The accuracy of dominance analysis as a metric to assess relative importance: The joint impact of sampling error variance and measurement unreliability.
Dominance analysis (DA) has been established as a useful tool for practitioners and researchers to identify the relative importance of predictors in a linear regression. This article examines the joint impact of two common and pervasive artifacts-sampling error variance and measurement unreliability...
| Publicado en: | Journal of Applied Psychology Vol. 104; no. 4; pp. 593 - 604 |
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| Autores principales: | , , |
| Formato: | journal article |
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American Psychological Association
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=135619746&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 135619746 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00219010 JAY jtl: Journal of Applied Psychology issn: 00219010 maglogo: N pubinfo: dt: Apr2019 vid: 104 iid: 4 pid: 34 pub: American Psychological Association artinfo: ui: 135619746 10.1037/apl0000361 ppf: 593 ppct: 11 formats: tig: atl: The accuracy of dominance analysis as a metric to assess relative importance: The joint impact of sampling error variance and measurement unreliability. aug: au: Braun, Michael T. Converse, Patrick D. Oswald, Frederick L. affil: University of South Florida Florida Institute of Technology Rice University su: Social dominance Measurement errors Sampling errors Monte Carlo method Regression analysis sug: subj: Social dominance Measurement errors Sampling errors Monte Carlo method Regression analysis keyword: dominance analysis Monte Carlo simulation multiple regression predictor importance relative weight analysis dominance analysis Monte Carlo simulation multiple regression predictor importance relative weight analysis ab: Dominance analysis (DA) has been established as a useful tool for practitioners and researchers to identify the relative importance of predictors in a linear regression. This article examines the joint impact of two common and pervasive artifacts-sampling error variance and measurement unreliability-on the accuracy of DA. We present Monte Carlo simulations that detail the decrease in the accuracy of DA in the presence of these artifacts, highlighting the practical extent of the inferential mistakes that can be made. Then, we detail and provide a user-friendly program in R (R Core Team, 2017) for estimating the effects of sampling error variance and unreliability on DA. Finally, by way of a detailed example, we provide specific recommendations for how researchers and practitioners should more appropriately interpret and report results of DA. (PsycINFO Database Record (c) 2019 APA, all rights reserved). pubtype: Academic Journal doctype: journal article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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