Sampling

1. Sampling Moazzam Ali

2. Sampling • How we select from an infinite number of observations we could possibly make • Why do we sample? • Size of the population • Cost of obtaining elements • Convenience and accessibility of elements • Sampling is the process of obtaining information from a subset (sample) of a larger group (population) • The results for the sample are then used to make estimates of the larger group • From Chapter 7 in Babbie & Mouton (2001)

3. Characteristics of a Good Sample Good sampling design should: • Relate to the objectives of the investigation • Be practical and achievable; • Be cost –  effective in terms of equipment and labour; • Provide estimates of population parameters that are truly representative and unbiased. Ideally, representative samples should be: • Taken at random so that every member of the population of data has an equal chance of selection; • Large enough to give sufficient precision; • Unbiased by the sampling procedure or equipment.

4. Sampling Terminology • Element • The unit about which information is collected • Typically the elements are people • But look at the section on “unit of analysis” again: any of them could be elements (schools, universities, corporations, etc.)

5. Sampling Terminology Population • All the potential study elements, as defined • Careful specification of the population Sample Population • Almost impossible to guarantee that every element meeting your definition of “the population” has a chance to be selected into the sample. • Thus the “study population” will be somewhat smaller than “the population”

6. Sampling Terminology Sampling Unit • Typically the sampling units are the same as the elements and probably the units of analysis • (We are not going to look into more complex sampling units) Sampling Frame • The actual list of sampling units (or elements). • e.g. if you want to study “Students at the University of Cape Town”, there is a list of such sampling units (but there are a number of definition issues to be resolved here) Sample • A subset of a population selected to estimate the behaviour or characteristics of the population. Research design - sampling

7. Sampling Designs Basically two sampling strategies available: • Probability sampling • Non-probability Sampling

8. Probability Sampling • Each member of the population has a certain probability to be selected into the sample Types of Probability Sampling • Random • Stratified Random • Systematic • Cluster

9. Random Sampling • Population members are selected directly from the sampling frame • Equal probability of selection for every member (sample size/population size) • 400/10,000 = .04 • Use random number table or random number generator

10. Systematic Sampling • Order all units in the sampling frame based on some variable and number them from 1 to N • Choose a random starting place from 1 to N and then sample every k units after that

11. Stratified Sampling • The chosen sample contains a number of distinct categories which are organized into segments, or strata • equalizing "important" variables • year in school, geographic area, product use, etc. • Steps: • Population is divided into mutually exclusive and exhaustive strata based on an appropriate population characteristic. (e.g. race, age, gender etc.) • Simple random samples are then drawn from each stratum.

12. Stratified Sampling

13. Stratified Sampling • The sample size is usually proportional to the relative size of the strata. • Ensures that particular groups (e.g. males and females) within a population are adequately represented in the sample • Has a smaller sampling error than simple random sample since a source of variation is eliminated

14. Cluster Sampling • The Population is divided into mutually exclusive and exhaustive subgroups, or clusters, usually based on geography or time period • Each cluster should be representative of the population i.e. be heterogeneous. • Means between clusters should be the same (homogeneous) • Then a sample of the clusters is selected. • then some randomly chosen units in the selected clusters are studied.

15. Cluster Sampling • divide population into clusters (usually along geographic boundaries) • randomly sample clusters • measure units within sampled clusters

16. Non-probability Sampling Members selected not according to logic of probability (or mathematical rules), but by other means (e.g. convenience, or access) Types of Non-Probability Sampling • convenience sampling • judgement sampling • snowball sampling • quota sampling

17. Convenience Sampling • Convenience Sampling A researcher's convenience forms the basis for selecting a sample. • people in my classes • Mall intercepts • People with some specific characteristic (e.g. bald)

18. Purposive Sampling • Select the sample on the basis of knowledge of the population: your own knowledge, or use expert judges to identify candidates to select • Typically used for very rare populations, such as deviant cases.

19. Snowball Sampling • Typically used in qualitative research • When members of a population are difficult to locate, for covert sub-populations, non-cooperative groups • Recruit one respondent, who identifies others, who identify others,…. • Primarily used for exploratory purposes Research design - sampling

20. Quota Sampling • A stratified convenience sampling strategy • Begins with a table that describes the characteristics of the target population • e.g. the composition of postgraduate students at UCT in terms of faculty, race, and gender • Then select on a convenience basis, postgraduate students in the same proportions regarding faculty, race, and gender than in the population • Of course, the quota frame (the proportions in the table) must be accurate • And biases may be introduced when selecting elements to study Research design - sampling

21. Thank You