Too Anecdotal to Be True? Mechanical Turk Is Not All Bots and Bad Data: Response to Webb and Tangney (2022).

In response to Webb and Tangney (2022) we call into question the conclusion that data collected on Amazon's Mechanical Turk (MTurk) was "at best-only 2.6% valid" (p. 1). We suggest that Webb and Tangney made certain choices during the study-design and data-collection process that adversely affected the quality of the data collected. As a result, the anecdotal experience of these authors provides weak evidence that MTurk provides low-quality data as implied. In our commentary we highlight best practice recommendations and make suggestions for more effectively collecting and screening online panel data.

Bias, Fairness, and Validity in Graduate-School Admissions: A Psychometric Perspective.

As many schools and departments are considering the removal of the Graduate Record Examination (GRE) from their graduate-school admission processes to enhance equity and diversity in higher education, controversies arise. From a psychometric perspective, we see a critical need for clarifying the meanings of measurement "bias" and "fairness" to create common ground for constructive discussions within the field of psychology, higher education, and beyond. We critically evaluate six major sources of information that are widely used to help inform graduate-school admissions decisions: grade point average, personal statements, resumes/curriculum vitae, letters of recommendation, interviews, and GRE. We review empirical research evidence available to date on the validity, bias, and fairness issues associated with each of these admission measures and identify potential issues that have been overlooked in the literature. We conclude by suggesting several directions for practical steps to improve the current admissions decisions and highlighting areas in which future research would be beneficial.

Rejoinder to Commentaries on Woo et al. (2022).

In this rejoinder, we discuss several areas of agreement as well as some noteworthy divergence in perspectives that are worth exploring further. We also note a few areas where immediate clarifications may be necessary. Next, we discuss practical solutions and challenges for improving the validity and fairness of graduate admissions. We conclude with a call for intellectual humility and openness in further advancing the field's discussions on this critical topic as well as for authenticity and persistence in effecting real changes to the system.

How do instrumental and expressive network positions relate to turnover? A meta-analytic investigation.

Although social network methods have proven valuable for predicting employee turnover, an informed use of network methods for turnover management requires an integration and extension of extant networks-turnover research. To that end, this article addresses two relatively neglected issues in the networks-turnover literature: the lack of integration of turnover process models into networks-turnover research and the differential influence of "network content" (i.e., instrumental vs. expressive network resources) on turnover processes. To address these issues, we draw from social capital and turnover theories as a basis for investigating how turnover antecedents (i.e., work attitudes, job alternatives, and job performance) mediate the associations between instrumental and expressive degree centrality and turnover. We test a theoretical model using meta-analytic path analysis based on the results of random-effects meta-analyses (64 independent samples of working adults) of instrumental and expressive degree centrality in relation to job satisfaction, organizational commitment, job alternatives, job performance, and employee turnover. We found that both instrumental and expressive degree centrality relate to employee turnover, but through different mediating processes; instrumental degree centrality decreased the likelihood of turnover via job performance and organizational commitment, whereas expressive degree centrality decreased the likelihood of turnover via job satisfaction and organizational commitment. Furthermore, expressive degree centrality (as compared to instrumental degree centrality) had a negative association with turnover after accounting for these prominent turnover antecedents. These findings illustrate the importance of distinguishing between instrumental and expressive network positions in the turnover process as well as the value of leveraging employee networks for employee retention. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Systems Perspective of Amazon Mechanical Turk for Organizational Research: Review and Recommendations.

Amazon Mechanical Turk (MTurk) is becoming a prevalent source of quick and cost effective data for organizational research, but there are questions about the appropriateness of the platform for organizational research. To answer these questions, we conducted an integrative review based on 75 papers evaluating the MTurk platform and 250 MTurk samples used in organizational research. This integrative review provides four contributions: (1) we analyze the trends associated with the use of MTurk samples in organizational research; (2) we develop a systems perspective (recruitment system, selection system, and work management system) to synthesize and organize the key factors influencing data collected on MTurk that may affect generalizability and data quality; (3) within each factor, we also use available MTurk samples from the organizational literature to analyze key issues (e.g., sample characteristics, use of attention checks, payment); and (4) based on our review, we provide specific recommendations and a checklist for data reporting in order to improve data transparency and enable further research on this issue.