Examining the Direction of Synchronous Effects in Panel Data: A Shiny Implementation of Direction Dependence Analysis

Music City Center 

Panel models are extensively utilized in social and psychological sciences to examine dynamic relationships between constructs measured repeatedly over time. Despite their widespread use, these models carry inherent limitations, particularly regarding assumptions of omitted confounders, challenges in precise model specification, and uncertainty in choosing appropriate measurement intervals. Of specific concern in panel models is the synchronous effect—the contemporaneous relationship between variables measured simultaneously—which is frequently ignored or presumed negligible due to methodological difficulties in verifying its directionality and potential confounding influences.

Recognizing this limitation, our study introduces Panel Data Direction Dependence Analysis (Panel DDA). Standard DDA, originally developed for cross-sectional data, does not take into account either prior measures or cross-lagged effects and does not take the advantage of panel data structure. We propose a two-stage procedure for Panel Data DDA, controlling for potential confounding from previous measurements and subsequently applying standard DDA procedures to determine the causal direction of synchronous effects.

To evaluate the effectiveness of Panel Data DDA, we conducted two extensive Monte Carlo simulation studies, each comprising 500 iterations. Our simulations compared the panel-based approach with traditional cross-sectional DDA under varying conditions of synchronous and cross-lagged effects. Results demonstrated superior performance of Panel DDA.

To promote broader adoption and practical application, we developed an accessible Shiny application. This tool allows users to intuitively conduct both panel and cross-sectional DDA analyses, visualize data distributions, manage outliers. We illustrate the practical utility of our method using real-world longitudinal data from the Network for Educator Effectiveness (NEE), focusing on teacher-student relationships (TSR), teacher cognitive engagement (CE), and teacher problem-solving and critical thinking (PCT). Empirical findings confirmed that TSR significantly predicts CE synchronously, reinforcing the importance of positive teacher-student interactions. Moreover, TSR emerged as a critical confounder when evaluating the relationship between CE and PCT, underscoring the necessity of controlling confounders in synchronous effect analyses.

Overall, this study advances methodological rigor in analyzing synchronous effects within panel data, significantly enhancing causal inference capabilities. The integration of a user-friendly analytical tool further ensures that this methodological advancement is widely accessible, offering substantial practical benefits to researchers across social sciences, education, and psychology who engage with longitudinal data.

Panel Data Analysis

Direction Dependence Analysis (DDA)

Synchronous Effect Model

Causal Inference

Shiny Application

Monte Carlo Simulation 

Section on Statistical Computing