Research 1: Sampling
- 1. Lesson 11: SAMPLING By Mikaine Liana Sia Arada
- 3. Definition Sampling is a word that refers to your method or process of selecting respondents or people to answer questions meant to yield data for a research study. The chosen ones constitute the sample through which you will derive facts and evidence to support the claims or conclusions propounded by your research problem. The bigger group from where you choose the sample is the population. Sampling frame is the term used to mean the list of the members of such population from where you will get the sample. 3
- 4. History 2 4
- 5. History The beginning of sampling could be traced back to the early political activities of the Americans in 1920 when Literary Digest did a pioneering survey about the American citizens’ favorite among the 1920 presidential candidates. This was the very first survey that served as the impetus for the discovery by academic researchers of other sampling strategies that they categorized into two classes: probability sampling or unbiased sampling and non- probability sampling. (Babbie 2013) 5
- 7. Probability Sampling or Unbiased Sampling Probability sampling involves all members listed in the sampling frame representing a certain population focused on by your study. An equal chance of participation in the sampling or selection process is given to every member listed in the sampling frame. By means of this unbiased sampling, you are able to obtain a sample that is capable of representing the population under study or of showing strong similarities in characteristics with the members of the population. 7
- 8. Probability Sampling or Unbiased Sampling A sampling error crops up if the selection does not take place in the way it is planned. Such sampling error is manifested by strong dissimilarity between the sample and the ones listed in the sampling frame. (P) How numerous the sampling errors are depends on the side of the sample. The smaller the sample is, the bigger the number sampling errors. Thus, choose to have a big sample of respondents to avoid sampling errors. However, deciding to increase the size of your sample is not so easy. There are these things you have to mull over in finalizing about this such as expenses for questionnaires and interview trips, interview schedules, and time for reading respondents' answers. 8
- 9. Probability Sampling or Unbiased Sampling The right sample size also depends on whether or not the group is heterogeneous or homogeneous. The first group requires a bigger size; the second, a smaller one. For a study of the field of social sciences requiring an in-depth investigation of something such as one involving the national government, the right sample size ranges from 1,000 to 1,500 or up to 2,500. On the other hand, hundreds, not thousands, of respondents suffice for a study about any local government unit. (Suter 2012; Emmel 2013) 9
- 11. SimpleRandom Sampling Systematic Sampling Stratified Sampling Cluster Sampling 1. SIMPLE RANDOM SAMPLING Simple random sampling is the best type of probability sampling through which you can choose sample from a population. Using a pure-chance selection, you assure every member the same opportunity to be in the sample. Here, the only basis of including or excluding a member is by chance or opportunity, not by any occurrence accounted for by cause-effect relationships. Simple random sampling happens through any of these two methods: (Burns 2012) 1) Have a list of all members of the population; write each name on a card through a pure-chance selection. 2) Have a list of all members; give a number to member and then use randomized or unordered numbers in selecting names from the list.
- 12. SimpleRandom Sampling Systematic Sampling Stratified Sampling Cluster Sampling 2. SYSTEMATIC SAMPLING For this kind of probability sampling, chance and system are the ones to determine who should compose the sample. For instance, if you want to have a sample of 1,500 students, take every 15th name on the list until you complete the total number of respondents to constitute your sample.
- 13. SimpleRandom Sampling Systematic Sampling Stratified Sampling Cluster Sampling 3. STRATIFIED SAMPLING The group comprising the sample is chosen in a way that such a group is liable to subdivision during the data analysis stage. A study needing group-by-group analysis finds stratified sampling the right probability sampling to use.
- 14. SimpleRandom Sampling Systematic Sampling Stratified Sampling Cluster Sampling 3. STRATIFIED SAMPLING This is a probability sampling that makes you isolate a set of persons instead of individual members to serve as sample members. For example, if you want to have a sample of 120 out of 1,000 students, you can randomly select three sections with 40 students each to constitute the sample.
- 16. Non- Probability Sampling Non-probability sampling disregards random selection of subjects. The subjects are chosen based on their availability or the purpose of the study, and in some cases, on the sole discretion of the researcher. This is not a scientific way of selecting respondents. Neither does it offer a valid or an objective way of detecting sampling errors. (Edmond 2013) 16
- 18. Quota Sampling Voluntary Sampling Purposeor Judgmental Sampling Availability Sampling Snowball Sampling 1. QUOTA SAMPLING You resort to quota sampling when you think you know the characteristics of the target population very well. In this case, you tend to choose sample members possessing or indicating the characteristics of the target population. Using a quota or specific set of persons whom you believed to have the characteristics of the target population involved in the study is your way of showing that the sample you have chosen closely represents the target population as regard such characteristics.
- 19. Quota Sampling Voluntary Sampling Purposeor Judgmental Sampling Availability Sampling Snowball Sampling 2. VOLUNTARY SAMPLING Since the subjects you expect to participate in the sample selection are the ones volunteering to constitute the sample, there is no need for you to do any selection process.
- 20. Quota Sampling Voluntary Sampling Purposeor Judgmental Sampling Availability Sampling Snowball Sampling 3. PURPOSIVE OR JUDGEMENTAL SAMPLING You choose people whom you are sure could correspond to the objectives of your study , like selecting those with the rich experience or interest in your study.
- 21. Quota Sampling Voluntary Sampling Purposeor Judgmental Sampling Availability Sampling Snowball Sampling 4. AVAILABILITY SAMPLING The willingness of a person as your subject to interact with you counts a lot in this non-probability sampling method. If during the data-collection time, you and counter people walking on a school campus, along corridors, and along the park or employees lining up at an office, and these people show willingness to respond to your questions, then you automatically consider them as your respondents.
- 22. Quota Sampling Voluntary Sampling Purposeor Judgmental Sampling Availability Sampling Snowball Sampling 5. SNOWBALL SAMPLING Similar to snow expanding wildly or rolling rapidly, this sampling method does not give a specific set of examples. This is true for a study involving unspecified group of people. Dealing with varied groups of people such as street children, mendicants, drug dependents call center workers, informal settlers, street vendors, and the like is possible in this kind of non-probability sampling. Free to obtain data from any group just like snow really expanding and accumulating at a certain place, you tend to increase the number of people you want to form the sample of your study. (Harding 2013)