Measures of data quality are important for the evaluation and
improvement of survey design and procedures. A detailed investigation of the
sources, magnitude and impact of errors is necessary to identify how survey
design and procedures may be improved and how resources allocated more
efficiently among various aspects of the survey operation. A major part of this
thesis is devoted to the overview of statistical theory and methods for
measuring the contribution of response variability to the overall error of a
survey.
A very common practice in surveys is to select groups (clusters) of
elements together instead of independent selection of elements. In practice cluster samples tend to produce higher sampling variance for statistics than
element samples of the same size. Their frequent use stems from the desirable
cost features that they have.
Most data collection and sample designs involve some overlapping
between interviewer workload and the sampling units (clusters). For those
cases, a proportion of the measurement variance, which is due to interviewers,
is reflected to some degree in the sampling variance calculations.
The prime purpose in this thesis is to determine a variance formula that
decomposes the total variance into sampling and measurement variance
components for two commonly used data collection and sample designs. Once
such a decomposition is obtained, determining an optimum allocation in
existence of measurement errors would be possible.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/1206044/index.pdf |
| Date | 01 January 2003 |
| Creators | Akdemir, Deniz |
| Contributors | Ayhan, Oztas H. |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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