Chapter 10 of comprehensive study on research methods
- 1. POPULATION  A population refers to the entire group of people, events, or things of interest that the researcher wishes to investigate.  It is the group of people, events or things of interest for which the researcher wants to make inference  Example: A researcher is trying to study the impact of social media on teenagers’ awareness in Pakistan. Then, all the teenagers in Pakistan will be population of the study.
- 2. ELEMENT  An element is a single member of the population  Example: A researcher is trying to study the impact of social media on teenagers’ awareness in Pakistan. Then, each teenager of Pakistan will be an element of the study.
- 3. SAMPLE  A sample is a subset of the population.  It comprises some members selected from it.  In other words, some, but not all, elements of the population form the sample.  Example: A researcher is trying to study the impact of social media on teenagers’ awareness in Pakistan. He/she draws 1500 teenagers from the population of teenagers, then, those 1599 teenagers will the sample of the study.
- 4. SUBJECT  A subject is a single member of the sample.  Example: A researcher is trying to study the impact of social media on teenagers’ awareness in Pakistan. He draws a sample of 1500. Each teenager of the sample will be a subject of the study.
- 5. THE SAMPLING PROCESS  Sampling is the process of selecting a sufficient number of the right elements from the population, so that a study of the sample and an understanding of its properties or characteristics make it possible for us to generalize such properties or characteristics to the population elements.  The major steps in sampling include: 1. Define the population 2. Determine the sampling frame 3. Determine the sampling design 4. Determine the appropriate sample size 5. Execute the sampling process
- 6. PROBABILITY SAMPLING Simple Random Sampling:  Every element in the population has a known and equal chance of being selected as a subject.  Let us say there are 1000 elements in the population, and we need a sample of 100. Suppose we were to drop pieces of paper in a hat, each bearing the name of one of the elements, and draw 100 of those from the hat with our eyes closed.  We know that the first piece drawn will have a 1/1000 chance of being drawn, the next one a 1/999 chance of being drawn, and so on.
- 7. PROBABILITY SAMPLING Restricted or Complex Probability Sampling:  These probability sampling procedures offer a viable, and sometimes more efficient, alternative to the unrestricted design we just discussed.  Efficiency is improved in that more information can be obtained for a given sample size using some of the complex probability sampling procedures than the simple random sampling design.  The five most common complex probability sampling designs – systematic sampling, stratified random sampling, cluster sampling, area sampling, and double sampling
- 8. PROBABILITY SAMPLING Systematic Sampling:  The systematic sampling design involves drawing every nth element in the population starting with a randomly chosen element between 1 and n. Stratified Random Sampling:  A process of stratification or segmentation, followed by random selection of subjects from each stratum.  The population is first divided into mutually exclusive groups that are relevant, appropriate, and meaningful in the context of the study.
- 9. PROBABILITY SAMPLING Cluster Sampling:  In cluster sampling, the target population is first divided into clusters.  Then, a random sample of clusters is drawn and for each selected cluster either all the elements or a sample of elements are included in the sample.  Cluster samples offer more heterogeneity within groups and more homogeneity among groups – the reverse of what we find in stratified random sampling, where there is homogeneity within each group and heterogeneity across groups.
- 10. PROBABILITY SAMPLING Double Sampling:  A sampling design where initially a sample is used in a study to collect some preliminary information of interest, and later a subsample of this primary sample is used to examine the matter in more detail, is called double sampling.
- 11. NONPROBABILITY SAMPLING Convenience Sampling:  It refers to the collection of information from members of the population who are conveniently available to provide it. Purposive Sampling:  Instead of obtaining information from those who are most readily or conveniently available, it might sometimes become necessary to obtain information from specific target groups.  The sampling here is confined to specific types of people who can provide the desired information, either because they are the only ones who have it, or they conform to some criteria set by the researcher.
- 12. NONPROBABILITY SAMPLING Quota Sampling:  Quota sampling, a second type of purposive sampling, ensures that certain groups are adequately represented in the study through the assignment of a quota.  Generally, the quota fixed for each subgroup is based on the total numbers of each group in the population. Snowball Sampling:  Snowball sampling is a non-probability sampling method where new units are recruited by other units to form part of the sample.
- 13. SAMPLE SIZE
- 14. SAMPLE SIZE
- 15. SAMPLE SIZE Rule of Thumb Roscoe (1975) proposes the following rules of thumb for determining sample size:  Sample sizes larger than 30 and less than 500 are appropriate for most research.  Where samples are to be broken into subsamples (males/females, juniors/seniors, etc.), a minimum sample size of 30 for each category is necessary.  In multivariate research (including multiple regression analyses), the sample size should be several times (preferably ten times or more) as large as the number of variables in the study.  For simple experimental research with tight experimental controls (matched pairs, etc.), successful research is possible with samples as small as 10 to 20 in size.