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 Random Stratified Random Systematic Cluster
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