A first-difference approach to studying sequential associations with longitudinal panel data

In the behavioral sciences, multivariate longitudinal panel data are commonly analyzed to examine sequential associations among multiple variables over time. Historically, researchers analyzed these data using the cross-lag panel model; however, modifications to this model have recently been proposed to separate between-person differences, with the goal of estimating within-person lagged effects. A challenge with these extensions is that the between-persons model may not represent all sources of between-person differences, which can bias the within-person parameter estimates. The authors propose an alternative approach to studying sequential associations: the first-difference approach. The first-difference approach, based on econometric models, removes between-person differences by differencing the longitudinal panel data, which then allows within-person effects to be estimated. The authors describe the cross-lag panel model and its extensions and then discuss the first-difference approach. A small simulation study is conducted to examine how the first-difference model is able to capture within-person effects. Finally, the authors apply the first-difference approach to longitudinal panel data from the Midlife in the United States Study and compare the results with the more commonly used cross-lag panel models.