How to Test, Interpret, and Report Cross Level Interactions скачать в хорошем качестве

How to Test, Interpret, and Report Cross Level Interactions

Best Practices for Multilevel Modeling and Estimating Cross-Level Interaction Effects Multilevel modeling is critical for understanding relationships across levels in management research. Here are five best practices for using multilevel modeling: 1️⃣ Clearly Define Cross-Level Interaction Effects: Cross-level interaction effects occur when a higher-level variable influences the relationship between lower-level variables. A precise definition and theoretical justification ensure that hypotheses are well-structured and meaningful. 2️⃣ Plan for Statistical Power Early: Ensure your study design includes enough sample sizes at both levels to detect cross-level interactions. Tools like the power calculator from Mathieu et al. (2012) help balance sample sizes and increase the likelihood of uncovering true effects. 3️⃣ Center Predictors Thoughtfully: Centering lower-level predictors, such as group-mean centering, improves the interpretability of cross-level interaction effects. This practice helps isolate the influence of higher-level variables on lower-level relationships. 4️⃣ Graph Interactions for Better Interpretation: Visualizing cross-level interactions highlights their nature and direction. While graphs clarify findings, remember to report quantitative effect size measures for a complete understanding. 5️⃣ Report Comprehensive Results: For transparency, include all coefficients, standard errors, and variance components. Reporting the multilevel model-building process strengthens replicability and trust in your findings. Applying these practices fosters robust, actionable insights in management research. How are you leveraging multilevel modeling in your work? Get article: Aguinis, H., Gottfredson, R. K., & Culpepper, S. A. 2013. Best-practice recommendations for estimating cross-level interaction effects using multilevel modeling. Journal of Management, 39(6): 1490-1528. https://doi.org/10.1177/0149206313478188