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From |
"Jason Becker" <[email protected]> |

To |
<[email protected]> |

Subject |
st: Accounting for measurement error in regression |

Date |
Wed, 20 Oct 2010 16:47:16 -0400 |

Hello, My data has measurement error which is generally modeled as following a Bernoulli distribution. The data are percentages of students at a school who score above a cutoff point on an exam, and the error is modeled as sqrt((p)*(q)/N) where p = percentage of students above the cutoff, q = percentage of students below the cutoff, and N is the number of students). Typically I've run regressions which assume that the percentage of students above this cutoff is the true value for each school. I'd like to take into account the error in assigning students to one category or another, but the methods described when I searched online (http://www.ats.ucla.edu/stat/stata/webbooks/reg/chapter4/statareg4.htm) are only applicable to constant measurement error. Is there some way for me to take this error into account in my model? I believe that if I calculate a regression without taking into account this error, it is inappropriate to apply the error bars around each observation after the fact because the model is constructed under the assumption that the sample data is accurate. It seems the common practice has been to construct 95% CI's around each observation after the regression is run to determine if a school meaningfully deviates from the regression estimate. I think the appropriate practice with a regression run without the error assumptions would be to construct an error region around the regression estimate itself and treat the school data as fixed. Thanks in advance for your help. Jason _____ Jason Becker Research Specialist Office of Data Analysis and Research Rhode Island Department of Education 255 Westminster Street Providence, RI 02903 (401)-222-8495 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Accounting for measurement error in regression***From:*Stas Kolenikov <[email protected]>

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