Empirical Standard Error Estimates
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of the health effects of air pollution (Ware et al.; 1984). The data analyzed are the 16 selected cases in Lipsitz et al. (1994). The binary response is the wheezing status of 16 children at ages 9, 10, 11, and 12 years. The mean response is modeled as a logistic regression model by using the explanatory use the empirical rule to estimate the standard deviation variables city of residence, age, and maternal smoking status at the particular age. The binary responses for individual
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children are assumed to be equally correlated, implying an exchangeable correlation structure. The data set and SAS statements that fit the model by the GEE method are
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as follows: data six; input case city$ @@; do i=1 to 4; input age smoke wheeze @@; output; end; datalines; 1 portage 9 0 1 10 0 1 11 0 1 12 0 0 2 kingston 9 1 1 10 2 1 11 2 0 12
Standard Error Regression Estimate
2 0 3 kingston 9 0 1 10 0 0 11 1 0 12 1 0 4 portage 9 0 0 10 0 1 11 0 1 12 1 0 5 kingston 9 0 0 10 1 0 11 1 0 12 1 0 6 portage 9 0 0 10 1 0 11 1 0 12 1 0 7 kingston 9 1 0 10 1 0 11 0 0 12 0 0 8 portage 9 1 0 10 1 0 11 1 0 12 2 0 9 portage 9 2 1 10 multiple standard error of estimate 2 0 11 1 0 12 1 0 10 kingston 9 0 0 10 0 0 11 0 0 12 1 0 11 kingston 9 1 1 10 0 0 11 0 1 12 0 1 12 portage 9 1 0 10 0 0 11 0 0 12 0 0 13 kingston 9 1 0 10 0 1 11 1 1 12 1 1 14 portage 9 1 0 10 2 0 11 1 0 12 2 1 15 kingston 9 1 0 10 1 0 11 1 0 12 2 1 16 portage 9 1 1 10 1 1 11 2 0 12 1 0 ; run; proc genmod data=six ; class case city ; model wheeze = city age smoke / dist=bin; repeated subject=case / type=exch covb corrw; run; The CLASS statement and the MODEL statement specify the model for the mean of the wheeze variable response as a logistic regression with city, age, and smoke as independent variables, just as for an ordinary logistic regression. The REPEATED statement invokes the GEE method, specifies the correlation structure, and controls the displayed output from the GEE model. The option SUBJECT=CASE specifies that individual subjects be identified in the input data set by the variable case. The SUBJECT= variable case must be listed in the CLASS statement. Measurements on individual subjects at ages 9, 10, 11, and 12 are in the proper order in the data set, so the WITHINSUBJECT= option is not required. The TYPE=EXCH option specifies an exchangeable working correlation structure, the COVB option specifies that the parameter estimate covariance matrix be displayed, and the CORRW option specifies that the final
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> Find a Publication Theoretical and Empirical Standard Errors for http://www.ets.org/research/policy_research_reports/publications/report/2008/hyfl Two Population Invariance Measures in the Linear Equating Case Author(s): von Davier, Alina A.; Manalo, Jonathan; Rijmen, Frank Publication Year: 2008 Report Number: RR-08-24 Source: ETS Research Report Document Type: Report Page Count: 23 Subject/Key Words: Jackknife Estimation Population Invariance Root Expected Mean Square Difference (REMSD) Root Mean Square standard error Difference (RMSD) Standard Error of Measurement Abstract The standard errors of the 2 most widely used population-invariance measures of equating functions, root mean square difference (RMSD) and root expected mean square difference (REMSD), are not derived from common equating methods such as linear equating. Consequently, it is unknown how standard error of much noise is contained in these estimates. This paper describes 2 methods for obtaining the standard errors for RMSD and REMSD. The delta method relies on an analytical approximation and provides asymptotic standard errors. The grouped jackknife method is a sampling-based method. Both methods were applied to a real data application. The results showed that there was very little difference between the standard errors found by the 2 methods. Read More Request Copy (specify title and report number, if any) http://dx.doi.org/10.1002/j.2333-8504.2008.tb02110.x Navigation for Research Home Navigation for Research HomeCapabilities and Services▼ Assessment Development and AnalysisAssessment ResearchPolicy Research Research Topics▼ Assessing People with Disabilities▼ Recent ResearchPublication Archive Automated Scoring and Natural Language Processing▼ SpeechWriting QualityWritten ContentMathematicsMeasurement and NLPEducational Applications CBAL InitiativeEnglish Language Learning and Assessment▼ U.S. K–12 EducationWorldwide Human Capital and Large-scale AssessmentReading for Understanding▼ Framework & Design PrinciplesAssessmentsResearch CollaborationPublications Statistics and Psychometrics▼ LegacyContinuous ImprovementNext Gene
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