sampling techniques in sample collection during research design

Research methodology sampling teachiques

1. Welcome to online Topic: 8. Sampling techniques- The nature of sampling, probability sampling design, non-probability sampling design, determination of sample size Resource Person Dr Basheer Ahmed Professor College of Education, Palamuru University, Mahabubnagar, TS Date : 12-10-2025

2. Keywords: Universe Population Subset Number of units Sample

3. Introduction to Sample A sample is a portion of people drawn from a larger population. It will be representative of the population only if it has same basic char- acteristics of the population from which it is drawn . Researchers must take sampling decisions early in the overall planning of a piece of research. Factors such as expense, time and accessibility frequently prevent researchers from gaining information from the whole population. Therefore they often need to be able to obtain data from a smaller group or subset of the total population in such a way that the knowledge gained is representative of the total population (however defined) under study. This smaller group or subset is the sample.

4. Introduction to Sample Experienced researchers start with the total population and work down to the sample. By contrast, less experienced researchers often work from the bottom up, that is, they determine the minimum number of respondents needed to conduct the research ( Bailey, 1994). However, unless they identify the total population in advance, it is virtually impossible for them to assess how representative the sample is that they have drawn.

5. Introduction to Sample A sample refers to a smaller, manageable version of a larger group. It is a subset containing the characteristics of a larger population. in other words, sampling is a process, which allows us to study a small group of people from the large group to derive inferences that are likely to be applicable to all the people of the large group. This is done as When a researcher conducts a research, it’s rarely possible to collect data from every person in that group or to study the whole population. Instead, the researcher selects a sample. Hence, a sample is the group of individuals who will actually participate in the research. In order to get a clearer picture we must first differentiate between population and a sample.

6. Sampling This is done as When a researcher conducts a research, it’s rarely possible to collect data from every person in that group or to study the whole population. Instead, the researcher selects a sample. Hence, a sample is the group of individuals who will actually participate in the research. In order to get a clearer picture we must first differentiate between population and a sample. The population is the entire group that a researcher wants to draw conclusions about. The sample is the specific group of individuals that a researcher will collect data from.

7. Factors of Sampling Decisions and problems such as these face researchers in deciding the sampling strategy to be used. Judgements have to be made about five key factors in sampling: 1. the sample size; 2. the representativeness and parameters of the sample; 3 .access to the sample; 4 .the sampling strategy to be used; 5.The kind of research that is being undertaken (e.g. quantitative/qualitative/mixed methods).

8. Sample Size A question that often plagues novice researchers is just how large their samples for the research should be. There is no clear-cut answer, for the correct sample size depends on the purpose of the study, the nature of the population under scrutiny, the level of accuracy required, the anticipated response rate, the number of variables that are included in the research, and whether the research is quantitative or qualitative. However, it is possible to give some advice on this matter. Gener -ally speaking, for quantitative research, the larger the sample the better, as this not only gives greater reliabil-ity but also enables more sophisticated statistics to be used.

9. Websites for sample size There are several websites that offer sample size calculation services for random samples. Some free sites at the time of writing are: www.surveysystem.com/sscalc.html; www.macorr.com/ss_calculator.html; www.raosoft.com/samplesize.html ; www.researchinfo.com/docs/calculators/samplesize.cfm ; www.nss.gov.au/nss/home.nsf/pages/Sample+Size+Calculator+Description?OpenDocument.com

10. Sampling strategy There are two main methods of sampling (Cohen and Holliday, 1979, 1982, 1996; Schofield, 1996). The researcher must decide whether to opt for a probability (also known as a random sample) or a non-probability sample (also known as a purposive  sample).

11. Simple random sampling In simple random sampling, each member of the popu-lation under study has an equal chance of being selected and the probability of a member of the population being selected is unaffected by the selection of other members of the population, i.e. each selection is entirely inde -pendent of the next. The method involves selecting at random from a list of the population (a sampling frame) the required number of subjects for the sample.

12. Systematic sampling This method is a modified form of simple random sam-pling . It involves selecting subjects from a population list in a systematic rather than a random fashion. For example, if from a population of, say, 2,000, a sample of 100 is required, then every twentieth person can be selected. The starting point for the selection is chosen at random . One can decide how frequently to make systematic sampling by a simple statistic - the total number of the wider population being represented divided by the sample size required : f= N/ sn Where, f=frequency interval N= the total number of the wider population sn = the required number in the sample

13. Let us say that the researcher is working with a school of 1,400 students; by looking at the table of sample size (Table 8.1) required for a random sample of these 1,400 students we see that 301 students are required to be in the sample. Hence the frequency interval (f) is: 1400/301=4.651 (which rounds up to 5.0) Hence the researcher would pick out every fifth name on the list of cases.

