Consequently, stratified sampling would be preferred. In contrast, if the question of interest is "Do you agree or disagree that weather affects your performance during an athletic event?" The answer to this question would probably be influenced by whether or not the sport is played outside or inside. For instance, consider the question "Do you agree or disagree that you receive adequate attention from the team of doctors at the Sports Medicine Clinic when injured?" The answer to this question would probably not be team dependent, so cluster sampling would be fine. It would depend on what questions are being asked. With Example 2: Either stratified sampling or cluster sampling could be used.In this case, selecting 2 clusters from 4 possible clusters really does not provide many advantages over simple random sampling. Cluster sampling really works best when there are a reasonable number of clusters relative to the entire population. For example, the percentage of people watching a live sporting event on television might be highly affected by the time zone they are in. With Example 1: Stratified sampling would be preferred over cluster sampling, particularly if the questions of interest are affected by time zone.The following explanations add some clarification about when to use which method. However, there are obviously times when one sampling method is preferred over the other. (Eastern, Central, Mountain, Pacific.)Ģ time zones from the 4 possible time zonesĤ elementary schools from the l1 possible elementary schoolsĮvery person in the 2 selected time zonesĮvery student in the 4 selected elementary schoolsĮach of the three examples that are found in Tables 2.2 and 2.3 was used to illustrate how both stratified and cluster sampling could be accomplished. Examples of Cluster SamplesĪll elementary students in a local school districtĤ Time Zones in the U.S. Additionally, the statistical analysis used with cluster sampling is not only different but also more complicated than that used with stratified sampling. It is important to note that, unlike with the strata in stratified sampling, the clusters should be microcosms, rather than subsections, of the population. obtain data on every sampling unit in each of the randomly selected clusters.obtain a simple random sample of so many clusters from all possible clusters.divide the population into groups (clusters).(Eastern, Central, Mountain, Pacific)ġ1 different elementary schools in the local school districtĢ0 students from each of the 11 elementary schoolsĬluster Sampling is very different from Stratified Sampling. Examples of Stratified SamplesĪll elementary students in the local school districtĤ Time Zones in the U.S. Table 2.2 shows some examples of ways to obtain a stratified sample.
Under these conditions, stratification generally produces more precise estimates of the population percents than estimates that would be found from a simple random sample. Stratified sampling works best when a heterogeneous population is split into fairly homogeneous groups. collect data on each sampling unit that was randomly sampled from each group (stratum).obtain a simple random sample from each group (stratum).partition the population into groups (strata).Stratified Sampling is possible when it makes sense to partition the population into groups based on a factor that may influence the variable that is being measured. Of the five methods listed above, students have the most trouble distinguishing between stratified sampling and cluster sampling. Multistage Sampling (in which some of the methods above are combined in stages).The following sampling methods are examples of probability sampling: In probability sampling, it is possible to both determine which sampling units belong to which sample and the probability that each sample will be selected.
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