Analyzing Longitudinal Reciprocal Relations: A Comparison of Three Cross-Lagged Modeling Approaches

• In Event: Virtual Poster Session
  In Virtual iPresentation Only: SIG-Structural Equation Modeling Virtual Poster Session

Abstract

The study used three discrete time structural equation models: the traditional cross-lagged panel model (CLPM), the random intercept cross-lagged panel model (RI-CLPM), and the latent-state trait model with autoregressive effects (LST-A) to examine the longitudinal reciprocal relations between family social support and job engagement among Latiné engineers. Following the analytical approaches used in Filosa et al.’s (2023) study, we compared the results of the estimated autoregressive effects, cross-lagged effects, and model fit. The results from all three approaches showed a similar pattern in the cross-lagged effects between family social support and work engagement over time. Different strengths of the autoregressive effects were observed across models. The significance of the study was discussed.

Authors

• Chia-Lin Tsai, University of Northern Colorado

• Diana Mathis, Purdue University

• Lisa Flores, University of Missouri

• Rachel Navarro, University of North Dakota - Grand Forks

• Pat O. Garriott, University of Denver

• Han Na Suh, University of Hawai'i at Hilo