Stratified Sampling. When sociologists decide on a sampling method, the aim is usually to try and make it as representative of the target population as possible. With stratified sampling, the sampling frame is divided up into various social groups (e.g. by age, social class, gender, ethnicity, etc.) and then random sampling is used for each Preparing to Stratify. In our example we want to resample the sample data to reflect the correct proportions of Gender and Home Ownership. The first thing we need to do is to create a single feature that contains all of the data we want to stratify on as follows …. Male, Home Mortgage 0.321737. Male, Rent 0.280076. Simple random samples involve the random selection of data from the entire population so each possible sample is equally likely to occur. In contrast, stratified random sampling divides the population into smaller groups, or strata, based on shared characteristics. A sample is a set of observations from the population. Stratified random sampling may have higher statistical accuracy than a simple random sample because you take specific subgroups into account. Cluster sampling. Similar to stratified random sampling, in cluster sampling, the researchers divide the total population into subgroups. However, this differs from stratified random sampling because , sampling fraction Definition: The stratified random sampling estimate yst of population mean Y is defined by k N h yh h=1 yst = N which is, generally, different for y unless N N h = n n h in which case we have “proportional allocation”. Theorem 1: For sampling without replacement (or with replacement) is an unbiased estimator of Y Stratified sampling has some disadvantages compared to simple random sampling, particularly when the population is homogeneous and has no clear subgroups. This type of sampling requires more time .

what is stratified random sampling