Standard DA-I indicator 1.4 Objective 1: TSWBAT classify sampling techniques Objective 2: TSWBAT complete an activity related to the types of data

Sampling There are many reasons for selecting a sample rather than obtaining information from an entire population (a census). The most common is limited resources.

Sampling Techniques There are four sampling techniques: Random Sampling Systematic Sampling Stratified Sampling Cluster Sampling

Random Sampling It has the following properties: The population consists of N objects. The sample consists of n objects. If all possible samples of n objects are equally likely to occur, the sampling method is called random sampling.

Example In a medical study, the population might be all adults over age 50 who have high blood pressure in Marlboro County.  In a random sample, you may study about 20 adults selected in random process.

Systematic Sampling With systematic random sampling, we create a list of every member of the population. From the list, we randomly select the first sample element from the first k elements on the population list. Thereafter, we select every kth element on the list.

Example Suppose you want to sample 8 houses from a street of 120 houses. 120/8=15, so every 15th house is chosen after a random starting point between 1 and 15. If the random starting point is 11, then the houses selected are 11, 26, 41, 56, 71, 86, 101, and 116.

Stratified Sample With stratified sampling, the researcher divides the population into separate groups, called strata. Then, a probability sampling (often a simple random sample) is drawn from each group.

Example Suppose you want to take an opinion from MCHS students. At first, divide MCHS students into sub groups like freshmen, sophomores, juniors, and seniors. And then you make a sample by selecting some students from each sub group (strata).

Cluster Sampling With cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. The researcher conducts his analysis on data from the sampled clusters

Example Use cluster instead of strata.

Differences between Stratified Sampling and Cluster Sampling In cluster sampling, the naturally occurring groups are selected for being included in the sample. Its main use is in market research. In this method, the total population is divided into samples or groups after which, a sample of the groups is selected. Stratified sampling is a sampling method wherein the population is divided into several strata or categories and a sample is taken from each stratum. This method is very efficient, and it helps researchers get enough hints about specific groups in the population.