![]() Sometimes you may need to use different methods to collect data from different subgroups.įor example, in order to lower the cost and difficulty of your study, you may want to sample urban subjects by going door-to-door, but rural subjects using mail. Allowing for a variety of data collection methods.In this case, stratified sampling allows for more precise measures of the variables you wish to study, with lower variance within each subgroup and therefore for the population as a whole. The scores are likely to be grouped by family income category. Lowering the overall variance in the populationĪlthough your overall population can be quite heterogeneous, it may be more homogenous within certain subgroups.įor example, if you are studying how a new schooling program affects the test scores of children, both their original scores and any change in scores will most likely be highly correlated with family income.With other methods of sampling, you might end up with a low sample size for certain subgroups because they’re less common in the overall population. If you want the data collected from each subgroup to have a similar level of variance, you need a similar sample size for each subgroup. ![]() It is theoretically possible (albeit unlikely) that this would not happen when using other sampling methods such as simple random sampling. It has several potential advantages:Ī stratified sample includes subjects from every subgroup, ensuring that it reflects the diversity of your population. Stratified sampling is the best choice among the probability sampling methods when you believe that subgroups will have different mean values for the variable(s) you’re studying. That means every member of the population can be clearly classified into exactly one subgroup. To use stratified sampling, you need to be able to divide your population into mutually exclusive and exhaustive subgroups.
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