Pervasiveness of Dominant General Factors in Organizational Measurement

Ree, Malcolm James; Carretta, Thomas R.; Teachout, Mark S.

doi:10.1017/iop.2015.16

Cited by 44 publications

(40 citation statements)

References 68 publications

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“…Psychometrika, 70(1), 123-133.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). w i e r n i k e t a l .…”

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How Data Analysis Can Dominate Interpretations of Dominant General Factors

Wiernik

Wilmot

Kostal

2015

Ind. Organ. Psychol.

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438 b r e n ton m . w i e r n i k e t a l . van der Maas, H. L., Dolan, C. V., Grasman, R. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. . Cronbach's α, Revelle's β, and Mc-Donald's ω H: Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70(1), 123-133.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. Job satisfaction is a quintessential multidimensional construct with a DGF. It comprises several dimensions, each of which reflects attitudes toward different components of the job and all of which are simultaneously c hoic e of a na ly t ic m et hod 439

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How Data Analysis Can Dominate Interpretations of Dominant General Factors

Wiernik

Wilmot

Kostal

2015

Ind. Organ. Psychol.

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“…Other, less researched general factors reported in the psychology and related literatures include a general factor of human performance (Furnham, von Stumm, Makendrayogam, & Chamorro-Premuzic, 2009), job performance (Viswesvaran, Schmidt, & Ones, 2005), General factors and the cerebellum 8 academic achievement (Marsh, Byrne, & Shavelson, 1988), psychomotor functions (Carretta & Ree, 1997), motor competence (Utesch et al, 2016), physical fitness (Utesch, Dreiskamper, Strauss, & Naul, 2018), and numerous other constructs (Ree, Carretta, & Teachout, 2015).…”

Section: General Factors and The Cerebellummentioning

confidence: 99%

From general factors to response generalisation: the importance of the cerebellum for mental health and social effectiveness

Walsh¹

2020

Preprint

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Various psychometric general factor models exist to describe a diverse range of human psychological traits e.g. general factors of intelligence, personality and psychopathology. Historically, these latent traits have been viewed and studied as distinct constructs. However, accumulating evidence demonstrates empirical overlap, and conceptual proposals have been made arguing that collectively these traits form a marker of general social effectiveness. Here I argue that the cerebellum has an important, and previously neglected role in supporting diverse general factor psychological traits that enable such social effectiveness. In this conceptual article, I review evidence derived largely from neuroimaging studies, that these general traits (intelligence, personality, psychopathology and others) share overlapping neural correlates, and potentially shared underlying computations in the cerebellum. This is important as the cerebellum has an integrative role in skilled behaviour and psychological flexibility by enabling cognitive, affective and behavioural generalisation and adaptation across diverse environmental contexts. This article also discusses the importance of the cerebellum for understanding general factor development, given the early maturational timing of the cerebellum, and high sensitivity to early environmental influence. Finally, I propose a cerebellar role within the Hierarchical Taxonomy of Psychopathology (HiToP); an important framework for understanding of the latent transdiagnostic structure of psychopathology. If correct, consideration of the cerebellum can facilitate integration of psychometric and translational neuroscience perspectives to provide a broader and unified biopsychosocial understanding of psychological, and specifically mental health phenomena.

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“…Commentary on Ree, Carretta, and Teachout (2015) In Industrial and Organizational Psychology volume 8, issue 3, a commentary to the focal article Ree et al was unfortunately omitted. This commentary, "I've Found It, but What Does It Mean?…”

Section: Addendummentioning

confidence: 99%

The Importance of Sample Composition Depends on the Research Question

Gillespie

Brodke

et al. 2016

Ind. Organ. Psychol.

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Bergman and Jean (2016) compare published industrial and organizational (I-O) literature with labor statistics, demonstrating an underrepresentation of "workers" (i.e., "wage earners, laborers, first-line personnel, freelancers, contract workers") relative to managerial, professional, and executive positions. They note that one of four ways in which worker underrepresentation undermines the utility of I-O psychology research is that we could miss the role of worker status as a main effect on important variables and/or a moderator of key relationships, which could hinder understanding of important phenomena as they relate to workers. We applaud the emphasis on workers and agree with this basic premise.In order to draw valid inferences regarding the strength of an attitude or prevalence of an opinion-that is, generalizing a sample statistic to a larger population-workers indeed ought to be sampled if they are part of the population to which one is trying to generalize. However, when trying to generalize inferences about behavior, as in theory testing, the particular sample used might not be a critical concern unless (a) the research contains a specific and well-defined population of interest, (b) there are important differences in the motivation of respondents, and/or (c) there is reason to believe that features of the sample have a systematic influence on the relationships of interest (Highhouse & Gillespie, 2009, pp. 261-262).Therefore, for any given study, we might ask, Is there compelling rationale or evidence to suggest that worker status has a systematic influence on key variables or relationships? Assuming the field is unlikely to reverse publication trends enough to represent workers proportionally, more research is

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Pervasiveness of Dominant General Factors in Organizational Measurement

Cited by 44 publications

References 68 publications

How Data Analysis Can Dominate Interpretations of Dominant General Factors

How Data Analysis Can Dominate Interpretations of Dominant General Factors

From general factors to response generalisation: the importance of the cerebellum for mental health and social effectiveness

The Importance of Sample Composition Depends on the Research Question

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