979; McArdle and Nesselroade 2014).”
On the Differences between General Cross-Lagged Panel Model and Random-Intercept Cross-Lagged Panel Model: Interpretation of Cross-Lagged Parameters and Model Choice (2020, background): “Although longitudinal designs have numerous advantages over cross-sectional designs (e.g., McArdle & Nesselroade, 2014), the issue of causal inference becomes complicated and challenging in general for longitudinal studies, because researchers must effectively account for time-varying and…”