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BRM 7- Selecting Samples (Lecture 2).ppt
- 1. Slide 7.1 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Chapter 6 Selecting Samples
- 2. Slide 7.2 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 By the end of this chapter you should: • understand the need for sampling in business and management research; • be aware of a range of probability and non-probability sampling techniques and the possible need to combine techniques within a research project; • be able to select appropriate sampling techniques for a variety of research scenarios and be able to justify their selection; • be able to use a range of sampling techniques; • be able to assess the representativeness of respondents; • be able to assess the extent to which it is reasonable to generalize from a sample; • be able to apply the knowledge, skills and understanding gained to your own research project. Learning outcomes
- 3. Slide 7.3 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Selecting samples Population, sample and individual cases Source: Saunders et al. (2009) Sampling means Selecting a particular group or sample to represent the entire population is called sampling
- 4. Slide 7.4 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 The need to sample Sampling- a valid alternative to a census when • A survey of the entire population is impracticable • Budget constraints restrict data collection • Time constraints restrict data collection • Results from data collection are needed quickly
- 5. Slide 7.5 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Overview of sampling techniques Sampling techniques Source: Saunders et al. (2009) Figure 7.2 Sampling techniques
- 6. Slide 7.6 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Probability sampling The four stage process 1. Identify sampling frame from research objectives 2. Decide on a suitable sample size 3. Select the appropriate technique and the sample 4. Check that the sample is representative • Random Sampling. • Each individual of population has a fixed, known & equal opportunity to be a part of the sample. • It is based on the randomization principle • This helps to reduce the possibility of bias for populations of less than 50 cases Henry (1990) advises against probability sampling.
- 7. Slide 7.7 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 1. Identify sampling frame from research objectives
- 8. Slide 7.8 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009
- 9. Slide 7.9 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Identifying a suitable sampling frame Key points to consider • Problems of using existing databases • Extent of possible generalisation from the sample • Validity and reliability • Avoidance of bias
- 10. Slide 7.10 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Sample size Choice of sample size is influenced by • Confidence needed in the data • Margin of error that can be tolerated • Margin of error (also called The confidence interval ) is the plus-or-minus figure usually reported in newspaper or television opinion poll results. For example, if you use a margin of error of 4 and 47% percent of your sample picks an answer you can be "sure" that if you had asked the question of the entire relevant population between 43% (47-4) and 51% (47+4) would have picked that answer. • Types of analyses to be undertaken • Size of the sample population and distribution
- 11. Slide 7.11 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009
- 12. Slide 7.12 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 The importance of response rate Key considerations • Non- respondents and analysis of refusals • Obtaining a representative sample • Calculating the active response rate • Estimating response rate and sample size
- 13. Slide 7.13 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Selecting a sampling technique Five main techniques used for a probability sample • Simple random • Systematic • Stratified random • Cluster • Multi-stage
- 14. Slide 7.14 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Simple random sampling • Number each of the cases in your sampling frame with a unique number. • Select cases using random numbers until, actual sample size is reached. In SRS, every cases in the population being samples has an equal chance of being chosen. It is an equal probability sampling method. It ensures the best result. It is best method theoretically but practically if population is larger then complete listing of population is difficult. • Numbers are assign to all subjects and then using a random number generate to chose random numbers.
- 15. Slide 7.15 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Step 1: Make a list Step 2: Assign a sequential number ( Sampling Frame) Step 3 : Figure out what your sample size is going to be Step4: Use any method of selection from following ( Random Number Tables, Lottery table, Throwing dies, Tossing of Coins)
- 16. Slide 7.16 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Systematic Random Sampling involves you selecting the sample at regular intervals from the sampling frame. • Number each of the cases in your sampling frame with a unique number. • Select the first case using a random number • Calculate the sampling fraction • Select subsequent cases systematically using the sampling fraction to determine the frequency of selection. • 2, 7, 12, 17, 22, 27, 32, 37
- 17. Slide 7.17 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Systematic Random Sampling • Systematic sampling is version of simple random sampling. • In systematic sampling, a list of population is made and then every nth element from the lst is chosen as a sample. • Systematic sampling has the potential to be biased if the list has been arranged in any type of order.
