Best-practice recommendations for estimating cross-level interaction effects using multilevel modeling

• Autores: Herman Aguinis, Ryan K. Gottfredson, Steven Andrew Culpepper
• Localización: Journal of Management, ISSN-e 1557-1211, Vol. 39, Nº. 6, 2013, págs. 1490-1528
• Idioma: inglés
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• Dialnet Métricas: 8 Citas
• Resumen

  • Multilevel modeling allows researchers to understand whether relationships between lower-level variables (e.g., individual job satisfaction and individual performance, firm capabilities and performance) change as a function of higher-order moderator variables (e.g., leadership climate, market-based conditions). We describe how to estimate such cross-level interaction effects and distill the technical literature for a general readership of management researchers, including a description of the multilevel model building process and an illustration of analyses and results with a data set grounded in substantive theory. In addition, we provide 10 specific best-practice recommendations regarding persistent and important challenges that researchers face before and after data collection to improve the accuracy of substantive conclusions involving cross-level interaction effects. Our recommendations provide guidance on how to define the cross-level interaction effect, compute statistical power and make research design decisions, test hypotheses with various types of moderator variables (e.g., continuous, categorical), rescale (i.e., center) predictors, graph the cross-level interaction effect, interpret interactions given the symmetrical nature of such effects, test multiple cross-level interaction hypotheses, test cross-level interactions involving more than two levels of nesting, compute effect-size estimates and interpret the practical importance of a cross-level interaction effect, and report results regarding the multilevel model building process.