14. Random stratified sampling Random stratified sampling involves dividing the population into homogenous groups, each group containing subjects with similar characteristics. For example, group A might contain males and group B, females. In order to obtain a sample representative of the whole population in terms of sex, a random selection of sub-jects from group A and group B must be taken. If needed, the exact proportion of males to females in the whole population can be reflected in the sample. The researcher will have to identify those characteristics of the wider population which must be included in the sample, i.e. to identify the parameters of the wider population. This is the essence of establishing the sampling  frame.

15. Cluster sampling When the population is large and widely dispersed, gathering a simple random sample poses administra-tive problems. Suppose we want to survey students' fitness levels in a particularly large community or across a country. It would be completely impractical to select students randomly and spend an inordinate amount of time travelling about in order to test them. By cluster sampling, the researcher can select a spe-cific number of schools and test all the students in those selected schools, i.e. a geographically close cluster is sampled.

16. This sampling implies dividing population into clusters and drawing random sample either from all clusters or selected clusters. This method is used when (a) cluster criteria are significant for the study, and (b) economic considerations are significant . Initial clusters are called primary sampling units; clusters within the primary clusters are called secondary sampling units; and clusters within the secondary clusters are called multi-stage clusters. When clusters are geographic units, it is called area sampling. For example, dividing one city into various wards, each ward into areas, each area into each neighbourhoods and each neighbourhood  into lanes

17. Stage sampling Stage sampling is an extension of cluster sampling. It involves selecting the sample in stages, that is, taking samples from samples. Using the large community example in cluster sampling, one type of stage sampling might be to select a number of schools at random, and from within each of these schools, select a number of classes at random and from within those classes select a number of students.

18. The first stage is to list he 11 schools on a piece of paper and then to put the names of the 11 schools onto a small card and place each card in a hat. She draws out the first name of the school, puts a tally mark by the appropriate school on her list and returns the card to the hat. The process is repeated 321 times, bringing the total to 322. the final totals might appear thus: Schools 1 2 3 4 5 6 7 8 9 10 11 Total Required number of students 22 31 32 24 29 20 35 28 32 38 31 322

19. For the second stage she then approaches the 11 schools and asks each of them to select randomly the required number of student for each school. Randomness has been maintained in two stages and a large number(2000) has been rendered manageable. The process at work here is to go from the general to the specific, the wide to the focused, the large to the small. Caution has to be exercised here, as the assumption is that the schools are of the same size and are large; that may not be the case in practice, in which case this strategy may be inadvisable.

20. Multi-phase sampling In stage sampling there is a single unifying purpose throughout the sampling. In the previous example the purpose was to reach a particular group of students from a particular region. In a multi-phase sample the purposes change at each phase, for example, at phase one the selection of the 'sample might be based on the criterion of geography (e.g. students living in a particular region); phase two might be based on an economic criterion (e.g. schools whose budgets are administered in markedly different ways); phase three might be based on a political criterion (e.g. schools whose students are drawn from areas with a tradition of support for a particular political party), and so on. What is evident here is that the sample population will change at each phase of the research.

21. Non-probability samples Just as there are several types of probability sample, so there are several types of non-probability sample: convenience sampling, quota sampling, purposive sam-pling , dimensional sampling and snowball sampling. Each type of sample seeks only to represent itself or instances of itself in a similar population, rather than attempting to represent the whole, undifferentiated population.

22. Convenience sampling Convenience sampling or , as it is sometimes called, accidental or opportunity sampling - involves choosing the nearest individuals to serve as respondents and continuing that process until the required sample size has been obtained or those who happen to be available and accessible at the time. Captive audiences such as students or student teachers often serve as respondents based on convenience sampling. The researcher simply chooses the sample from those to whom she has easy access.

