Data Collection Methods and Sampling Methods

Cluster Sampling Cluster sampling, also called block sampling. In cluster sampling, the population that is being sampled is divided into groups called clusters. Instead of these subgroups being homogeneous based on selected criteria as in stratified sampling, a cluster is as heterogeneous as possible to matching the population. A random sample is then taken from within one or more selected clusters.

For example, if an organization has 30 small projects currently under development, an auditor looking for compliance to the coding standard might use cluster sampling to randomly select 4 of those projects as representatives for the audit and then randomly sample code modules for auditing from just those 4 projects. Cluster sampling can tell us a lot about that particular cluster, but unless the clusters are selected randomly and a lot of clusters are sampled, generalizations cannot always be made about the entire population.

For example, random sampling from all the source code modules written during the previous week, or all the modules in a particular subsystem, or all modules written in a particular language may cause biases to enter the sample that would not allow statistically valid generalization. Advantages uThere is no need to have a sampling frame for the whole population. uusually less costly comparing to random sampling such as stratified uresearcher can increase sample size with this technique Disadvantages Selection may be biased since the sampling is not random uTechnique is the least representative of the population uThis is also probability sampling with a possibility of high sampling error Quota Sampling In quota sampling, the population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgment is used to select the subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.

It is this second step which makes the technique one of non-probability sampling. In quota sampling the selection of the sample is non-random. For example interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for many years. Advantages uThere is no non-response situations There are low costs and convenience uPractical method of obtaining a sample if there is no sampling frame Disadvantages uIt is completely non random uBias is most likely to exist since interviewers determine the results uA lot of interviewer training and communication skills is needed to make the sample therefore it may be highly expensive Stratified Sampling The statistical sampling method called stratified sampling is used when representatives from each subgroup within the population need to be represented in the sample.

The first step in stratified sampling is to divide the population into subgroups (strata) based on mutually exclusive criteria. Random or systematic samples are then taken from each subgroup. The sampling fraction for each subgroup may be taken in the same proportion as the subgroup has in the population. For example, if the person conducting a customer satisfaction survey selected random customers from each customer type in proportion to the number of customers of that type in the population.

For example, if 40 samples are to be selected, and 10% of the customers are managers, 60% are users, 25% are operators and 5% are database administrators then 4 managers, 24 uses, 10 operators and 2 administrators would be randomly selected. Stratified sampling can also sample an equal number of items from each subgroup. For example, a development lead randomly selected three modules out of each programming language used to examine against the coding standard Advantages uReduced bias since it is random It is precise and effective since it targets the direct characteristics required in the target sample in strata levels Disadvantages uRequires an extensive sampling frame uThe strata levels of importance can only be subjectively selected uMore expensive than the quota and cluster since it requires more time and labor Systematic Sampling In this method, every nth element from the list is selected as the sample, starting with a sample element n randomly selected from the first k elements. For example, if the population has 1000 elements and a sample size of 100 is needed, then k would be 1000/100 = 10.

If number 7 is randomly selected from the first ten elements on the list, the sample would continue down the list selecting the 7th element from each group of ten elements. Care must be taken when using systematic sampling to ensure that the original population list has not been ordered in a way that introduces any non-random factors into the sampling. An example of systematic sampling would be if the auditor of the acceptance test process selected the 14th acceptance test case out of the first 20 test cases in a random list of all acceptance test cases to retest during the audit process.

The auditor would then keep adding twenty and select the 34th test case, 54th test case, 74th test case and so on to retest until the end of the list is reached Advantages uQuick to use uEasy to check for errors uCan be used where no sampling framework exists Disadvantages uBias can occur in cases of repetition of sets if population frames are possible uDifficult to use in exogenous population uThe process of selection can interact with a hidden periodic trait within the population Multistage Sampling