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Best-Practice Recommendations for Estimating Cross-Level Interaction Effects Using Multilevel Modeling
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. .Printed Journal
Call Number | Location | Available |
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JOM3906 | PSB lt.dasar - Pascasarjana | 1 |
Penerbit | Southern Management Association., |
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Edisi | - |
Subjek | Cross level moderation multilevel modeling interaction |
ISBN/ISSN | - |
Klasifikasi | - |
Deskripsi Fisik | - |
Info Detail Spesifik | - |
Other Version/Related | Tidak tersedia versi lain |
Lampiran Berkas | Tidak Ada Data |