Designing measurement studies under budget constraints: controlling error of measurement and power.
A methodology is presented for minimizing the mean error variance-covariance component in studies with resource constraints. When designing measurement studies, two important statistical issues—power and measurement error—must be considered. Power is concerned with the number of observations to emp...
| Publicado en: | Educational & Psychological Measurement Vol. 55; pp. 423 - 429 |
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| Formato: | Artículo |
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Sage Publications Inc.
June 1995
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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=508447470&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 508447470 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00131644 EPM jtl: Educational & Psychological Measurement issn: 00131644 maglogo: N pubinfo: dt: June 1995 vid: 55 pid: 344 pub: Sage Publications Inc. artinfo: ui: 508447470 10.1177/0013164495055003005 ppf: 423 ppct: 6 formats: tig: atl: Designing measurement studies under budget constraints: controlling error of measurement and power. aug: au: Marcoulides, George A. su: Test reliability Error analysis in mathematics Experimental design Statistical power analysis sug: subj: Test reliability Error analysis in mathematics Experimental design Statistical power analysis ab: A methodology is presented for minimizing the mean error variance-covariance component in studies with resource constraints. When designing measurement studies, two important statistical issues—power and measurement error—must be considered. Power is concerned with the number of observations to employ, and measurement error is concerned with the best method of allocating a fixed number of conditions to the different facets of observation. The mean error variance-covariance component is an estimate that takes into account both of these statistical issues. This method is illustrated with a one-facet multivariate design, and extensions to other designs are explored. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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