A dominant general factor (DGF) is present when a single factor accounts for the majority of reliable variance across a set of measures (Ree, Carretta, & Teachout, 2015). In the presence of a DGF, dimension scores necessarily reflect a blend of both general and specific factors. For some constructs, specific factors contain little unique reliable variance after controlling for the general factor (Reise, 2012), whereas for others, specific factors contribute a more substantial proportion of variance (e.g., Kinicki, McKee-Ryan, Schriesheim, & Carson, 2002). We agree with Ree et al. that the presence of a DGF has implications for interpreting scores. However, we argue that the conflation of general and specific factor variances has the strongest implications for understanding how constructs relate to external variables. When dimension scales contain substantial general and specific factor variance, traditional methods of data analysis will produce ambiguous or even misleading results. In this commentary, we show how several common data analytic methods, when used with data sets containing a DGF, will substantively alter conclusions.

Building on the focal article by Hyland, Lee, and Mills (2015), we propose conservation of resources (COR) theory (Hobfoll, 1989) as a framework that may explain why mindfulness contributes to work motivation and performance in an organizational context. We argue that mindfulness is especially beneficial in dynamic work contexts because it provides employees with a personal resource that makes them more resilient to the loss of job resources and more aware of alternative job resources in their changed work environment.

Neubert, Mainert, Kretzschmar, and Greiff (2015) argue that the variable and dynamic nature of new job roles requires employees to demonstrate skills such as complex problem solving (CPS) or collaborative problem solving (ColPS). We argue that assessing CPS and ColPS in place of traditional assessment would be criterion deficient. We argue that traditional personnel assessments are important even for 21st century jobs. In this commentary we attempt to (a) highlight the contribution of currently assessed individual predictors and job skills in relation to personnel selection and (b) discuss how those traditional assessments can inform performance even in dynamic environments, such as those experienced in team-based work.

Most organizations require some level of emphasis on sustained performance to survive. This is particularly true of publically traded organizations or those concerned with profit-and-loss accountability. In short, performance needs to be managed. As a result, performance management (PM) is a key practice in business and is often one of the primary areas of responsibility for industrial and organizational (I-O) practitioners in organizational settings. It is also a key lever for change in organization development (OD) interventions (e.g., Church, Rotolo, Shull, & Tuller, 2014), particularly when linked to specific behaviors that are being introduced and/or reinforced for the future success of the organization. Unfortunately, as Pulakos, Mueller Hanson, Arad, and Moye (2015) have noted, leaders and employees in most organizations fundamentally dislike PM. In fact, this dislike is so intense that it has resulted in professional conferences, workshops, and popular business books focused on the simplification, replacement, or even demise of the field (e.g., Culbert, 2010; Effron & Ort, 2010). There is even a PM book for dummies (Lloyd, 2009).

Since 2007, my work has involved helping companies to maximize the value of a technology system that supports strategic human resource processes, including performance management (PM). This technology is largely agnostic when it comes to PM process design. It can be configured for PM processes that make extensive ratings or avoid ratings entirely, encourage elaborate goal cascading or completely forego the use of goals, focus on once a year PM events or encourage ongoing employee–manager check-ins, and so forth. In sum, the system can be configured in any number of ways.

As pointed out by Hyland, Lee, and Mills (2015), the most prominent effect of mindfulness is a significant decrease in experienced stress levels, and one of the most popular mindfulness interventions is mindfulness-based stress reduction. When it comes to psychological stress, desirable outcomes are both expected and documented for employees who adopt a mindful approach to work. But how are these beneficial effects happening, exactly?

Neubert, Mainert, Kretzschmar, and Greiff (2015) suggest two individual differences that have been overlooked in the industrial–organizational (I-O) literature (i.e., complex and collaborative problem solving) will be critical in the 21st century workplace. Below, I will briefly summarize some history and empirical findings at odds with Neubert et al.’s (2015) assertions.

Ree, Carretta, and Teachout (2015) raise the need for further investigation into dominant general factors (DGFs) and their prevalence in measures used for the purposes of employee selection, development, and performance measurement. They imply that a method of choice for estimating the contribution of DGFs is principal components analysis (PCA), and they interpret the variance accounted for by the first component of the PCA solution as indicative of the contribution of a general factor. In this response, we illustrate the hazard of equating the first component of a PCA with a general factor, and we illustrate how this becomes particularly problematic when applying PCA to multifaceted variables. Rather than simply critique this use of PCA, we offer an alternative approach that helps to address and illustrate the problem that we raise.

In Industrial and Organizational Psychology volume 7, issue 4, a commentary to the focal article Oswald et al.1 was unfortunately omitted. This commentary, “Imperfect Corrections or Correct Imperfections? Psychometric Corrections in Meta-Analysis,” by Frederick L. Oswald, Seydahmet Ercan, Samuel T. McAbee, Jisoo Ock, and Amy Shaw,2 is reproduced in the following pages.