- 18. Slide 7.18 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Systematic Random Sampling
- 19. Slide 7.19 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Stratified random sampling • Stratified sampling involves dividing the potential samples into two or more mutually exclusive groups based on categories of interest in the research • Stratified sampling works best when a heterogeneous population is split into fairly homogeneous groups. • Choose the stratification variable or variables • Divide the sampling frame into the discrete strata(Stratum). • Number each of the cases within each stratum with a unique number • Select your sample using either simple random or systematic random sampling
- 20. Slide 7.20 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Stratified random sampling • Steps: 1. Partition the population into groups(Strata) 2. Obtain a simple random sample from each group 3. Collect data on each sampling unit that was randomly samples from each group(stratum)
- 21. Slide 7.21 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 4. Cluster Sampling • In this, randomly clusters are selected instead of individual samples in the first stage of sampling. • It is a way to randomly select participants from a list that is too large for simple random sampling. • This technique is used when no list of individual sample is available. • In this type of sampling clusters are made at the higher level and then sampling at subsequent levels is done until individual samples are reached (e.g, cluster may be a school, a team or village) • Choose the cluster grouping for your sampling frame. • Number each of the clusters with a unique number. • Select sample of clusters using random sampling
- 22. Slide 7.22 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 5. Multistager Sampling • When We do selection on so many stages then it is called multistage sampling
- 23. Slide 7.23 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Non- probability sampling • Non-random sampling • There is no specific chances of an individual to be a part of the sample. • All the individuals of the universe are not given an equal chance to be a part of the sample • There is no probability attached to the unit of the population • The selection of units depend on the subjective judgment of the researcher. • Selection depends on the assumption that the characteristics are evenly distributed within the population. • Therefore, the conclusions drawn by the sampler cannot be inferred from the sample to the whole population
- 24. Slide 7.24 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Non- probability sampling (2) Sampling techniques • Convenience sampling • Quota sampling (larger populations) • Purposive sampling • Snowball sampling • Self-selection sampling • Convenience sampling Point to consider Deciding on a suitable sample size Data saturation Selecting the appropriate technique
- 25. Slide 7.25 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 1. Convenience sampling • In Convenience sampling, the researcher select those units from the population which are accessible to the researcher • Subjects are chosen simply because they are easy to recruit. • This technique is considered easiest, cheapest and least time consuming
- 26. Slide 7.26 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 1. Consecutive sampling • A type of Convenience sampling, • The researcher picks a sample conduct research over a period of time, collect results and then moves on to another sample
- 27. Slide 7.27 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Quota Sampling • In quota sampling the researcher ensures equal or proportionate representation of subjects depending on which trait is considered as basis of the quota • The bases of the quota are usually age, gender, education, race, religion and socioeconomic status • For example, if basis of the quota is socioeconomic status and the researcher needs equal representation of each quota (Sample size 150) • (Upper class-Elite 30, Upper Middle Class 30 Lower Middle class 30, Working Class 30 Poor 30) • Divide the population into specific groups. • Calculate quota for each group based on relevant and available data • Collect data from each quota
- 28. Slide 7.28 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 3. Judgment or (Purposive sampling • Subjects are chosen to be part of the sample with a specific purpose in mind. • With judgmental sampling, the researcher believes that some subjects are more fit for the research compared other individuals. • This is the reason why they are purposively chosen as subjects. • • Extreme case/deviant sampling: unusual or special case enable to learn the most about the RQ. • Heterogeneous or maximum variation sampling: representing different subgroups • Homogeneous sampling: One subgroup. :
- 29. Slide 7.29 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 4. Snowball sampling • Make contact with one or two cases in the population. • Ask these cases to identify further cases. • Ask these new case to identify further new cases. • Stop when either no new cases are given or the sample is large enough.
- 30. Slide 7.30 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Self select sampling • Publicize your need for cases • Collect data from those who respond
- 31. Slide 7.31 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Convenience sampling • Also called purposive or availability sampling. • Select case based on ease or convenience.
- 32. Slide 7.32 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Key Points • Choice of sampling techniques depends upon the research question(s) and their objectives • Factors affecting sample size include: - confidence needed in the findings - accuracy required - likely categories for analysis
- 33. Slide 7.33 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Key Points • Probability sampling requires a sampling frame and can be more time consuming • When a sampling frame is not possible, non- probability sampling is used • Many research projects use a combination of sampling techniques
- 34. Slide 7.34 Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009 Conclusion All choices depend on the ability to gain access to organisations
Editor's Notes
- Those discussed in this chapter are highlighted in Figure 7.2. With probability samples the chance, or probability, of each case being selected from the population is known and is usually equal for all cases. This means that it is possible to answer research questions and to achieve objectives that require you to estimate statistically the characteristics of the population from the sample. Consequently, probability sampling is often associated with survey and experimental research strategies (Section 5.3). For non-probability samples, the probability of each case being selected from the total population is not known and it is impossible to answer research questions or to address objectives that require you to make statistical inferences about the characteristics of the population. You may still be able to generalise from non-probability samples about the population, but not on statistical grounds. However, with both types of sample you can answer other forms of research questions, such as ‘What job attributes attract people to jobs?’ or ‘How are financial services institutions adapting the services they provide to meet recent legislation?’
