Suchergebnisse

Techniques such as dominance analysis and relative weight analysis have been proposed recently to evaluate more accurately predictor importance in ordinary least squares (OLS) regression. Similar questions of predictor importance also arise in instances where logistic regression is the primary mode of analysis. This article presents an extension of relative weight analysis that can be applied in logistic regression and thus aids in the determination of predictor importance. We briefly review relative importance techniques and then discuss a new procedure for calculating relative importance estimates in logistic regression. Finally, we present a substantive example applying this new approach to an example data set.

Abstract. Differences in mean rates of change are of primary interest in many controlled treatment evaluation studies. Generalized linear mixed model (GLMM) procedures are widely conceived to be the preferred method of analysis for repeated measurement designs when there are missing data due to dropouts, but systematic dependence of the dropout probabilities on antecedent or concurrent factors poses a problem for testing the significance of differences in mean rates of change across time in such designs. Controlling for the dependence of dropout probabilities on baseline values poses a special problem because a theoretically correct GLMM random-effects model does not permit including the same baseline score as both covariate and dependent variable. Monte Carlo methods are used herein to evaluate the actual Type 1 error rates and power resulting from two commonly-illustrated GLMM random-effects model formulations for testing the GROUPS × TIMES linear interaction effect in group-randomized repeated measurements designs. The two GLMM model formulations differ by either including or not including baseline scores as a covariate in the attempt to control for imbalance caused by the baseline-dependent dropouts. Results from those analyses are compared with results from a simpler two-stage analysis in which dropout-weighted slope coefficients fitted separately to the available repeated measurements for each subject serve as the dependent variable for an ordinary ANCOVA test for difference in mean rates of change. The Monte Carlo results confirm modestly superior Type 1 error protection but quite superior power for the simpler two-stage analysis of dropout-weighted slope coefficients as compared with those for either of the more mathematically complex GLMM analyses.

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

This study empirically examined the statistical and methodological issues raised in the reviewing process to determine what the "gatekeepers" of the literature, the reviewers and editors, really say about methodology when making decisions to accept or reject manuscripts. Three hundred and four editors' and reviewers' letters for 69 manuscripts submitted to the Journal of Business and Psychology were qualitatively coded using an iterative approach. Systematic coding generated 267 codes from 1,751 statements that identified common methodological and statistical errors by authors and offered themes across these issues. We examined the relationship between the issues identified and manuscript outcomes. The most prevalent methodological and statistical topics were measurement, control variables, common method variance, factor analysis, and structural equation modeling. Common errors included the choice and comprehensiveness of analyses. This qualitative analysis of methods in reviews provides insight into how current methodological debates reveal themselves in the review process. This study offers guidance and advice for authors to improve the quality of their research and for editors and reviewers to improve the quality of their reviews.

The current article notes that the standard application of relative importance analyses is not appropriate when examining the relative importance of interactive or other higher order effects (e.g., quadratic, cubic). Although there is a growing demand for strategies that could be used to decompose the predicted variance in regression models containing such effects, there has been no formal, systematic discussion of whether it is appropriate to use relative importance statistics in such decompositions, and if it is appropriate, how to go about doing so. The purpose of this article is to address this gap in the literature by describing three different yet related strategies for decomposing variance in higher-order multiple regression models—hierarchical F tests (a between-sets test), constrained relative importance analysis (a within-sets test), and residualized relative importance analysis (a between- and within-sets test). Using a previously published data set, we illustrate the different types of inferences these three strategies permit researchers to draw. We conclude with recommendations for researchers seeking to decompose the predicted variance in regression models testing higher order effects.

