Detecting consensus emergence in organizational multilevel data : power simulations

Theories suggest that groups within organizations often develop shared values, beliefs, affect, behaviors, or agreed-on routines; however, researchers rarely study predictors of consensus emergence over time. Recently, a multilevel-methods approach for detecting and studying emergence in organizational field data has been described. This approach-the consensus emergence model-builds on an extended three-level multilevel model. Researchers planning future studies based on the consensus emergence model need to consider (a) sample size characteristics required to detect emergence effects with satisfactory statistical power and (b) how the distribution of the overall sample size across the levels of the multilevel model influences power. We systematically address both issues by conducting a power simulation for detecting main and moderating effects involving consensus emergence under a variety of typical research scenarios and provide an R-based tool that readers can use to estimate power. Our discussion focuses on the future use and development of multilevel methods for studying emergence in organizational research

Methodological and conceptual issues in studying effort-reward fit

Purpose Research on effort-reward "imbalance" (ERI) has gained popularity in the occupational health literature, and authors typically use effort-reward ratios (ERRs) to study this phenomenon. This article provides a methodological and theoretical critique of this literature and suggestions on how future research can better study joint effects of efforts and reward. Design/methodology/approach The authors conducted a simulation study, analyzed panel data and surveyed the literature on the theoretical and methodological basis of the "imbalance" concept. Findings The simulation study indicates that under many conditions the ERR captures main effects of effort and reward and that effects also depend on the scaling of the variables. The panel data showed that when main effects and the interactions of effort and reward are entered simultaneously in a regression predicting mental and physical health, the significant effect of the ERRs disappears. The literature review reveals that psychological theories include more elaborate theoretical ideas on joint effects of effort and reward. Research limitations/implications The results suggest that moderated multiple regression analyses are better suited to detect a misfit between effort and reward than ERRs. The authors also suggest to use the term effort-reward fit in future research. Originality/value Methodologically and conceptually the authors showed that the ERR is not an appropriate approach because it confuses main effects with interaction effects. Furthermore, the concept of ERI is better substituted by a broader conceptualization of effort-reward fit that can be integrated with the existing literature on person-environment fit. Recommendations for future research are provided