It's important to place the issues expressed by Ryan and Ford (2010) in historical perspective. Although they acknowledge some earlier expressions of concern, especially with respect to professional training (Naylor, 1971), I think it's fair to characterize the overall tenor of their essay as one of sounding the alarm: a new and unique impending identity crisis. However, as previously noted, “industrial-organizational psychology seems to be subject to recurring identity threats” (Lefkowitz, 2005, p. 18).

When the Penn State football scandal exploded in 2012, observers tended to frame events in terms of individuals behaving badly or irresponsibly. The perpetrator of child abuse was convicted and sent to prison; the head football coach was fired; the president of the University and several senior administrators were terminated; and the former head of the Board of Trustees was forced to resign. Certainly, these actions were understandable under the circumstances. Terrible crimes had been committed and covered up for over a decade. Nevertheless, an exclusively individual focus overlooks the roles of 9 groups whose collective behavior first allowed the criminal acts to occur and then put an end to them. The groups included the children's families and high school coaches, the Penn State football coaching staff, the Penn State senior administration, the Penn State Board of Trustees, the Second Mile charitable organization, the Centre County Pennsylvania criminal justice system, Penn State students, the Big 10 athletic conference, and the National Collegiate Athletic Association. This article employs a group and intergroup perspective to analyze key events and to explain both the dysfunctional systemic behavior and the corrective actions.

Over the past 25 years, industrial and organizational (I-O) psychologists have made great strides forward in the area of teams research. They have developed and tested meso-level theories that explain and predict the behavior of individuals in teams and teams operating within and across organizations. The continued contributions of I-O psychologists to theory and research on teams require us to address the challenges—several of which were well described in the focal article (Guzzo, Fink, King, Tonidandel, & Landis, 2015)—and embrace the opportunities that are being ushered in by big and broad data streams (Hendler, 2013). We suggest that a principal unique value add of the I-O psychologist to the basic scientific endeavor of understanding small teams comes in the form of theory—theories that explain why, when, how, and to what end individuals form relationships needed for teams to function in unison toward the accomplishment of collective goals. Some have argued that the big data revolution means “the end of theory,” suggesting petabyte data render theoretical models obsolete (Anderson, 2008). On the contrary, we submit that big-data enabled social science holds the promise of rapid progress in social science theory, particularly in the area of teams.

Sackett et al. (2022) identified previously unnoticed flaws in the way range restriction corrections have been applied in prior meta-analyses of personnel selection tools. They offered revised estimates of operational validity, which are often quite different from the prior estimates. The present paper attempts to draw out the applied implications of that work. We aim to a) present a conceptual overview of the critique of prior approaches to correction, b) outline the implications of this new perspective for the relative validity of different predictors and for the tradeoff between validity and diversity in selection system design, c) highlight the need to attend to variability in meta-analytic validity estimates, rather than just the mean, d) summarize reactions encountered to date to Sackett et al., and e) offer a series of recommendations regarding how to go about correcting validity estimates for unreliability in the criterion and for range restriction in applied work.

Although we agree with Costanza and Finkelstein (2015) that current research has failed to find consistent evidence of actual generational differences and that this research is limited methodologically, we suggest that at least some of these limitations could and should be addressed by future research before any firm conclusions are drawn. Further, the authors make an important but empirically untested assumption that generational stereotypes exist. We discuss why it is important to rigorously test this assumption. Finally, the authors assert that generational stereotypes are being “sold” as business strategy to organizations and managers and that that they should be neither sold nor used in the workplace. However, the authors’ recommendation is based on the premise that individuals acquire stereotypes from others and that they can easily suppress the use of their stereotypes. This advice runs counter to what research suggests about stereotypic processes. We more fully discuss each of these three points next.

We have structured our response around 3 rough categories of commentary themes: those that provided illustrations of existing person-centric research, those that provided novel ideas about extending the viewpoint to traditional research areas, and those that criticized our neglect of issues of morality and power differences. In our response, we clarify and reiterate our position as advocating a science of first-person work experience. In doing so, we differentiate our agenda from others labeled person-centric but not first-person experiential in the way we describe it. We also differentiate our position from a primary interest in worker treatment and well-being, except as part of work experience, and defend that difference.

Guzzo, Fink, King, Tonidandel, and Landis (2015) provide a clear overview of the implications of conducting research using big data. One element we believe was overlooked, however, was an individual-level perspective on big data; that is, what impact does this sort of data collection have on the individuals being studied? As psychologists, the ethics and impact of big data collection from workers should be at the forefront of our minds. In this reply, we use years of research on electronic monitoring and tracking to provide evidence that an individual-level perspective is an essential part of the discussion surrounding industrial–organizational psychology and big data. Specifically, we examine electronic performance monitoring (EPM) literature to identify how the widespread, pervasive collection of employee data affects employees’ attitudes and behaviors.

In their focal article, Ruggs et al. (2013) outline the missed opportunities for researchers within industrial–organizational (I–O) psychology in examining marginalized employees. The authors identify seven groups as having been overlooked by I–O psychologists and thus deserving greater attention in the future. They conclude their focal article by noting that, “Instead of being on the front line serving as scientists and allies for those who are marginalized and treated poorly, we have let these individuals take a backseat while we have gone fishing.” We disagree with this assertion. It is not that we have gone fishing and ignored marginalized employees. Rather, we have gone fishing, in rough waters, to locate hard-to-find fish. We are not purposefully ignoring marginalized employees. On the contrary, we are conducting research while battling numerical representation issues and ethical and administration issues. The focus of our commentary is to highlight these challenges and offer suggestions for addressing them in an effort to assist researchers in actually doing what the authors of the focal article are calling for them to do—to successfully engage in more focused research on these under-represented members of the workforce.