A Monte Carlo Evaluation of Tests for Comparing Dependent Correlations.
The authors conducted a Monte Carlo simulation of 8 statistical tests for comparing dependent zero-order correlations. In particular, they evaluated the Type I error rates and power of a number of test statistics for sample sizes (Ns) of 20, 50, 100, and 300 under 3 different population distribution...
| Publicado en: | Journal of General Psychology Vol. 130; no. 2; pp. 149 - 169 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
April 2003
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| Materias: | |
| 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=510238727&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 510238727 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00221309 JGP jtl: Journal of General Psychology issn: 00221309 maglogo: N pubinfo: dt: April 2003 vid: 130 iid: 2 pid: 58221 pub: Taylor & Francis Ltd artinfo: ui: 510238727 10.1080/00221300309601282 ppf: 149 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P size: 951KB tig: atl: A Monte Carlo Evaluation of Tests for Comparing Dependent Correlations. aug: au: Hittner, James B. May, Kim Silver, N. Clayton su: Statistical correlation Monte Carlo method Effect sizes (Statistics) sug: subj: Statistical correlation Monte Carlo method Effect sizes (Statistics) ab: The authors conducted a Monte Carlo simulation of 8 statistical tests for comparing dependent zero-order correlations. In particular, they evaluated the Type I error rates and power of a number of test statistics for sample sizes (Ns) of 20, 50, 100, and 300 under 3 different population distributions (normal, uniform, and exponential). For the Type I error rate analyses, the authors evaluated 3 different magnitudes of the predictor-criterion correlations (ρ{sub y, x1} = ρ{sub y, x2} = .1, .4, and .7). For the power analyses, they examined 3 different effect sizes or magnitudes of discrepancy between ρ{sub y, x1} and ρ{sub y, x2} (values of .1, .3, and .6). They conducted all of the simulations at 3 different levels of predictor intercorrelation (ρ{sub x1, x2} = .1, .3, and .6). The results indicated that both Type I error rate and power depend not only on sample size and population distribution, but also on (a) the predictor intercorrelation and (b) the effect size (for power) or the magnitude of the predictor-criterion correlations (for Type I error rate). When the authors considered Type I error rate and power simultaneously, the findings suggested that O. J. Dunn and V.A. Clark's (1969) z and E. J. Williams's (1959) t have the best overall statistical properties. The findings extend and refine previous simulation research and as such, should have greater utility for applied researchers. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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