Testing for Lagged,Cotemporal,and Total Dependence in Cross-Lagged Panel Analysis

Abstract

Panel studies are statistical studies in which two or more variables are observed for two or more subjects at two or more points in time. Cross-lagged panel studies are those studies in which the variables are continuous and divide naturally into two sets. Focus is on estimating and testing the effects, or impacts, of each set of variables on the other. In the regression approach the cross-lagged model is formulated as a multivariate regression model and regression methods are used to make inferences about the parameters. We contribute to this approach by decomposing the dependence in such models into lagged dependence and cotemporal dependence and developing three strategies for assessing the degree of dependence. We present hypothesis tests for these strategies and then demonstrate the three by analyzing a set of psychiatric panel data.

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