STRATIFIED RANDOM SAMPLING – A representative number of subjects from various subgroups is randomly selected.
Suppose we wish to study computer use of educators in the Hartford system. Assume we want the teaching level (elementary, middle school, and high school) in our sample to be proportional to what exists in the population of Hartford teachers.
First we must determine what percentage of the teachers in the Hartford system are elementary, middle school, and high school. For this example, we will use 50%, 20% and 30% respectively. Because those percentages exist in our population, we want our sample to have the same percentages.
Let’s also assume that we want to sample 200 teachers. Since 50% of those teachers need to be elementary teachers, we need 100 elementary teachers in our sample (200 X .50). To achieve this, we obtain a list of all of the elementary teachers in the system. From that list we randomly select 100.
Similarly, we use a list of all of the middle school teachers and randomly select 40 (20% of 200). We do the same for the high school teachers and select 60.
The sample we selected is exactly proportional to the population with regards to teaching level. If we had not used STRATIFIED RANDOM SAMPLING we might have reached a similar proportion, or by chance, we might have had over representation of one of the groups.