To obtain a stratified simple random sample, randomize within each strata via randtreat. Note that since some combinations of stratification variables may be more common than others, the strata sizes may vary. You can generate strata using the Stata command egen strata=group(A B C). 3 age groupings), you will have 18 distinct strata. 2 geographic groupings), and Variable C (i.e.

For example, if you are stratifying on Variable A (i.e. You may stratify with one or multiple variables as the number of variables increases, so does the number of strata. You may stratify on variables that you believe may significantly impact the outcome variable and/or on subgroups that you are particularly interested in evaluating. To stratify, first divide the target population into subgroups, or stratum. In general, stratified random sampling is essential for any evaluation that seeks to compare program impacts between subgroups. Stratified random sampling provides a solution to this scenario by balancing treatment and control across sub-populations and thus facilitating statistically significant comparisons across groups. In this case, even a large random sample would likely fail to contain a sufficient number of rural households to reliably estimate the rural-specific impact of the program. However, only 20 percent of households in Region Y are rural. In addition to measuring the overall impact of mobile money accounts on households in Region Y, you would like to measure the specific impact of mobile money accounts on rural households in Region Y. Say, for example, you are evaluating a mobile money program in Region Y. Second, it allows researchers to identify if and to what extent a program differentially impacts distinct groups of the population. First, it ensures that the sample and treatment groups are representative of the broader population. Stratification accomplishes two key goals. The Stata commands egen strata and randtreat are useful for stratification.Stratified random sampling is essential for any evaluation that seeks to compare program impacts between subgroups.Stratified random sampling ensures that sub-groups of a population are represented in the sample and in treatment groups.Time consuming and tedious & data need to be available for strata. Stratified Population = heterogeneous Highly representative, unbiased & can be inferred statistically.

Overview Sampling Techniques – Advantages & Disadvantages Sampling Types When to use it ?Ĭonditions Advantages Disadvantages Random Population elements = homogeneous on important parameters Highly representative, unbiased Time consuming and tedious, need complete data -set (may not be updated) Systematic Population elements = homogeneous on important parameters Easier than previous one & evenly distributed sample Less random than simple random sampling & may lack certain important trait. As per the concept you randomly pick 5 wards and then surveyed the people of 5 wards completely. so now you got to know that your city has 20 wards in which you have to conduct survey. If the city you decide to create zones and you found that certain regions are economically well and certain are extremely backward then try to form new zones in order to be fairer & more unbiased. One thing to keep in mind here is the cluster sampling would be more effective if we have homogeneity with other clusters i.e the cluster have similar features to each other. I hope you all know that our district/city has ward system (as in India), it may have been demarcated similarly in other countries by different system. You were asked to conduct a survey in your city you divided the locality into zones or let’s say you followed the way of wards i.e you decide to conduct sampling in the currently divided zones by the municipality i.e through the way of wards. In Cluster Sampling method we divide the population into clusters/groups/bunches and then select certain whole groups randomly and survey them all (present in the selected groups).

A bunch of grapes, A collection of cars etc.

Stratified vs simple random sampling