Correcting for Omitted-Variables and Measurement-Error Bias in Regression with an Application to the Effect of Lead on IQ
Ordinary least squares (OLS) regression estimates are biased, in general, when relevant variables are omitted from the regression equation or when included variables are measured with error. The errors-in-variables bias can be corrected using auxiliary information about unobservable measurement erro...
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| Veröffentlicht in: | Journal of the American Statistical Association 1998-06, Vol.93 (442), p.494-505 |
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| creator | Marais, M. Laurentius Wecker, William E. |
| description | Ordinary least squares (OLS) regression estimates are biased, in general, when relevant variables are omitted from the regression equation or when included variables are measured with error. The errors-in-variables bias can be corrected using auxiliary information about unobservable measurement errors. In this article we demonstrate how auxiliary information can also be used to correct for omitted-variables bias. We illustrate our methods with an application to four published studies of the effect on IQ of childhood exposure to lead. Each of the published studies used OLS methods (or equivalent). None of the studies includes a father IQ variable, and none accounts for the biasing effect of measurement error in the right-side variables. For each of the studies we demonstrate that bias-corrected estimates of the effect of lead on IQ are much reduced in size and are not significantly different from 0. Our methods can be used in other applications involving omitted variables or errors of measurement in the included variables. |
| doi_str_mv | 10.1080/01621459.1998.10473697 |
| format | Article |
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For each of the studies we demonstrate that bias-corrected estimates of the effect of lead on IQ are much reduced in size and are not significantly different from 0. 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None of the studies includes a father IQ variable, and none accounts for the biasing effect of measurement error in the right-side variables. For each of the studies we demonstrate that bias-corrected estimates of the effect of lead on IQ are much reduced in size and are not significantly different from 0. Our methods can be used in other applications involving omitted variables or errors of measurement in the included variables.</description><subject>Applications and Case Studies</subject><subject>Auxiliary information</subject><subject>Bias correction</subject><subject>Causality</subject><subject>Children</subject><subject>Coefficients</subject><subject>Confounding</subject><subject>Error rates</subject><subject>Errors in variables</subject><subject>Estimation bias</subject><subject>Estimators</subject><subject>Intelligence</subject><subject>Intelligence quotient</subject><subject>Intelligence tests</subject><subject>Learning disabilities</subject><subject>Point estimators</subject><subject>Pollution</subject><subject>Regression analysis</subject><subject>Regression coefficients</subject><subject>Standard deviation</subject><subject>Statistical analysis</subject><subject>Statistics</subject><issn>0162-1459</issn><issn>1537-274X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><recordid>eNqNkU1r3DAQhkVpodukf6HoUHpzqg9bso7bZdMGtoSUEHITY2ucKNjWVtIS9t9XZhvorZnLwOh5ZwQPIZ84u-CsZV8ZV4LXjbngxrRlVGupjH5DVryRuhK6vn9LVgtULdR78iGlJ1ZKt-2KHDchRuyznx_oECK9nnzO6Ko7iB66EROF2dGfCOkQccI5V9sYC_fNQ6J-pr_wIWJKPsz02efHQtP1fj_6HvIyy4HmR6TbYSg3aBjoDsHR8nB1c07eDTAm_Pi3n5Hby-3t5ke1u_5-tVnvqr5Wda60BNbwvoPGDahrDWgEN6B4rRtwEnTnul50LUeDTKJwhhlRWNU5gU7JM_LltHYfw-8Dpmwnn3ocR5gxHJKVrWJGN_JVoFK6fR0o9bJRncA-hpQiDnYf_QTxaDmzizr7os4u6uyLuhL8fAo-pRzivykhmbZCacZMU7D1CfNzUTfBc4ijsxmOY4hDhLn35TP_OfUHct6svw</recordid><startdate>19980601</startdate><enddate>19980601</enddate><creator>Marais, M. 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Laurentius</creatorcontrib><creatorcontrib>Wecker, William E.</creatorcontrib><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Journal of the American Statistical Association</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Marais, M. Laurentius</au><au>Wecker, William E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Correcting for Omitted-Variables and Measurement-Error Bias in Regression with an Application to the Effect of Lead on IQ</atitle><jtitle>Journal of the American Statistical Association</jtitle><date>1998-06-01</date><risdate>1998</risdate><volume>93</volume><issue>442</issue><spage>494</spage><epage>505</epage><pages>494-505</pages><issn>0162-1459</issn><eissn>1537-274X</eissn><abstract>Ordinary least squares (OLS) regression estimates are biased, in general, when relevant variables are omitted from the regression equation or when included variables are measured with error. The errors-in-variables bias can be corrected using auxiliary information about unobservable measurement errors. In this article we demonstrate how auxiliary information can also be used to correct for omitted-variables bias. We illustrate our methods with an application to four published studies of the effect on IQ of childhood exposure to lead. Each of the published studies used OLS methods (or equivalent). None of the studies includes a father IQ variable, and none accounts for the biasing effect of measurement error in the right-side variables. For each of the studies we demonstrate that bias-corrected estimates of the effect of lead on IQ are much reduced in size and are not significantly different from 0. Our methods can be used in other applications involving omitted variables or errors of measurement in the included variables.</abstract><pub>Taylor & Francis Group</pub><doi>10.1080/01621459.1998.10473697</doi><tpages>12</tpages></addata></record> |
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| ispartof | Journal of the American Statistical Association, 1998-06, Vol.93 (442), p.494-505 |
| issn | 0162-1459 1537-274X |
| language | eng |
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| source | JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing; Taylor & Francis:Master (3349 titles) |
| subjects | Applications and Case Studies Auxiliary information Bias correction Causality Children Coefficients Confounding Error rates Errors in variables Estimation bias Estimators Intelligence Intelligence quotient Intelligence tests Learning disabilities Point estimators Pollution Regression analysis Regression coefficients Standard deviation Statistical analysis Statistics |
| title | Correcting for Omitted-Variables and Measurement-Error Bias in Regression with an Application to the Effect of Lead on IQ |
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