The goal of focal articles in Industrial and Organizational Psychology: Perspectives on Science and Practice is to present new ideas or different takes on existing ideas and stimulate a conversation in the form of comment articles that extend the arguments in the focal article or that present new ideas stimulated by those articles. The two focal articles in this issue stimulated a wide range of reactions and a good deal of constructive input.

The goal of focal articles in Industrial and Organizational Psychology: Perspectives on Science and Practice is to present new ideas or different takes on existing ideas and stimulate a conversation in the form of comment articles that extend the arguments in the focal article or that present new ideas stimulated by those articles. The two focal articles in this issue stimulated a wide range of reactions and a good deal of constructive input.

Focal articles in Industrial and Organizational Psychology: Perspectives on Science and Practice present new ideas or different takes on existing ideas and stimulate a conversation in the form of comment articles that extend the arguments in the focal article or that present new ideas stimulated by those focal articles. The two focal articles in this issue stimulated a wide range of reactions and a good deal of constructive input.

The goal of focal articles in Industrial and Organizational Psychology: Perspectives on Science and Practice is to present new ideas or different takes on existing ideas and stimulate a conversation in the form of comment articles that extend the arguments in the focal article or that present new ideas stimulated by those articles. The two focal articles in this issue stimulated a wide range of reactions and a good deal of constructive input.

Sampling strategy has critical implications for the validity of a researcher's conclusions. Despite this, sampling is frequently neglected in research methods textbooks, during the research design process, and in the reporting of our journals. The lack of guidance on this issue often leads reviewers and journal editors to rely on simple rules of thumb, myth, and tradition for judgments about sampling, which promotes the unnecessary and counterproductive characterization of sampling strategies as universally “good” or “bad.” Such oversimplification, especially by journal editors and reviewers, slows the progress of the social sciences by considering legitimate data sources to be categorically unacceptable. Instead, we argue that sampling is better understood in methodological terms of range restriction and omitted variables bias. This considered approach has far-reaching implications because in industrial–organizational (I-O) psychology, as in most social sciences, virtually all of the samples are convenience samples. Organizational samples are not gold standard research sources; instead, they are merely a specific type of convenience sample with their own positive and negative implications for validity. This fact does not condemn the science of I-O psychology but does highlight the need for more careful consideration of how and when a finding may generalize based on the particular mix of validity-related affordances provided by each sample source that might be used to investigate a particular research question. We call for researchers to explore such considerations cautiously and explicitly both in the publication and in the review of research.

The world is awash in data. Data is being created and stored at ever-increasing rates through a variety of new methods and technologies. Data is accumulating in all sorts of accessible places. Much of that data is of great interest to industrial–organizational (I-O) psychologists, often in ways never anticipated by those who develop technologies and processes that generate and store that data. I-O psychologists also generate data in the course of research and practice in ways that, especially if joined with data originating from other sources, create giant datasets. This abundance of data—variables, measurements, observations, facts—can be used to inform a vast number of issues in research and practice. This is the new “big data” world, and beyond opportunities, this new world also presents challenges and potential hazards.

Stereotypes about generational differences in the workplace abound, and interventions for helping organizations and managers to deal with these supposed differences are increasing. In addition to popular press articles describing the differences and extolling the practices and strategies to deal with them, there are a growing number of researchers who are attempting to establish that there are such differences. Overall, however, there is little solid empirical evidence supporting generationally based differences and almost no theory behind why such differences should even exist. The purposes of this focal article are to explore the myths surrounding generations, to review the risks in using generations in organizational decisions, and to make recommendations for practitioners and researchers on how to proceed in this area.

Antinepotism policies are common in work organizations. Although cronyism appears to be commonplace as well, official policing of cronyism is less common. We argue that social connections in some crony relationships and apparently nepotic ones may add considerable value to organizations. We also argue that policing of nepotic relationships can be a form of unfair discrimination when the perception of inequity, rather than its reality, is being policed. Finally we consider effective approaches that simultaneously preserve the value of social connection, avoid the actual ethical breaches associated with some social connections, and avoid any unfair discrimination on the basis of group memberships (in this case, family and friends).