PurposeThe purpose of this paper is to examine the relative importance of four managerial skill dimensions (technical skill, administrative skill, human skill, and citizenship behavior) for predicting managerial effectiveness. In addition, it aims to explore whether the relative importance of these skill dimensions varies as a function of gender or organizational level.Design/methodology/approachParticipants were 733 managers enrolled in a nationally recognized leadership development program. Ratings of managerial skill were obtained from peers using a well‐validated 360‐degree assessment instrument, while manager effectiveness ratings were provided by supervisors. Moderated multiple regression and relative weight analysis were used to test the study's hypotheses.FindingsUsing ratings provided by multiple sources, these results show that all four of the managerial skill dimensions were significantly important predictors of manager effectiveness. Human skills were significantly more important than technical skill and citizenship behavior, while administrative skills were most important overall. Gender was not a significant moderator of the skill‐effectiveness relationship, but organizational level was.Practical implicationsIndividuals tasked with selecting, developing, or placing managers should take all four skill dimensions into account. Moreover, special consideration should be given to administrative skill, and this emphasis should increase for managers higher up in the organizational hierarchy.Originality/valueAlthough prior research has speculated about the importance of different managerial skills, this study is the first to provide empirical support for this skill typology in predicting actual managerial effectiveness using appropriate statistical analyses for examining the relative importance of these skill dimensions.

This study explores how researchers in the organizational sciences use and/or cite methodological 'best practice' (BP) articles. Namely, are scholars adhering fully to the prescribed practices they cite, or are they cherry picking from recommended practices without disclosing? Or worse yet, are scholars inaccurately following the methodological best practices they cite? To answer these questions, we selected three seminal and highly cited best practice articles published in Organizational Research Methods (ORM) within the past ten years. These articles offer clear and specific methodological recommendations for researchers as they make decisions regarding the design, measurement, and interpretation of empirical studies. We then gathered all articles that have cited these best practice pieces. Using comprehensive coding forms, we evaluated how authors are using and citing best practice articles (e.g., if they are appropriately following the recommended practices). Our results revealed substantial variation in how authors cited best practice articles, with 17.4% appropriately citing, 47.7% citing with minor inaccuracies, and 34.5% inappropriately citing BP articles. These findings shed light on the use (and misuse) of methodological recommendations, offering insight into how we can better improve our digestion and implementation of best practices as we design and test research and theory. Key implications and recommendations for editors, reviewers, and authors are discussed.

PurposeThough a number of demographics (e.g. sex, age) have been associated with work overload, scholars have yet to consider the potential impact of immigrant status. This is important because immigrants constitute a significant proportion of the workforce, and evidence suggests many employers believe they are easier to exploit. This paper aims to examine work hours, interpersonal justice, and immigrant status as predictors of work overload.Design/methodology/approachThe hypotheses were tested using a large, national random telephone survey of employees in the United States (n=2,757).FindingsAs expected, employees who worked more hours tended to perceive more work overload. Importantly, however, this effect interacted with interpersonal justice differently for immigrant and native‐born employees. Justice attenuated the effect of work hours for the former but seemed to exacerbate it somewhat for the latter. Of note, the interactive effect was more than five times larger for immigrants than for natives.Practical implicationsThe study shows that supervisors might require their employees to work longer hours without necessarily being perceived as abusive (i.e. overloading them). Doing so, however, requires treating employees justly in the form of respect, courtesy, and dignity. Though this form of just treatment is important for all employees, its effects are especially pronounced for immigrants.Originality/valueThe relationship between the number of hours worked and perceptions of work overload is examined for immigrant and non‐immigrant workers in the USA.

Meta-analyses are well known and widely implemented in almost every domain of research in management as well as the social, medical, and behavioral sciences. While this technique is useful for determining validity coefficients (i.e., effect sizes), meta-analyses are predicated on the assumption of independence of primary effect sizes, which might be routinely violated in the organizational sciences. Here, we discuss the implications of violating the independence assumption and demonstrate how meta-analysis could be cast as a multilevel, variance known (Vknown) model to account for such dependency in primary studies' effect sizes. We illustrate such techniques for meta-analytic data via the HLM 7.0 software as it remains the most widely used multilevel analyses software in management. In so doing, we draw on examples in educational psychology (where such techniques were first developed), organizational sciences, and a Monte Carlo simulation (Appendix). We conclude with a discussion of implications, caveats, and future extensions. Our Appendix details features of a newly developed application that is free (based on R), user-friendly, and provides an alternative to the HLM program.