As Hyland, Lee, and Mills (2015) note, many conceptualizations of mindfulness include three characteristics. In particular, mindfulness is often defined as a state of consciousness in which an individual (a) focuses on the present moment, (b) attends to phenomena occurring both externally and internally, and (c) remains open to and accepting of observed stimuli—and thus avoids making judgments. Together, these characteristics grant insight into how mindfulness stands to improve performance in work settings. Just as directing attention to the work environment and the tasks and events associated with it can equip workers with key information for making decisions and help them identify and circumvent risks and threats, so too can attending to internal phenomena (e.g., emotions and intuitions) provide workers with useful inputs to the decisions they face (Dane, 2011). Furthermore, in refraining from making judgments and evaluations, workers are more likely to view unfolding events through a variety of lenses and are less likely to view their thoughts, feelings, and reactions as manifestations or indictments of who they are (Glomb, Duffy, Bono, & Yang, 2011). In line with these observations, researchers have hypothesized and provided evidence for positive relationships between mindfulness and the well-being and performance of workers (e.g., Allen & Kiburz, 2012; Dane & Brummel, 2014; Hülsheger, Alberts, Feinholdt, & Lang, 2013).

Being familiar with their earlier work investigating the factor structures of the Armed Services Vocational Aptitude Battery and the Air Force Officer Qualifying Test, we read with interest Ree, Carretta, and Teachout's (2015) proposal to extend the idea of a dominant general factor (DGF) beyond the realm of cognitive abilities to other areas of research and practice in industrial–organizational (I-O) psychology. We found their ideas intriguing and their arguments compelling, but we stumbled on a reference to an article of one of the present authors (Lance, Teachout, & Donnelly, 1992) and Ree et al.’s claim that Lance et al. (1992) had found a DGF that accounted for 59% of the variance in job performance ratings in a military job. They did not. Rather, Lance et al. reported a hierarchical confirmatory factor analysis (CFA) model that explained parsimoniously the correlations among 15 job performance first-order factors in terms of four Job Proficiency and four other Measurement Source second-order factors. We reasoned that Ree et al. must have conducted some secondary analysis on the results presented by Lance et al., and indeed we replicated their claim by finding that the first unrotated principal component accounted for 59% of the variance in correlations among the four Proficiency second-order factors reported in Lance et al.’s Table 6.

In an attempt to distill what we know about the effects of workplace mindfulness-based training, Hyland, Lee, and Mills (2015) cast a wide net with regard to the array of studies included in their review. For example, they include studies that investigate the benefits associated with workplace mindfulness training (e.g., Wolever et al., 2012) as well as training conducted for patients within primary care settings (e.g., Allen, Bromley, Kuyken, & Sonnenberg, 2009). In addition, their review includes studies based on self-reports of individual differences in mindfulness traits/skills (e.g., Hafenbrack, Kinias, & Barsade, 2014). Reviewing a broad cross-section of research is helpful to illustrate the wide-ranging nature of mindfulness research but also has the potential to obfuscate what we know about mindfulness as it pertains to workers and workplaces.

In the focal article, Ree, Carretta, and Teachout (2015) address a common error in research methods, in which researchers neglect the shared variance between facets of a multidimensional construct. We agree with the need to attend to the entire factor structure of constructs when using measures, whether in research or application. The objective of this commentary is to elaborate on useful practices when a dominant general factor (DGF), as defined by the focal article, is found to be present and, in particular, to explore cases of DGF results under research paradigms not considered by the focal article.

There is understandable concern by LeBreton, Scherer, and James (2014) that psychometric corrections in organizational research are nothing more than a form of statistical hydraulics. Statistical corrections for measurement error variance and range restriction might inappropriately ratchet observed effects upward into regions of practical significance and publication glory—at the expense of highly questionable results.

Ree, Carretta, and Teachout's (2015) arguments for recognizing the importance of general factors are mostly on point, but they neglect two broad issues: (a) an important theoretical problem introduced by the presence of multiple factors (general, group, specific) and (b) the criterion validity of group factors in certain settings.

Hyland, Lee, and Mills (2015) provide ample evidence regarding the value of mindfulness to individuals and the industrial–organizational community as a whole. However, as they noted, mindfulness programs and practices have not yet found widespread implementation among businesses and other organizations. Hyland et al. suggest two primary reasons for this. First, the move toward a nonjudgmental, presently aware, and accepting mind is a tall order for most individuals in our Western society who are heavily conditioned to be relatively self-interested, judgmental, and future oriented. Second, typical mindfulness programs (e.g., mindfulness based stress reduction; MBSR) may be too costly and time consuming for organizations.