Basic concepts of sampling (stratified sampling)
15 important questions on Basic concepts of sampling (stratified sampling)
How is an estimate variance constructed for stratified sampling?
What are 6 reasons for stratified sampling?
2. Avoid possibility of a really bad sample (mixed population of males and females).
3. The population is geographically diverse, stratification is used to organize the sample and data collection.
4. Sample data of known precision for subgroups.
5. Stratified sampling may lower the costs of the survey.
6. Stratified sampling often gives more accurate estimates of population means and totals.
What are two benefits of stratified sampling?
2. Improved precision of estimates, specifically if strata are chosen such that they are homogeneous with respect to the target variable.
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What are the risks of over- and understratification?
What is a design effect?
E.g. var(strat mean)/var(srs mean) = 0.83
0.83*300 = 250 observations to obtain same accuracy.
Are the mean and variance estimator of a stratified sample biased?
What are requirements for stratification variables?
2. It must be possible to select a sample in each stratum separately.
Stratification variables should be variables that have a strong relationship with the target variable (then, the variances in the strata are small).
What methods can be used to determine which grouping is the most effective (smallest variance) in the case of a quantitative grouping variable?
1. Frequency distribution
2. Square root of the product of the frequency (f) and the interval width (w) is computed for each value of the variable.
3. Values are grouped in such a way that the sum of the computed quantities is approximately the same in each group.
What is proportionate to size sampling?
Another view at proportionate to size sampling is that you make sure that the population size of the stratum divided by the total population size is equal to the sample size of the stratum divided by the total sample size:
Nh / N = nh / n
What happens to the weights of the strata when you sample proportionate to size?
What is optimal allocation?
What is a consequence of optimal allocation for the inclusion probabilities?
When is stratification inefficient?
When is stratification efficient?
Why does homogenous strata lead to a more precise estimator?
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