A Multidimensional Approach for Evaluating Variables in Organizational Research and Practice

LeBreton, James M.; Hargis, Michael B.; Griepentrog, Brian K.; Oswald, Frederick L.; Ployhart, Robert E.

doi:10.1111/j.1744-6570.2007.00080.x

Cited by 177 publications

(194 citation statements)

References 31 publications

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“…As such, we additionally conducted relative weights analyses (Johnson, 2000) for each previously-described "model two" (i.e., big five traits and FTP; complete results of these analyses can be found in Table 7). Relative weights analyses are a valuable supplement to more traditional tests of incremental predictive effects, because the relative contribution of predictors to model R 2 cannot be accurately determined by examining regression weights alone (LeBreton, Hargis, Griepentrog, Oswald, & Ployhart, 2007;LeBreton, Ployhart, & Ladd, 2004). Considering the total amount of variance explained (R 2 ) across each of these models, the relative weights analyses suggest that between .94% (happiness; R 2 = .422) and 50.79% (GPA; R 2 = .086) of the explained variance in these outcome variables could be uniquely attributed to FTP.…”

Section: Incremental and Mediating Effects Of Future Time Perspectivementioning

confidence: 99%

Future time perspective: A systematic review and meta-analysis.

Kooij¹,

Kanfer²,

Betts³

et al. 2018

Journal of Applied Psychology

335

310

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Future time perspectiveKooij, Dorien; Kanfer, R.; Betts, M.; Rudolph, C.W. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.-Users may download and print one copy of any publication from the public portal for the purpose of private study or research -You may not further distribute the material or use it for any profit-making activity or commercial gain -You may freely distribute the URL identifying the publication in the public portal Take down policyIf you believe that this document breaches copyright, please contact us providing details, and we will remove access to the work immediately and investigate your claim. The ability to foresee, anticipate, and plan for future desired outcomes is crucial for wellbeing, motivation, and behavior. However, theories in organizational psychology do not incorporate time-related constructs such as Future Time Perspective (FTP), and research on FTP remains disjointed and scattered, with different domains focusing on different aspects of the construct, using different measures, and assessing different antecedents and consequences. In this review and meta-analysis, we aim to clarify the FTP construct, advance its theoretical development, and demonstrate its importance by: (a) integrating theory and empirical findings across different domains of research in order to identify major outcomes and antecedents of FTP, and (b) empirically examining whether and how these variables are moderated by FTP measures and dimensions. Results of a meta-analysis of k = 212 studies reveal significant relationships between FTP and major classes of consequences (i.e., those related to achievement, well-being, health behavior, risk behavior, and retirement planning), and between antecedents and FTP, as well as moderating effects of different FTP measures and dimensions. Highlighting the importance of FTP for organizational psychology theories, our findings demonstrate that FTP predicts these outcomes over-and-above the big five personality traits and mediates the associations between these personality traits and outcomes.Keywords: future time perspective; integrative review; meta-analysis; nomological network FUTURE TIME PERSPECTIVE META-ANALYSIS 3 Future Time Perspective: A Systematic Review and Meta-Analysis "We all have our time machines. Some take us back, they're called memories.Some take us forward, they're called dreams." (Irons, 1948).Time surrounds and embeds all human behavior (McGrath & Kelly, 1992). Time provides individuals with a benchmark for orienting the self in the midst of myriad ongoing activities in work and non-work life roles, such as learning, task performance, and parenting.Time also provides individuals with a point of reference (Mohammed & Nadkarni, 2011;Ringle & Savickas, 1983) and a salient metric by which to organize, construct, re...

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Section: Incremental and Mediating Effects Of Future Time Perspectivementioning

confidence: 99%

Future time perspective: A systematic review and meta-analysis.

