Lecture 9 Prof. Development and Research Lecturer: R. Milyankova Sampling Lecture 9 Prof. Development and Research Lecturer: R. Milyankova

Objectives of this session: To understand the need for sampling in B&M research To be aware of a range of probability and non-probability sampling techniques To be able to select, to justify and to use a range of sampling techniques To be able to assess the representativeness of respondents To be able to apply the knowledge, skills and understanding gained to your own research project

Sampling terminology Census – counting of the population Population – the full set of cases from which a sample is taken Sampling techniques – range of methods that enable you to reduce the amount of data you collect Population Case or element Sample

Need to sample Sampling provides a valid alternative when: It would be impracticable for you to survey the entire population Your budget constraints prevent you from surveying the entire population Your time constraints prevent you from surveying the entire population You have collected all the data but need the results very quickly

Major types of sampling methods Probability or representative sampling - The probability for each case is known and is usually equal for all cases - Uses some form of random selection - Requires that each unit has a known (often equal) probability of being selected - Used more for survey-based than for experiment research Non-probability or judgemental sampling - The probability of the separate cases is not known preliminary - Selection is systematic or haphazard, but not random - More frequently used for case study research

Sampling Techniques Extreme case Heterogeneous Homogeneous Critical case Typical case

Probability sampling: Stages Identify a suitable sampling frame based on your research questions and/or objectives (unbiased, current and accurate) Checklist for selecting a sample frame Are cases listed in the sampling frame relevant to your research topic, are they current? Does the sampling frame include all cases, is it complete? Does the sampling frame exclude the irrelevant cases, is it precise? Can you establish control precisely how he sample will be selected? (when purchased lists of samples)

Probability sampling: Stages 2. Decide on a suitable sample size – the larger the sampling size, the lower the error (the sampling is a compromise between the accuracy of your findings and the amount of time and money you invest in collecting data) The confidence you need to have in your data (the level of certainty) The margin of error that you can tolerate The types of analyses you are going to undertake The size of the total population from which your sample is being drawn

Probability sampling: Stages Minimum number of cases – 30 (The Economist). Less than 30 – use all cases + expert system Level of certainty – 95 % The margin of error depends on response rates (see Saunders, M. et all, 2003, Table 6.1, page 156)

Probability sampling: Stages Reasons for non-response: Refusal to respond Illegibility to respond Inability to locate respondents Respondent located but unable to make contact Total response rate = total number of responses total number of sample – ineligible Active response rate = total number of responses total number of responses–(ineligible+unreachable)

Probability sampling: Stages Select the most appropriate sampling technique and select the sample Simple random – accurate and easily accessible, concentrate on face-to face contact otherwise does not matter, difficult to explain to support workers, high cost Close your eyes and choose the number Systematic - accurate and easily accessible, suitable for all sizes, concentrate on face-to face contact otherwise does not matter, relatively easy to explain, low cost Every third case for example Stratified random - accurate and easily accessible, suitable for all sizes, concentrate on face-to face contact otherwise does not matter, relatively difficult to explain, low cost Divide the population into strata (men-women, retail-corporate) Cluster – as large as practicable, quick but reduced precision Discrete groups=clusters (geographical areas, town regions) Multi-stage – substantial errors possible It is a development of the cluster sampling Sampling fraction = actual sample size total population

Probability sampling: Stages Checking the sample is representative for the population Compare with samples, done for the needs of marketing or other sources for the population researched

The Theoretical Population Groups in Sampling The Theoretical Population How to identify the suitable sampling frame?

Groups in Sampling The Theoretical Population What population can you get access to? (Telephone directory)

The Theoretical Population Groups in Sampling The Theoretical Population The Study Population

The Theoretical Population How can you get access to them? Groups in Sampling The Theoretical Population The Study Population How can you get access to them? (methods of research)

Groups in Sampling The Theoretical Population The Study Population The Sampling Frame -complete list of all the cases in the population

The Theoretical Population Groups in Sampling The Theoretical Population The Study Population The Sampling Frame Who is in your study? The sample

Deciding on a suitable sampling size The larger your sampling size the lower the error The confidence you need to have in your data – the level of certainty that the characteristics of data collected will represent the characteristics of the total population The margin of error that you can tolerate – the accuracy you require for any estimates made from your sample The types of analysis you are going to undertake – The size of the total population from which your sample is being drawn

The Theoretical Population Where Can We Go Wrong? The Theoretical Population The Study Population The Sampling Frame The sample

Sample sizes for different sizes of population at a 95% level of certainty Margin of error Population 5% 3% 2% 1% 50 44 48 49 100 79 91 96 99 150 108 132 141 148 200 168 185 196 250 151 203 226 244 300 234 267 291 400 334 384 500 217 340 414 475 750 254 440 571 696 1000 278 516 706 906 2000 322 1091 1655 5000 357 879 1622 3288 10000 370 964 1936 4899 100000 383 1056 2345 8762 1000000 1066 2395 9513

Sampling Techniques Extreme case Heterogeneous Homogeneous Critical case Typical case

Non-probability sampling Quota sampling – non-random, used for interview surveys, the population is divided into specific groups, stratified, less costly, can be set up very quickly Purposive (judgmental) sampling – - extreme case or deviant sampling - heterogeneous or maximum variation sampling - homogeneous all sample members are similar - critical case sampling – selected either because they are important or because they are different - typical case sampling -

Non-probability sampling Snowball sampling – when it is difficult to identify members of the desired population Self-selection sampling – participate if they want Convenience (haphazard) sampling – select those cases that are easier to obtain for your sample

Statistical Terms in Sampling Variable self esteem 1 2 3 4 5

Statistical Terms in Sampling Variable self esteem 1 2 3 4 5 Statistic Average = 3.72 sample

Statistical Terms in Sampling 1 2 3 4 5 Variable self esteem Statistic Average = 3.72 sample Parameter Average = 3.75 population

The Sampling Distribution sample 4 . 2 3 8 6 5 sample 4 . 2 3 8 6 5 sample 4 . 2 3 8 6 5 Average Average Average 4 . 2 3 8 6 1 5 ...is the distribution of a statistic across an infinite number of samples The Sampling Distribution...