Stratified random sampling is a type of probability sampling technique [see our article Probability sampling if you do not know what probability sampling is]. Unlike the simple random sample and the systematic random sample, sometimes we are interested in particular strata (meaning groups) within the population (e.g., males vs. females; houses vs. apartments, etc.) [see our article, Sampling: The basics, if you are unsure about the terms unit, sample, strata and population]. With the stratified random sample, there is an equal chance (probability) of selecting each unit from within a particular stratum (group) of the population when creating the sample. This article explains (a) what stratified random sampling is, (b) how to create a stratified random sample, and (c) the advantages and disadvantages (limitations) of stratified random sampling. Show
Stratified random sampling explainedImagine that a researcher wants to understand more about the career goals of students at the University of Bath. Let's say that the university has roughly 10,000 students. These 10,000 students are our population (N). In order to select a sample (n) of students from this population of 10,000 students, we could choose to use a simple random sample or a systematic random sample. However, sometimes we are interested in particular strata (groups) within the population. Therefore, the stratified random sample involves dividing the population into two or more strata (groups). These strata are expressed as H. For example, imagine we were interested in comparing the differences in career goals between male and female students at the University of Bath. If this was the case, we would want to ensure that the sample we selected had a proportional number of male and female students. This is known as proportionate stratification (as opposed to disproportionate stratification, where the sample size of each of the stratum is not proportionate to the population size of the same stratum). With stratified random sampling, there would an equal chance (probability) that each female or male student could be selected for inclusion in each stratum of our sample. However, in line with proportionate stratification, the total number of male and female students included in our sampling frame would only be equal if 5,000 students from the university were male and the other 5,000 students were female. Since this is unlikely to be the case, the number of units that should be selected for each stratum (i.e., the number of male and female students selected) will vary. We explain how this is achieved in the next section: Creating a stratified random sample. Creating a stratified random sampleTo create a stratified random sample, there are seven steps: (a) defining the population; (b) choosing the relevant stratification; (c) listing the population; (d) listing the population according to the chosen stratification; (e) choosing your sample size; (f) calculating a proportionate stratification; and (g) using a simple random or systematic sample to select your sample. STEP ONE |