Cross-Lagged Panel Analysis

Directions of Causality

Basic methods for testing causality have several limitations. Correlational analysis relies on theoretical inferences to make arguments about causality. Because cross-sectional data represent only one moment in time, there is no way to determine if these inferences are correct. The experimental method utilizes randomization and control to provide a more robust method for examining causality. In many cases, however, randomization and control are not practical or even possible. For example, costs associated with recruiting truly random samples for multiple time points are often too expensive. Resources are not the only barriers to randomization. In many cases, randomization creates ethical dilemmas that make studies examining certain variables such as aging or illness problematic. In these situations, researchers often turn to longitudinal research and cross-lagged panel analysis.

Cross-Lagged Correlations

Cross-lagged panel analysis is used to compare the relationship between variable X at Time 1 (X1) and variable Y at Time 2 (Y2) with the relationship between Y1 and X2. In the past, this was accomplished by examining zero-order correlations. Cross-lagged correlations (CLC) were used to make arguments about causal directions between variables. Correlations of the same size indicated a reciprocal relationship. If one of the coefficients was larger, however, it suggested that changes in one variable lead to changes in the other variable and not the other way around. Comparing CLC thus provides some evidence of directional influence, but it has serious flaws.

Several weaknesses have been identified in the cross-lagged correlations method. One weakness is that CLC do not account for contemporaneous relationships between variables. Contemporaneous relationships refer to the synchronous correlations between variables within the same time point. Another weakness is that CLC do not account for the stability of each construct across time points. Stability refers to the degree to which values of a variable are unchanging over time. As a result of these shortcomings, the CLC method has largely been discarded in favor of cross-lagged path (or regression) models.

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