As discussed in Guzzo, Fink, King, Tonidandel, and Landis's (2015) focal article, big data is more than a passing trend in business analytics. The plethora of information available presents a host of interesting challenges and opportunities for industrial and organizational (I-O) psychology. When utilizing big data sources to make organizational decisions, our field has a considerable amount to offer in the form of advice on how big data metrics are derived and used and on the potential threats to validity that their use presents. We’ve all heard the axiom, “garbage in, garbage out,” and that applies regardless of whether the scale is a small wastebasket or a dump truck.

The focal article of Landers and Behrend (2015) persuasively argues that universally condemning potential convenience data sources outside of traditional industrial–organizational (I-O) samples such as college students and organization samples is misguided. This author agrees that instead we need to consider the context, strengths, and weaknesses of more recently recognized potential data sources. This commentary will focus on understanding the context of one particular potential data source, Amazon's Mechanical Turk (MTurk; https://www.mturk.com/). While some existing research has looked at the demographic characteristics of Amazon MTurk workers and how those workers’ answers compared with more traditional samples (Casler, Bickel, & Hackett, 2013; Goodman, Cryder, & Cheema, 2013; Paolacci & Chandler, 2014), for this commentary I decided to take a primarily different tack. For the space of approximately 50 days, I acted as an MTurk worker on the site and participated in online communities at which MTurk workers congregate. The purposes of this were to experience the MTurk worker environment firsthand and observe how MTurk workers interact with each other and the site. This was done in the spirit of participant-observer research. Stanton and Rogelberg (2002) argue that online communities might be a particularly fruitful avenue for such participant-observer research within the field of I-O psychology. I am quick to note here that I don't see my efforts here as anywhere near as extensive as much of the participant-observer work of the past, and I did my time on MTurk in the spirit of such work rather than as a match for their methodological and analytical rigor. The observations I make in this commentary will be couched in my own experiences as well as the existing literature base on Amazon MTurk.

We propose that generational differences are meaningful despite some theoretical and methodological challenges (cf. Costanza & Finkelstein, 2015). We will address five main issues: operationalizing generations, measuring generational differences, theoretical models of generations, mechanisms of generational change, and the importance of science versus stereotypes.

The focal article has given examples of children, other relatives, and friends as potential beneficiaries of preferential treatment and has discussed the counterbalancing likelihood of organizational gain from (properly) employing individuals who already share social connections. Surprisingly, there is minimal mention of spouses or domestic partners. From the 1970s through the 1990s, a number of articles were published on the legal and practical issues of applying antinepotism policies to spouses, but since 2000, the literature has been almost entirely silent. This is surprising given that, in 2013, the Bureau of Labor Statistics reported that 47.4% of U.S. families involve husbands and wives who both work.

We are in almost full agreement with Landers and Behrend's (2015) thoughtful and balanced critiques of various convenience sampling strategies focusing on the four most frequently used data sources in our field. In this commentary, we expand on Landers and Behrend's discussions specifically around Mechanical Turk (MTurk) by providing further supporting voice and/or clarity to the four potential concerns and relative advantages associated with MTurk. We also raise a few additional concerns and challenges to which the current literature does not yet offer definitive answers. We conclude with some practical guidelines summarizing the relative advantages and unique challenges of using MTurk.

The term nepotism (and even worse, cronyism) carries a negative connotation: favoring a relative (or friend) in an employment situation without considering the individual's suitability for the job. Although there can be obvious benefits associated with hiring kin (e.g., a sense of trust, swift learning of job-relevant content, loyalty, etc.; Bellow, 2003; Jones & Stout, 2015), the term itself implies that nepotism is a bad thing, and organizations often take steps to keep bad things from happening. Jones and Stout (2015) have argued that sweeping antinepotism policies in organizations eliminate the positives associated with hiring via a social connection preference, and such policies can lead to unfair discrimination. As industrial and organizational (I-O) psychologists, however, if we do our job—and by that I mean exemplary and objective screening, hiring, and performance assessment—and if we adequately manage the negative impressions that may reside in the minds of employees regarding nepotism, nobody gets hurt.