Kooij¹,

Kanfer²,

Betts³

et al. 2018

Journal of Applied Psychology

335

310

View full text Add to dashboard Cite

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“…For this regression we calculated the general dominance weights for conscientiousness and procrastination using the procedure recommended by LeBreton et al (2007). The weights were .22 for conscientiousness and .08 for procrastination.…”

Section: Correlations With Coursework and Exam Marksmentioning

confidence: 99%

Conscientiousness and procrastination predict academic coursework marks rather than examination performance

Morris

Fritz

2015

Learning and Individual Differences

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Past research has reported a consistent but small relationship (e.g. r = .23) between conscientiousness and university academic performance. However, in almost all cases the nature of the academic work has not been divided into the major elements of coursework and examination performance. We examined the relationships between conscientiousness and procrastination and the coursework and examination performance of psychology students in their second and third year modules. Both conscientiousness (r = .45) and procrastination (r = -.39) were significant predictors of overall coursework marks and significantly predicted coursework marks for all but one of the individual modules. Correlations with examination marks were smaller and less consistent. Regression analysis showed that conscientiousness was the more dominant predictor than procrastination. These results extend the literature relating conscientiousness to academic performance, demonstrating that the relationship is stronger with coursework than with exams.

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“…The relative weight statistic has been shown to provide extremely good estimates of the relative importance of predictor variables when those predictor variables are correlated. This has been found in both simulation studies (LeBreton et al 2004b) and primary studies (LeBreton et al 2004a;LeBreton et al 2007). Recently, new developments in relative weight analysis have expanded its applications from traditional multiple regression to more complicated regression models such as multivariate multiple regression (LeBreton and Tonidandel 2008), regression models containing higher order terms such as cross-product terms, quadratic terms, or other polynomial terms , logistic regression (Tonidandel and LeBreton 2010), and multivariate analysis of variance (MANOVA; Tonidandel and LeBreton 2013).…”

Section: Relative Weight Analysismentioning

confidence: 99%

“…The column labeled ''Raw.RelWeight'' provides estimates of variable importance using the metric of relative effect sizes (LeBreton et al 2007). Specifically, these weights represent an additive decomposition of the total model R 2 and can be interpreted as the proportion of variance in job satisfaction that is appropriately attributed to each climate variable.…”

Section: #The Raw and Rescaled Weightsmentioning

confidence: 99%

RWA Web: A Free, Comprehensive, Web-Based, and User-Friendly Tool for Relative Weight Analyses

2014

Self Cite

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Over the last 15 years, a number of methodological developments have enabled researchers to draw more accurate inferences concerning the relative contribution (i.e., relative importance) among multiple (often correlated) predictor variables in a regression analysis. One such development has been relative weight analysis (RWA). Researchers can use a RWA to decompose the total variance predicted in a regression model (R 2 ) into weights that accurately reflect the proportional contribution of the various predictor variables. Prior to RWA, researchers were forced to rely on traditional statistics (e.g., correlations; standardized regression weights), which are known to yield faulty or misleading information concerning variable importance (especially when predictor variables are correlated with one another, which is often the case in organizational research). Although there has been a surge of interest in RWA over the last 10 years, integration of this statistical tool into organizational research has been hampered by the lack of a user-friendly statistical package for implementing RWA. Indeed, most popular statistical packages (e.g., SPSS, SAS) have yet to include RWA protocols into their regression modules. The purpose of this paper is to present a new, free, comprehensive, web-based, user-friendly resource, RWA-Web, which may be used by anyone having simple access to the internet. Our paper is structured as a tutorial on using RWA-Web to examine relative importance in the classic multiple regression model, the multivariate multiple regression model, and the logistic regression model. We also illustrate how RWAWeb may be used to conduct null hypothesis significance tests using advanced bootstrapping procedures.

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A Multidimensional Approach for Evaluating Variables in Organizational Research and Practice

Cited by 177 publications

References 31 publications

Future time perspective: A systematic review and meta-analysis.

Future time perspective: A systematic review and meta-analysis.

Conscientiousness and procrastination predict academic coursework marks rather than examination performance

RWA Web: A Free, Comprehensive, Web-Based, and User-Friendly Tool for Relative Weight Analyses

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