23. Quota sampling This is a version of stratified sampling with the difference that instead of dividing the population into strata and randomly choosing the re spondents , it works on 'quotas' fixed by the researcher. In the example of studying 50 MBA students from 150 students in five institutions , the researcher fixes the quota of 10 students from each institution, out of which five will be boys and five girls. The choice of the respondents is left to the interviewer. Determining quotas depends on a number of factors related to the nature and type of research. For instance, the researcher might decide to interview three boys out of five boys (from one MBA institution) from final year and two from previous year, or two studying the morning course (of two years) and three studying the evening course (of three years).

24. Quota can also be fixed according to their proportion in the en -tire population. For instance, for studying the attitudes of persons towards use of loudspeakers in religious places in one educational in- stitution with 100 males and 50 females belonging to different religions, quota can be fixed in the ratio of one female for every two males.

25. Purposive sampling In purposive sampling, often (but by no means exclusively ) a feature of qualitative research, researchers hand-pick the cases to be included in the sample on the basis of their judgement of their typicality or possession of the particular characteristics being sought. In this way, they build up a sample that is satisfactory to their specific needs . As its name suggests, a purposive sample has been chosen for a specific purpose, for example: (a) a group of principals and senior managers of secondary schools is chosen as the research is studying the incidence of stress amongst senior managers; (b) a group of disaffected students has been chosen because they might indicate most distinctly the factors which contribute to students' disaffection

26. Snowball sampling In snowball sampling researchers identify a small number of individuals who have the characteristics in which they are interested. These people are then used as informants to identify, or put the researchers in touch with, others who qualify for inclusion and these, in turn, identify yet others - hence the term snowball sam-pling (also known as 'chain-referral methods'). This method is useful for sampling a population where access is difficult, maybe because the topic for research (and hence the sample) is sensitive.

27. Snowball sampling is also useful where communication networks are undeveloped (e.g. where a researcher wishes to interview stand-in 'supply' teachers - teachers who are brought in on an ad hoc basis to cover for absent regular members of a school's teaching staff - but finds it difficult to acquire a list of these stand-in teachers), or where an outside researcher has difficulty in gaining access to schools (going through informal networks of friends/acquaintance and their friends and acquaintances and so on rather than through formal channels). The task for the researcher is to establish who are the critical or key informants with whom initial contact must be made.

28. Volunteer sampling In cases where access is difficult, thee researcher may have to rely on volunteers, for example, personal friends, or participants who reply to a newspaper advertisement, or those who happen to be interested from a particular school, or those attending courses. Sometimes this is inevitable(Morrison, 2006), as it is the only kind of sampling that is possible, and it is maybe better to have this kind of sampling than no research at all.

29. Theoretical sampling This is a feature of grounded theory. In grounded theory the sample size is relatively immaterial, as one works with the data that one has. Grounded sampling requires the researcher to have sufficient data to be able to generate and ‘ground’ th e theory in the research context.

30. Conclusion The message from this chapter is the same as for many of the others, namely that every element of the research should not be arbitrary but planned and delib-erate , and that, as before, the criterion of planning must be fitness for purpose. The selection of a sam-pling strategy must be governed by the criterion of suitability. The choice of which strategy to adopt must be mindful of the purposes of the research, the times- cales and constraints on the research, the research design, the methods of data collection and the meth- odology of the research. The sampling chosen must be appropriate for all these factors if validity is to be served.

31. To the question 'how large should my sample be?'. the answer is complicated. This chapter has suggested that it all depends on : the research purposes, questions and design; the population size ; the confidence level and confidence interval required ; the likely response rate ; the accuracy required (the smallest sampling error sought ); the kinds of variables to be used (categorical, continuous ); the statistics to be used ; the number of strata required;

32. the kind(s) of sample (different kinds of sample within probability, non-probability and mixed methods sampling ); the representativeness of the sample ; the allowances to be made for attrition and non-response ; the need to keep proportionality in a proportionate sample; the number of variables included in the study ; the variability of the factor under study ; the kind of research that is being undertaken ( qualitative/quantitative/mixed methods ). That said, this chapter has urged researchers to use large rather than small samples in quantitative research and sufficiently large and small samples to enable thick descriptions to be achieved

33. Exercise https://docs.google.com/forms/d/e/1FAIpQLScJGpMXjycwhfuEEIjB3DLV03MQVmuywlJFTBKTeDeYo-O3PA/viewform?usp=sf_link

34. Does anyone have any questions? Dr. Basheer Ahmed Principal, College Of Education, Palamuru University Thank You