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Selecting samples

Selecting samples. Chapter 6. Meeting objectives. Understand the need and importance of sampling in business and management research. Be aware of a range of probability and non-probability sampling techniques. Be aware of how to select the write research sample.

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Selecting samples

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  1. Selecting samples Chapter 6 sampling Dr Majed El-Farra

  2. Meeting objectives • Understand the need and importance of sampling in business and management research. • Be aware of a range of probability and non-probability sampling techniques. • Be aware of how to select the write research sample. sampling Dr Majed El-Farra

  3. Sample provide alternatives to census when: importance: • It would be impractical to survey the entire population. • You have limited budget to survey the entire population. • Limited time available. • You need quick results. sampling Dr Majed El-Farra

  4. Sampling techniques • Probability or representative sampling • Non-probability or judgmental sampling. • Probability sampling: the chance or probability of each case being selected from the population is known and is usually equal for all cases. Non-probability sampling: the opposite. sampling Dr Majed El-Farra

  5. Probability sampling • It is associated with survey-based research where you need to make inferences/conclusions from your sample about a population to answer your research question or to meet your objectives. sampling Dr Majed El-Farra

  6. Steps of designing probability sampling. • Identify a suitable sampling frame based on your research question. • Decide on a suitable sample size. • Select the most appropriate sampling technique and select the sample. • Check that the sample is representative of the population. sampling Dr Majed El-Farra

  7. Identifying a suitable sampling frame • The sampling frame for probability sample is a complete list of all the population cases. • Examples on sampling frame: teamwork activity: • Leadership style of Gaza’s managers in the private sector. • Constraints of scientific research at IUG faculty of commerce: Academics viewpoint. • Internal constraints of industrial development in the Gaza Strip. sampling Dr Majed El-Farra

  8. Factors govern sample size • The confidence you need in data. • The margin of error that you can tolerate. • The type of analysis that you are going to undertake. • The size of total population. sampling Dr Majed El-Farra

  9. Views on Sample size • The economists believe that sample size of 30 would be relevant to many cases. • Researchers normally work to a 95% level certainty. The margin error is 5%. sampling Dr Majed El-Farra

  10. Reasons for non-response • Refusal to respond. • Ineligibility to respond. Does not meet research requirements. • Inability to locate respondent. • Respondent located but unable sampling Dr Majed El-Farra

  11. Select the most appropriate sampling technique and select the sample. • Simple sample • Systematic • Stratified sample • Cluster • Multi-stage sampling Dr Majed El-Farra

  12. Simple random sample • Select the sample at a random from the sampling frame. • How to select: • Number each of the cases in your sampling frame (0,1,2,..). • Select cases using random number. • Methods: sampling with replacement and without replacement. • Random selection reduce bias. • Random sample is used when you can list the entire population. sampling Dr Majed El-Farra

  13. Systematic sampling • Selecting the sample at regular intervals from the sampling frame. • Selection steps: • Number each of the cases with unique number. • Select the first case using a random number. • Calculate the sampling fraction: sample size/population size • Select the subsequent cases systematically. sampling Dr Majed El-Farra

  14. Stratifies random sampling • steps • You divide the population into 2 or more strata based on one or a number of attributes. • Random sample is drawn (simple or systematic) from each strata. sampling Dr Majed El-Farra

  15. Cluster sampling • You divide the population into discrete/separate groups prior to sampling. • The sampling frame is the complete list of clusters rather than complete list of cases within the population. • You select few clusters. • Used when you undertake an interview based research and you have limited resources. • Ex. Conducting research on the behavior in of visa card clients in the Gaza Strip. sampling Dr Majed El-Farra

  16. Multi-stage sampling or multi-stage cluster sampling • Used when the population geographically dispersed, and face to face contact is needed. • Steps: • Phase one: • Choose sampling frame of relevant discrete groups. • Number each group with a unique number (0,1) • Select randomly a small sample of relevant discrete groups using some form of random sampling • Phase two: From the relevant discrete groups choose a sampling frame and continue the steps as mentioned above. • Phase three: repeat phase 2 if necessary. sampling Dr Majed El-Farra

  17. Non-probability sampling • In the exploratory stages non-probability sampling may be the most practical, using pilot survey. • Sampling techniques: • Quota • Purposive • Snowball • Self-selection • convenience sampling Dr Majed El-Farra

  18. Non-probability sampling • Quota • Alternative to stratified sample. • When is it used? • Cost constrained • Data needed quickly • Steps of designing quota sampling: • Divide the population into specific groups. • Calculate a quota for each group based on relevant and available data. • Collect data from defined numbers. • Quota is used in market research surveys, usually include measures of age, gender, socioeconomic status. • It can often produce a good results, but you cannot measure the level of certainty. sampling Dr Majed El-Farra

  19. Non-probability sampling • Purposive or judgmental: Usually used with small samples, or used with researchers following the grounded theory. • Purposive sampling strategies: • Focuses on extreme cases e.g., most success companies. This enables you understand more typical cases. • Heterogeneous or maximum variation sampling. You should identify the diverse characteristics prior selection. • Homogeneous: focus on one particular subgroup, all sample members are similar. sampling Dr Majed El-Farra

  20. Non-probability sampling • Snowball Used when it is difficult to identify members of desired population. Respondents are most likely identify other potential respondents. There is a problem in representativeness. e.g. knowing how CEO succeeded and reach such high rank. We start with one CEO and he helps in identify others to meet and others identify others and so one. sampling Dr Majed El-Farra

  21. Non-probability sampling • Self-selection When you allow individuals to identify their desire/willingness to take part in the research by informing them with your research problem and objectives. Data will be collected from individuals who express their interest to participate. sampling Dr Majed El-Farra

  22. Non-probability sampling • Convenience/ haphazard sampling. Involves selecting haphazardly those cases that are easiest to obtain. Example: standing at the uni. Campus and ask students you meet to fill a questionnaire and you keep doing so tell you get the sample size. sampling Dr Majed El-Farra

  23. Group work • Simple sample • Systematic • Stratified sample • Cluster • Multi-stage Two groups will work on these samples. They should draw working examples how to use these samples in reality. Theses samples will be divided between the groups. sampling Dr Majed El-Farra

  24. Group work Sampling techniques: • Quota • Purposive • Snowball • Self-selection • Convenience Two groups will work on these samples. They should draw working examples how to use these samples in reality. Theses samples will be divided between the groups. sampling Dr Majed El-Farra

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