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Post-Stratification
Stratification is a well-known sampling tool built on the premise that like units in a population should be treated similarly. It is a statistical fact that grouping similar units, when sampling, can generally reduce the variance of the survey estimates obtained. Stratification can be done when selecting units for study, or it can be carried out afterward. The latter application is usually termed post-stratification.
Illustration
To illustrate the differences between stratification and post-stratification, assume a researcher is interested in the total poundage of a population that consisted of 10,000 baby mice and one adult elephant. Suppose, further, that the average baby mouse weighted 0.2 pounds but the elephant weighted three and one-half tons or 7,000 pounds. This would mean, if the whole population were to be ...
- Ethical Issues in Survey Research
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- Roper, Elmo
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- Statistics Canada
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- U.S. Bureau of the Census
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- Null Hypothesis
- Outliers
- Panel Data Analysis
- Parameter
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- Post-Survey Adjustments
- Precision
- Probability
- p-Value
- Raking
- Random Assignment
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- Research Question
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- Significance Level
- Solomon Four-Group Design
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- STATA
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- Statistical Package for the Social Sciences (SPSS)
- Statistical Power
- SUDAAN
- Systematic Error
- Taylor Series Linearization
- Test-Retest Reliability
- Total Survey Error (TSE)
- t-Test
- Type I Error
- Type II Error
- Unbiased Statistic
- Validity
- Variable
- Variance
- Variance Estimation
- WesVar
- z-Score
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