Harry Joo

The Time Has Come Bayesian Methods for Data Analysis in the Organizational Sciences

The use of Bayesian methods for data analysis is creating a revolution in fields ranging from genetics to marketing. Yet, results of our literature review, including more than 10,000 articles published in 15 journals from January 2001 and December 2010, indicate that Bayesian approaches are essentially absent from the organizational sciences. Our article introduces organizational science researchers to Bayesian methods and describes why and how they should be used. We use multiple linear regression as the framework to offer a step-by-step demonstration, including the use of software, regarding how to implement Bayesian methods. We explain and illustrate how to determine the prior distribution, compute the posterior distribution, possibly accept the null value, and produce a write-up describing the entire Bayesian process, including graphs, results, and their interpretation. We also offer a summary of the advantages of using Bayesian analysis and examples of how specific published research based on frequentist analysis-based approaches failed to benefit from the advantages offered by a Bayesian approach and how using Bayesian analyses would have led to richer and, in some cases, different substantive conclusions. We hope that our article will serve as a catalyst for the adoption of Bayesian methods in organizational science research.

Best-Practice Recommendations for Defining, Identifying, and Handling Outliers

The presence of outliers, which are data points that deviate markedly from others, is one of the most enduring and pervasive methodological challenges in organizational science research. We provide evidence that different ways of defining, identifying, and handling outliers alter substantive research conclusions. Then, we report results of a literature review of 46 methodological sources (i.e., journal articles, book chapters, and books) addressing the topic of outliers, as well as 232 organizational science journal articles mentioning issues about outliers. Our literature review uncovered (a) 14 unique and mutually exclusive outlier definitions, 39 outlier identification techniques, and 20 different ways of handling outliers; (b) inconsistencies in how outliers are defined, identified, and handled in various methodological sources; and (c) confusion and lack of transparency in how outliers are addressed by substantive researchers. We offer guidelines, including decision-making trees, that researchers can follow to define, identify, and handle error, interesting, and influential (i.e., model fit and prediction) outliers. Although our emphasis is on regression, structural equation modeling, and multilevel modeling, our general framework forms the basis for a research agenda regarding outliers in the context of other data-analytic approaches. Our recommendations can be used by authors as well as journal editors and reviewers to improve the consistency and transparency of practices regarding the treatment of outliers in organizational science research.

Using Market Basket Analysis in Management Research

Market basket analysis (MBA), also known as association rule mining or affinity analysis, is a data-mining technique that originated in the field of marketing and more recently has been used effectively in other fields, such as bioinformatics, nuclear science, pharmacoepidemiology, immunology, and geophysics. The goal of MBA is to identify relationships (i.e., association rules) between groups of products, items, or categories. We describe MBA and explain that it allows for inductive theorizing; can address contingency (i.e., moderated) relationships; does not rely on assumptions such as linearity, normality, and residual equal variance, which are often violated when using general linear model–based techniques; allows for the use of data often considered “unusable” and “messy” in management research (e.g., data not collected specifically for research purposes); can help build dynamic theories (i.e., theories that consider the role of time explicitly); is suited to examine relationships across levels of analysis; and is practitioner friendly. We explain how the adoption of MBA is likely to help bridge the much-lamented micro–macro and science–practice divides. We also illustrate that use of MBA can lead to insights in substantive management domains, such as human resource management (e.g., employee benefits), organizational behavior (e.g., dysfunctional employee behavior), entrepreneurship (e.g., entrepreneurs’ identities), and strategic management (e.g., corporate social responsibility). We hope our article will serve as a catalyst for the adoption of MBA as a novel methodological approach in management research.

Research on Hispanics benefits the field of management

Purpose – The papers published in this special issue demonstrate that the field of management can make important contributions to the knowledge about Hispanics and Latin Americans (HLAs) in the workplace. The purpose of this paper is to offer an alternative yet complementary perspective that conducting research on HLAs will make important contributions to the field of management. Design/methodology/approach – Conceptual paper. Findings – Research on HLAs provides opportunities to develop and use innovative research design and measurement approaches (including qualitative and hybrid methods), leads to innovative solutions and protocols for addressing ethical challenges and Institutional Review Board regulations, and creates opportunities to access large secondary databases, sources of data collection, and research funding. Research limitations/implications – Additional research is needed to realize the benefits that result from conducting research on HLAs in the workplace. Practical implications – Because ...

Not all nonnormal distributions are created equal: Improved theoretical and measurement precision.

We offer a four-category taxonomy of individual output distributions (i.e., distributions of cumulative results): (1) pure power law; (2) lognormal; (3) exponential tail (including exponential and power law with an exponential cutoff); and (4) symmetric or potentially symmetric (including normal, Poisson, and Weibull). The four categories are uniquely associated with mutually exclusive generative mechanisms: self-organized criticality, proportionate differentiation, incremental differentiation, and homogenization. We then introduce distribution pitting, a falsification-based method for comparing distributions to assess how well each one fits a given data set. In doing so, we also introduce decision rules to determine the likely dominant shape and generative mechanism among many that may operate concurrently. Next, we implement distribution pitting using 229 samples of individual output for several occupations (e.g., movie directors, writers, musicians, athletes, bank tellers, call center employees, grocery checkers, electrical fixture assemblers, and wirers). Results suggest that for 75% of our samples, exponential tail distributions and their generative mechanism (i.e., incremental differentiation) likely constitute the dominant distribution shape and explanation of nonnormally distributed individual output. This finding challenges past conclusions indicating the pervasiveness of other types of distributions and their generative mechanisms. Our results further contribute to theory by offering premises about the link between past and future individual output. For future research, our taxonomy and methodology can be used to pit distributions of other variables (e.g., organizational citizenship behaviors). Finally, we offer practical insights on how to increase overall individual output and produce more top performers.

Gender productivity gap among star performers in STEM and other scientific fields.

We examined the gender productivity gap in science, technology, engineering, mathematics, and other scientific fields (i.e., applied psychology, mathematical psychology), specifically among star performers. Study 1 included 3,853 researchers who published 3,161 articles in mathematics. Study 2 included 45,007 researchers who published 7,746 articles in genetics. Study 3 included 4,081 researchers who published 2,807 articles in applied psychology and 6,337 researchers who published 3,796 articles in mathematical psychology. Results showed that (a) the power law with exponential cutoff is the best-fitting distribution of research productivity across fields and gender groups and (b) there is a considerable gender productivity gap among stars in favor of men across fields. Specifically, the underrepresentation of women is more extreme as we consider more elite ranges of performance (i.e., top 10%, 5%, and 1% of performers). Conceptually, results suggest that individuals vary in research productivity predominantly because of the generative mechanism of incremental differentiation, which is the mechanism that produces power laws with exponential cutoffs. Also, results suggest that incremental differentiation occurs to a greater degree among men and certain forms of discrimination may disproportionately constrain women’s output increments. Practically, results suggest that women may have to accumulate more scientific knowledge, resources, and social capital to achieve the same level of increase in total outputs as their male counterparts. Finally, we offer recommendations on interventions aimed at reducing constraints for incremental differentiation among women that could be useful for narrowing the gender productivity gap specifically among star performers.