Relationships of Measurement Error and Prediction Error in Observed-Score Regression.
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| Title: | Relationships of Measurement Error and Prediction Error in Observed-Score Regression. |
|---|---|
| Authors: | Moses, Tim1 |
| Source: | Journal of Educational Measurement. Winter2012, Vol. 49 Issue 4, p380-398. 19p. 4 Black and White Photographs, 3 Charts. |
| Subject Terms: | *Statistical correlation, Measurement errors, Regression analysis, Simulation methods & models, Homoscedasticity |
| Abstract: | The focus of this paper is assessing the impact of measurement errors on the prediction error of an observed-score regression. Measures are presented and described for decomposing the linear regression's prediction error variance into parts attributable to the true score variance and the error variances of the dependent variable and the predictor variable(s). These measures are demonstrated for regression situations reflecting a range of true score correlations and reliabilities and using one and two predictors. Simulation results also are presented which show that the measures of prediction error variance and its parts are generally well estimated for the considered ranges of true score correlations and reliabilities and for homoscedastic and heteroscedastic data. The final discussion considers how the decomposition might be useful for addressing additional questions about regression functions' prediction error variances. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 84484561 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Relationships of Measurement Error and Prediction Error in Observed-Score Regression. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Moses%2C+Tim%22">Moses, Tim</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Winter2012, Vol. 49 Issue 4, p380-398. 19p. 4 Black and White Photographs, 3 Charts. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+errors%22">Measurement errors</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Homoscedasticity%22">Homoscedasticity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The focus of this paper is assessing the impact of measurement errors on the prediction error of an observed-score regression. Measures are presented and described for decomposing the linear regression's prediction error variance into parts attributable to the true score variance and the error variances of the dependent variable and the predictor variable(s). These measures are demonstrated for regression situations reflecting a range of true score correlations and reliabilities and using one and two predictors. Simulation results also are presented which show that the measures of prediction error variance and its parts are generally well estimated for the considered ranges of true score correlations and reliabilities and for homoscedastic and heteroscedastic data. The final discussion considers how the decomposition might be useful for addressing additional questions about regression functions' prediction error variances. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=84484561 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/j.1745-3984.2012.00182.x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 380 Subjects: – SubjectFull: Statistical correlation Type: general – SubjectFull: Measurement errors Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Homoscedasticity Type: general Titles: – TitleFull: Relationships of Measurement Error and Prediction Error in Observed-Score Regression. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Moses, Tim IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Winter2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 00220655 Numbering: – Type: volume Value: 49 – Type: issue Value: 4 Titles: – TitleFull: Journal of Educational Measurement Type: main |
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