Cross-Lagged Panel Analysis

Cross-lagged panel analysis is an analytical strategy used to describe reciprocal relationships, or directional influences, between variables over time. Cross-lagged panel models (CLPM), also referred to as cross-lagged path models and cross-lagged regression models, are estimated using panel data, or longitudinal data where each observation or person is recorded at multiple points in time. The models are considered "crossed" because they estimate relationships from one variable to another and vice-versa. They are considered "lagged" because they estimate relationships between variables across different time points. Taken together, cross-lagged panel models estimate the directional influence variables have on each other over time. The primary goal of cross-lagged panel models is to examine the causal influences between variables. In essence, cross-lagged panel analysis compares the relationship between variable X at Time 1 and variable Y at Time 2 with the relationship between variable Y at Time 1 and X at Time 2. It is widely used to examine the stability and relationships between variables over time to better understand how variables influence each other over time. This entry discusses cross-lagged panel analysis, an analytical strategy used in longitudinal communication research. It describes its rationales and origins in research. It also describes modern path-analytic approaches to cross-lagged panel analysis. Finally, this entry discusses some important assumptions and issues with cross-lagged panel analysis.