Stratified Sampling: In this form of sampling, the population is first divided into two or more mutually exclusive segments based on some categories of variables of interest in the research. It is designed to organize the population into homogenous subsets before sampling, then drawing a random sample within each subset. With stratified random sampling the population of N units is divided into subpopulations of units respectively. These subpopulations, called strata, are non-overlapping and together they comprise the whole of the population. When these have been determined, a sample is drawn from each, with a separate draw for each of the different strata. The sample sizes within the strata are denoted by respectively. If a SRS is taken within each stratum, then the whole sampling procedure is described as stratified random sampling. The primary benefit of this method is to ensure that cases from smaller strata of the population are included in sufficient numbers to allow comparison.
Its Merits include:
- Administrative convenience may dictate the use of stratification, for example, if an agency administering a survey may have regional offices, which can supervise the survey for a part of the population.
- Stratification may improve the estimates of characteristics of the whole population. It may be possible to divide a heterogeneous population into sub-populations, each of which is internally homogenous. If these strata are homogenous, i.e., the measurements vary little from one unit to another; a precise estimate of any stratum mean can be obtained from a small sample in that stratum. The estimate can then be combined into a precise estimate for the whole population.
- There is also a statistical advantage in the method, as a stratified random sample nearly always results in a smaller variance for the estimated mean or other population parameters of interest.
Demerits:Sampling problems may be inherent with certain sub populations, such as people living in institutions (e.g. hotels, hospitals, prisons).
Systematic Sampling: This method of sampling is at first glance very different from SRS. In practice, it is a variant of simple random sampling that involves some listing of elements - every nth element of list is then drawn for inclusion in the sample. Say you have a list of 10,000 people and you want a sample of 1,000. Creating such a sample includes three steps:
- Divide number of cases in the population by the desired sample size. In this example, dividing 10,000 by 1,000 gives a value of 10.
- Select a random number between one and the value attained in Step 1. In this example, we choose a number between 1 and 10 - say we pick 7.
- Starting with case number chosen in Step 2, take every tenth record (7, 17, 27, etc.). More generally, suppose that the N units in the population are ranked 1 to N in some order (e.g., alphabetic). To select a sample of n units, we take a unit at random, from the 1st k units and take every k-th unit thereafter.
The advantages of systematic sampling method over simple random sampling include:
- It is easier to draw a sample and often easier to execute without mistakes. This is a particular advantage when the drawing is done in the field.
- It stratifies the population into n strata, consisting of the 1st k units, the 2nd k units, and so on. Thus, we might expect the systematic sample to be as precise as a stratified random sample with one unit per stratum. The difference is that with the systematic one the units occur at the same relative position in the stratum Do whereas with the stratified, the position Sit in the stratum is determined separately a by randomization within each stratum.