@book{Aguinis2013,
	title = {Best-{Practice} {Recommendations} for {Estimating} {Cross}-{Level} {Interaction} {Effects} {Using} {Multilevel} {Modeling}},
	volume = {39},
	isbn = {0149-2063},
	abstract = {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.},
	author = {Aguinis, Herman and Gottfredson, Ryan K. and Culpepper, Steven Andrew},
	year = {2013},
	doi = {10.1177/0149206313478188},
	note = {Publication Title: Journal of Management
Issue: 6
ISSN: 01492063},
	keywords = {cross-level, interaction, moderation, multilevel modeling},
}

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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.","author":[{"propositions":[],"lastnames":["Aguinis"],"firstnames":["Herman"],"suffixes":[]},{"propositions":[],"lastnames":["Gottfredson"],"firstnames":["Ryan","K."],"suffixes":[]},{"propositions":[],"lastnames":["Culpepper"],"firstnames":["Steven","Andrew"],"suffixes":[]}],"year":"2013","doi":"10.1177/0149206313478188","note":"Publication Title: Journal of Management Issue: 6 ISSN: 01492063","keywords":"cross-level, interaction, moderation, multilevel modeling","bibtex":"@book{Aguinis2013,\n\ttitle = {Best-{Practice} {Recommendations} for {Estimating} {Cross}-{Level} {Interaction} {Effects} {Using} {Multilevel} {Modeling}},\n\tvolume = {39},\n\tisbn = {0149-2063},\n\tabstract = {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.},\n\tauthor = {Aguinis, Herman and Gottfredson, Ryan K. and Culpepper, Steven Andrew},\n\tyear = {2013},\n\tdoi = {10.1177/0149206313478188},\n\tnote = {Publication Title: Journal of Management\nIssue: 6\nISSN: 01492063},\n\tkeywords = {cross-level, interaction, moderation, multilevel modeling},\n}\n\n","author_short":["Aguinis, H.","Gottfredson, R. 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