1. Logic of Sampling
(Babbie, E. & Mouton, J The Practice of Social Research. Cape Town:Oxford).
C Hart February 2007

2. Sampling Can’t observe everything - ‘what to observe & what not’
“Is the process of selecting observations”
Specific sampling techniques allow us to determine and/or control likelihood of specific individuals being selected

3. Sampling Methods: 1) Non-Probability
Reliance on available subjects
People passing on street – Can’t Generalize from data
Purposive/judgemental sampling
Own knowledge of population, its elements & nature of research aims – often with piloting of questionnaire
Qualitative researchers interested in deviant cases – not fitting into regular pattern
Snowball sampling – each interviewee suggests another
Most used in qualitative sampling
Collecting data on few members then asking them to provide information re locating other members – questionable representativeness
Primarily used for exploratory purposes
Quota sampling – like probability sampling addressing representativeness
Matrix table describing the characteristics
Those assigned to a cell are given a weight appropriate to their portion
Difficult to have correct quota frame, often biases
Selecting Informants
Qualitative research – Anthropologist studying a social setting – informant can speak on behalf of the group.

4. Sampling Methods: 2) Probability
Efficient method for selecting a sample that adequately reflect variations existing in the population
Conscious & Unconscious sampling Bias
Not to select out of convenience or intimidation therefore not typical/representative (bias) of the group
Representativeness & probability of selection
“when all members of the population have equal chance to being selected for the sample – EPSEM
Two advantages: 1) more representative – bias avoided
2) permits us to estimate representativeness of the sample

5. Concepts & Terminology
Element - Same as unit of analysis – for data analysis other for sampling
Population - Theoretically specified aggregation of study elements
Study population - Aggregation of elements from which the sample is actually selected
Sampling unit - Element/set of elements considered for selection in some stage of sampling
Sampling frame - Actual list of sampling units from which the sample is selected
Observation unit - Element/aggregation of elements from which information is collected often same as unit of analysis
Variable - Set of mutually exclusive attributes – gender, age, etc.
Parameter - Summary description of a given variable in a population
Statistic - Summary description of a given variable in a sample
Confidence levels & intervals - Two key components of sampling error estimates – accuracy of sample statistics are i.t.o. these two.

6. Probability Sampling Theory & Distribution
Purpose: To select a set of elements from a population in a way that descriptions accurately portray the total population
Random selection is key – each element has equal chance of selection to another
Random no. table & computer program methods – 1) checks un/conscious bias & 2) Access to probability theory – basis for estimates of the population (see pp )

7. Populations & Sampling Frames
“List/quasi list of elements from which probability sample is select”
Findings only represents the aggregation of elements to compose the sample frame
All elements must have equal representation in the frame
Don’t make assertions from a almost identical sample frame for the population
Sampling elements in a study need not be individuals
Error is reduced by two factors:
Large samples produces smaller error
Homogeneous population produces smaller sampling errors than heterogeneous populations

8. Types of Sampling Designs
Simple random sampling
Assigning a single no. to each element in the sample frame – selection of no. randomly
Systematic sampling
Rather than random. