A different way to solve the missing value problem: the case of equal employment opportunity data.
| dc.contributor.advisor | Smith, Paul | en_US |
| dc.contributor.author | zhou, jing | en_US |
| dc.contributor.department | Mathematical Statistics | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2006-09-12T05:51:30Z | |
| dc.date.available | 2006-09-12T05:51:30Z | |
| dc.date.issued | 2006-07-31 | en_US |
| dc.description.abstract | The purpose of this thesis is to review methods of imputation and apply them to data collected by Equal Employment Opportunity Commission (EEOC). First, I discuss several imputation methods and review theory of multiple imputation (MI). Next, I review aspects of missing data and outline an artificial data simulation. I describe simulation based on EEOC dataset listing numbers of employees by ethnicity in large establishments. Mean imputation and MI are applied to simulated datasets. In the first scenario, we impute data for nonresponding establishments. The more we impute, the higher our resulting population means. In the second scenario, we simulate item nonresponse. I find mean imputation and MI generate similar means. The means are not affected by percentage of missingness regardless of imputation methods. The results suggest MI produces larger standard error than mean imputation. Last the percentage of missingness has no effect on standard error in case of MI. | en_US |
| dc.format.extent | 651352 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1903/3830 | |
| dc.language.iso | en_US | |
| dc.subject.pqcontrolled | Statistics | en_US |
| dc.title | A different way to solve the missing value problem: the case of equal employment opportunity data. | en_US |
| dc.type | Thesis | en_US |
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