- Deciding on a suitable sample size Generalisations about populations from data collected using any probability sample are based on statistical probability. The larger your sample’s size the lower the likely error in generalising to the population. Probability sampling is therefore a compromise between the accuracy of your findings and the amount of time and money you invest in collecting, checking and analysing the data. Your choice of sample size within this compromise is governed by: • the confidence you need to have in your data – that is, the level of certainty that the characteristics of the data collected will represent the characteristics of the total population; • the margin of error that you can tolerate – that is, the accuracy you require for any estimates made from your sample; • the types of analyses you are going to undertake – in particular, the number of categories into which you wish to subdivide your data, as many statistical techniques have a minimum threshold of data cases for each cell (e.g. chi square, Section 12.5); and to a lesser extent: • the size of the total population from which your sample is being drawn. Given these competing influences, it is not surprising that the final sample size is almost always a matter of judgement as well as of calculation. For many research questions and objectives, your need to undertake particular statistical analyses (Section 12.5) will determine the threshold sample size for individual categories. In particular, an examination of virtually any statistics textbook (or Sections 12.3 and 12.5 of this book) will highlight that, in order to ensure spurious results do not occur, the data analysed must be normally distributed. Whilst the normal distribution is discussed in Chapter 12, its implications for sample size need to be considered here. Statisticians have proved that the larger the absolute size of a sample, the more closely its distribution will be to the normal distribution and thus the more robust it will be. This relationship, known as the central limit theorem, occurs even if the population from which the sample is drawn is not normally distributed. Statisticians have also shown that a sample size of 30 or more will usually result in a sampling distribution for the mean that is very close to a normal distribution. For this reason, Stutely’s (2003) advice of a minimum number of 30 for statistical analyses provides a useful rule of thumb for the smallest number in each category within your overall sample. Where the population in the category is less than 30, and you wish to undertake your analysis at this level of detail, you should normally collect data from all cases in that category. Alternatively, you may have access to an expert system such as Ex-Sample™. This software calculates the minimum sample size required for different statistical analyses as well as the maximum possible sample size given resources such as time, money and response rates. In addition, it provides a report justifying the sample size calculated (Idea Works 2008).
- Systematic sampling involves you selecting the sample at regular intervals from the sampling frame. To do this you: 1 Number each of the cases in your sampling frame with a unique number. The first case is numbered 0, the second 1 and so on. 2 Select the first case using a random number. 3 Calculate the sampling fraction. 4 Select subsequent cases systematically using the sampling fraction to determine the frequency of selection. To calculate the sampling fraction – that is, the proportion of the total population that you need to select – you use the formula sampling fraction = actual sample size total population If your sampling fraction is 1⁄3 you need to select one in every three cases – that is, every third case from the sampling frame. Unfortunately, your calculation will usually result in a more complicated fraction. In these instances it is normally acceptable to round your population down to the nearest 10 (or 100) and to increase your minimum sample size until a simpler sampling fraction can be calculated.
