Purpose Multilevel mixed effects models are widely used in organizational behavior and organizational psychology to test and advance theory. At times, however, the complexity of the models leads researchers to draw erroneous inferences or otherwise use the models in less than optimal ways. We present nine take-away points intended to enhance the theoretical precision and utility of the models. Approach We demonstrate our points using two types of simulated data: one in which group membership is irrelevant, and the other in which relationships exist only because of group membership. We then demonstrate that the effects we observe in simulated data replicate in organizational data. Findings Little that we address will be new to methodology experts; nonetheless, we draw together a variety of points that we believe will help advance both theory and analytic rigor in multilevel analyses. Implications We make two points that run somewhat counter to conventional norms. First, we argue that mixed-effects models are appropriate even when ICC(1) values associated with the outcome data are small and non-significant. Second, we show that high ICC(2) values are not a prerequisite for detecting emergent multilevel relationships. Originality/ValueThe article is designed to be a resource for researchers who are learning about and applying mixed-effects (i.e., multilevel) models. are routinely used to analyze data in organizational behavior and organizational psychology. Researchers recognize that nested data are often non-independent such that responses on the dependent variable from members of the same group are more similar than would be expected by chance (Bliese 2000). The idea that mixed-effects models can account for non-independence is well documented; however, options surrounding model specification and interpreting parameter estimates from mixed-effects models are not always straightforward. As such, researchers occasionally underutilize mixedeffects models, engage in inappropriate model building, or misinterpret model parameters.Our goals are to (a) clarify situations where mixed-effects models may be useful and (b) explain the interpretation of results in common variants of the mixed-effects model. Much of what we cover will be known to methodologists; nonetheless, we believe that going back to basic ideas can help researchers more effectively use these methods to test and advance theory. We also emphasize that two of the points we raise (using mixed-effects models when levels of nonindependence are minimal and detecting emergent effects when group-mean reliability is low) run counter to conventional norms, so we encourage authors, editors, and reviewers to reconsider these two points in particular.

The ability to detect differences between groups partially impacts how useful a group-level variable will be for subsequent analyses. Direct consensus and referent-shift consensus group-level constructs are often measured by aggregating group member responses to multi-item scales. We show that current measurement validation practice for these group-level constructs may not be optimized with respect to differentiating groups. More specifically, a 10-year review of multilevel articles in top journals reveals that multilevel measurement validation primarily relies on procedures designed for individual-level constructs. These procedures likely miss important information about how well each specific scale item differentiates between groups. We propose that group-level measurement validation be augmented with information about each scale item's ability to differentiate groups. Using previously published datasets, we demonstrate how ICC(1) estimates for each item of a scale provide unique information and can produce group-level scales with higher ICC(1) values that enhance predictive validity. We recommend that researchers supplement conventional measurement validation information with information about item-level ICC(1) values when developing or modifying scales to assess group-level constructs. (PsycINFO Database Record

For decades, scholars and managers alike have shared a sustained interest in harnessing the talents of high-performing employees primarily due to their disproportionate contributions. An emerging research stream has begun examining the diverse effects that high performers elicit on their peers. Prior work now spans multiple organizational fields of study and utilizes a variety of high performer conceptualizations, theoretical lenses, and methodological approaches to examine the main effects of high performers as well as the boundary conditions of these effects. However, the body of work on high performers has yet to be systematically reviewed to synthesize the current state of the high performer literature and build commonality across disciplines. In this multidisciplinary review, we first establish conceptual clarity on what a high performer is (and is not) and identify the conceptualization of high performers used in current research. We then use appraisal theories to create a framework to organize the cognitive, affective, and behavioral peer effects sparked by high performers as well as to build an integrative view of the psychological mechanisms through which peers interpret and react to high performers. Following this, we outline several boundary conditions of high performer peer effects, including the characteristics of high performers, peers, and the context in which high performers and peers interact. We further consider how the various operationalizations of high performers are associated with different peer effects. We conclude by identifying and elaborating several avenues for future research that may yield useful cross-disciplinary insights.

No abstract

is an index based on market (e.g., customer satisfaction), financial (e.g., Tobin' s q), or accounting (e.g., EBIT [earnings before interest and taxes]) metrics. Competitive advantage occurs when a firm generates aboveaverage returns. Performance follows the logic that "more is better," whereas competitive advantage follows the logic of differentiation.

While leader departures from work units frequently occur within organizations and are assumed to negatively impact unit functioning, the collective reaction to a leader departure event can vary across time. While a common expectation of leader departure models is that the incoming leader is permanent, it is unclear how unit-level reactions, such as collective turnover and unit performance, might change over time in response to a departure event when the departing leader is replaced with a temporary leader. We draw on context emergent turnover (CET) theory and literature on leader departures to develop and empirically test specific hypotheses exploring relationships among leader departures, collective turnover, and unit performance over time. In addition, we examine the extent to which these relationships are influenced by the temporary status of the incoming leader. Using discontinuous growth models, we examine a longitudinal data set from 324 units within a large Latin American operation of a global direct sales company (N = 3,082 performance periods). Findings indicate that, after a leader departs, there is an immediate increase in collective turnover and that unit performance decreases over time. Further, when the incoming leader is temporary, unit performance increases briefly, but the rate of performance drops over time. Overall, our research offers insights with regard to how leader departures impact unit outcomes, as well as how long such effects last.

No abstract

No abstract