The focal article by Costanza and Finkelstein (2015) shed a light on the current status of generational research at work that often treats generation as an age grouping variable. Although the authors primarily focused on the applicability of research findings to the current workforce, I believe the benefits of studying and understanding generations should not be limited to this purpose. The goal of this commentary is twofold. First, I would like to revisit the concept of generation and the purposes of studying generations at work. Second, given the problems with generational research presented by Costanza and Finkelstein, I would like to propose potential actions that we can take.

Guzzo, Fink, King, Tonidandel, and Landis's (2015) focal article was intended to be not a set of standards but instead “stepping stones” to “raise awareness and provide direction” (p. 492) to our field for working with big data. We believe that the work done by the authors successfully achieved those objectives, and we encourage the Society for Industrial and Organizational Psychology (SIOP) Executive Committee to keep advancing this work toward greater clarity and guidance. Having clear alignment among members of our field and guidelines for handling nebulous issues such as big data is an important aspect of the scientist–practitioner model.

Jones and Stout (2015) have recommended that industrial and organizational (I-O) researchers and practitioners take a more nuanced perspective with respect to nepotism and cronyism—recast as social connection preference (SCP)—when relevant I-O decisions (e.g., hiring) are made on the basis of kin or affiliation considerations. Jones and Stout's (2015) arguments have challenged the reflexive view that SCP is always negative and thus the prudent approach is to restrict and, if possible, prohibit it. They call for further research to flesh out our understanding of SCP in organizations, and we suggest that one way to do that is to explicitly incorporate trust theory, that is, the components of trustworthiness, which represent antecedents of trust.

Costanza and Finkelstein (2015) question whether there really are generational differences in the workplace. They argue that the generation construct is not properly operationalized (often confounding age, period, and cohort factors) and that generational research is atheoretical and plagued by methodological problems. We agree that generational research is largely guilty as charged. We also share their concern regarding the generationally based interventions that are being sold to, and adopted by, managers—interventions that are often little more than fads based on popular myths and folklore. We are also in full agreement with the potential significant harm that can be associated with stereotyping groups of individuals.

Landers and Behrend (2015) present yet another attempt to limit reviewer and editor reliance on surface characteristics when evaluating the generalizability of study results (see also Campbell, 1986; Dipboye & Flanagan, 1979; Greenberg, 1987; Highhouse, 2009; Highhouse & Gillespie, 2009). Most of the earlier treatments of sample generalizability, however, have focused on the use of college students in (mostly) laboratory studies. Many industrial–organizational (I-O) scholars have experienced the hostility with which studies using students as participants receives. For instance, Jen Gillespie and I observed, “Reviewers and editors commonly assert that students should not be used to study workplace phenomena as though such a declaration requires no further explanation” (Highhouse & Gillespie, 2009, p. 247). The difference this time, however, is that Landers and Behrend (2015) are reacting to dismissals of research using Mechanical Turk (MTurk) workers to make inferences about behavior in organizations. Landers and Behrend (2015) make the important point that any research population is likely to be atypical on some dimensions and that all samples are samples of convenience (see also Oakes, 1972). We agree. Furthermore, we make two observations about MTurk: (a) We believe that it should be met with less resistance than student samples have historically faced, and (b) we suggest that it provides a unique opportunity to bring back randomized experimentation in I-O psychology.

One important issue not highlighted by Guzzo, Fink, King, Tonidandel, and Landis (2015) is that simply establishing construct validity will be significantly more challenging with big data than ever before. One needs to only look as far as the other social sciences analyzing big data (e.g., communications, economics, industrial engineering) to observe the difficulty of making valid claims as to what measured variables substantively “mean.” This presents a significant hurdle in the application of big data to organizational research questions because of the critical importance of demonstrating validity in the organizational sciences as highlighted by Guzzo et al.