- Stratified random sampling is a modification of random sampling in which you divide the population into two or more relevant and significant strata based on one or a number of attributes. In effect, your sampling frame is divided into a number of subsets. A random sample (simple or systematic) is then drawn from each of the strata. Consequently, stratified sampling shares many of the advantages and disadvantages of simple random or systematic sampling. Dividing the population into a series of relevant strata means that the sample is more likely to be representative, as you can ensure that each of the strata is represented proportionally within your sample. However, it is only possible to do this if you are aware of, and can easily distinguish, significant strata in your sampling frame. In addition, the extra stage in the sampling procedure means that it is likely to take longer, to be more expensive, and to be more difficult to explain than simple random or systematic sampling. In some instances, as pointed out by deVaus (2002), your sampling frame will already be divided into strata. A sampling frame of employee names that is in alphabetical order will automatically ensure that, if systematic sampling is used (discussed earlier), employees will be sampled in the correct proportion to the letter with which their name begins. Similarly, membership lists that are ordered by date of joining will automatically result in stratification by length of membership if systematic sampling is used. However, if you are using simple random sampling or your sampling frame contains periodic patterns, you will need to stratify it. To do this you: 1 Choose the stratification variable or variables. 2 Divide the sampling frame into the discrete strata. Number each of the cases within each stratum with a unique number, as discussed earlier. 4 Select your sample using either simple random or systematic sampling, as discussed earlier. The stratification variable (or variables) chosen should represent the discrete characteristic (or characteristics) for which you want to ensure correct representation within the sample (Box 7.8). Samples can be stratified using more than one characteristic. You may wish to stratify a sample of an organisation’s employees by both department and salary grade. To do this you would: 1 divide the sampling frame into the discrete departments. 2 Within each department divide the sampling frame into discrete salary grades. 3 Number each of the cases within each salary grade within each department with a unique number, as discussed earlier. 4 Select your sample using either simple random or systematic sampling, as discussed earlier. Box 7.8 Focus on student research Stratified random sampling Dilek worked for a major supplier of office supplies to public and private organisations. As part of her research into her organisation’s customers, she needed to ensure that both public and private-sector organisations were represented correctly. An important stratum was, therefore, the sector of the organisation. Her sampling frame was thus divided into two discrete strata: public sector and private sector. Within each stratum, the individual cases were then numbered: Public sector stratum Private sector stratum Number Customer Selected Number Customer Selected 000 Anyshire County Council 000 ABC Automotive manufacturer 001 Anyshire Hospital Trust ✓ 001 Anytown printers and bookbinders 002 Newshire Army Training Barracks 002 Benjamin Toy Company 003 Newshire Police Force 003 Jane’s Internet Flower shop ✓ 004 Newshire Housing 004 Multimedia productions 005 St Peter’s Secondary School ✓ 005 Roger’s Consulting 006 University of Anytown 006 The Paperless Office ✓ 007 West Anyshire Council 007 U-need-us Ltd She decided to select a systematic sample. A sampling fraction of 1⁄4 meant that she needed to select every fourth customer on the list. As indicated by the ticks (✓), random numbers were used to select the first caseNumber each of the cases within each stratum with a unique number, as discussed earlier. 4 Select your sample using either simple random or systematic sampling, as discussed earlier. The stratification variable (or variables) chosen should represent the discrete characteristic (or characteristics) for which you want to ensure correct representation within the sample (Box 7.8). Samples can be stratified using more than one characteristic. You may wish to stratify a sample of an organisation’s employees by both department and salary grade. To do this you would: 1 divide the sampling frame into the discrete departments. 2 Within each department divide the sampling frame into discrete salary grades. 3 Number each of the cases within each salary grade within each department with a unique number, as discussed earlier. 4 Select your sample using either simple random or systematic sampling, as discussed Earlier.
- Stratified random sampling is a modification of random sampling in which you divide the population into two or more relevant and significant strata based on one or a number of attributes. In effect, your sampling frame is divided into a number of subsets. A random sample (simple or systematic) is then drawn from each of the strata. Consequently, stratified sampling shares many of the advantages and disadvantages of simple random or systematic sampling. Dividing the population into a series of relevant strata means that the sample is more likely to be representative, as you can ensure that each of the strata is represented proportionally within your sample. However, it is only possible to do this if you are aware of, and can easily distinguish, significant strata in your sampling frame. In addition, the extra stage in the sampling procedure means that it is likely to take longer, to be more expensive, and to be more difficult to explain than simple random or systematic sampling. In some instances, as pointed out by deVaus (2002), your sampling frame will already be divided into strata. A sampling frame of employee names that is in alphabetical order will automatically ensure that, if systematic sampling is used (discussed earlier), employees will be sampled in the correct proportion to the letter with which their name begins. Similarly, membership lists that are ordered by date of joining will automatically result in stratification by length of membership if systematic sampling is used. However, if you are using simple random sampling or your sampling frame contains periodic patterns, you will need to stratify it. To do this you: 1 Choose the stratification variable or variables. 2 Divide the sampling frame into the discrete strata. Number each of the cases within each stratum with a unique number, as discussed earlier. 4 Select your sample using either simple random or systematic sampling, as discussed earlier. The stratification variable (or variables) chosen should represent the discrete characteristic (or characteristics) for which you want to ensure correct representation within the sample (Box 7.8). Samples can be stratified using more than one characteristic. You may wish to stratify a sample of an organisation’s employees by both department and salary grade. To do this you would: 1 divide the sampling frame into the discrete departments. 2 Within each department divide the sampling frame into discrete salary grades. 3 Number each of the cases within each salary grade within each department with a unique number, as discussed earlier. 4 Select your sample using either simple random or systematic sampling, as discussed earlier. Box 7.8 Focus on student research Stratified random sampling Dilek worked for a major supplier of office supplies to public and private organisations. As part of her research into her organisation’s customers, she needed to ensure that both public and private-sector organisations were represented correctly. An important stratum was, therefore, the sector of the organisation. Her sampling frame was thus divided into two discrete strata: public sector and private sector. Within each stratum, the individual cases were then numbered: Public sector stratum Private sector stratum Number Customer Selected Number Customer Selected 000 Anyshire County Council 000 ABC Automotive manufacturer 001 Anyshire Hospital Trust ✓ 001 Anytown printers and bookbinders 002 Newshire Army Training Barracks 002 Benjamin Toy Company 003 Newshire Police Force 003 Jane’s Internet Flower shop ✓ 004 Newshire Housing 004 Multimedia productions 005 St Peter’s Secondary School ✓ 005 Roger’s Consulting 006 University of Anytown 006 The Paperless Office ✓ 007 West Anyshire Council 007 U-need-us Ltd She decided to select a systematic sample. A sampling fraction of 1⁄4 meant that she needed to select every fourth customer on the list. As indicated by the ticks (✓), random numbers were used to select the first caseNumber each of the cases within each stratum with a unique number, as discussed earlier. 4 Select your sample using either simple random or systematic sampling, as discussed earlier. The stratification variable (or variables) chosen should represent the discrete characteristic (or characteristics) for which you want to ensure correct representation within the sample (Box 7.8). Samples can be stratified using more than one characteristic. You may wish to stratify a sample of an organisation’s employees by both department and salary grade. To do this you would: 1 divide the sampling frame into the discrete departments. 2 Within each department divide the sampling frame into discrete salary grades. 3 Number each of the cases within each salary grade within each department with a unique number, as discussed earlier. 4 Select your sample using either simple random or systematic sampling, as discussed Earlier.
- Cluster sampling is, on the surface, similar to stratified sampling as you need to divide the population into discrete groups prior to sampling (Henry 1990). The groups are termed clusters in this form of sampling and can be based on any naturally occurring grouping. For example, you could group your data by type of manufacturing firm or geographical area (Box 7.9). For cluster sampling your sampling frame is the complete list of clusters rather than a complete list of individual cases within the population. You then select a few clusters, normally using simple random sampling. Data are then collected from every case within the selected clusters. The technique has three main stages: 1 choose the cluster grouping for your sampling frame. 2 Number each of the clusters with a unique number. The first cluster is numbered 0, the second 1 and so on. 3 Select your sample using some form of random sampling as discussed earlier. Selecting clusters randomly makes cluster sampling a probability sampling technique. Despite this, the technique normally results in a sample that represents the total population less accurately than stratified random sampling. Restricting the sample to a few relatively compact geographical sub-areas (clusters) maximises the amount of data you can collect using face to face methods within the resources available. However, it may also reduce the representativeness of your sample. For this reason you need to maximise the number of sub-areas to allow for variations in the population within the available resources. Your choice is between a large sample from a few discrete sub-groups and a smaller sample distributed over the whole group. It is a trade-off between the amount of precision lost by using a few sub-groups and the amount gained from a larger sample size.
- Cluster sampling is, on the surface, similar to stratified sampling as you need to divide the population into discrete groups prior to sampling (Henry 1990). The groups are termed clusters in this form of sampling and can be based on any naturally occurring grouping. For example, you could group your data by type of manufacturing firm or geographical area (Box 7.9). For cluster sampling your sampling frame is the complete list of clusters rather than a complete list of individual cases within the population. You then select a few clusters, normally using simple random sampling. Data are then collected from every case within the selected clusters. The technique has three main stages: 1 choose the cluster grouping for your sampling frame. 2 Number each of the clusters with a unique number. The first cluster is numbered 0, the second 1 and so on. 3 Select your sample using some form of random sampling as discussed earlier. Selecting clusters randomly makes cluster sampling a probability sampling technique. Despite this, the technique normally results in a sample that represents the total population less accurately than stratified random sampling. Restricting the sample to a few relatively compact geographical sub-areas (clusters) maximises the amount of data you can collect using face to face methods within the resources available. However, it may also reduce the representativeness of your sample. For this reason you need to maximise the number of sub-areas to allow for variations in the population within the available resources. Your choice is between a large sample from a few discrete sub-groups and a smaller sample distributed over the whole group. It is a trade-off between the amount of precision lost by using a few sub-groups and the amount gained from a larger sample size.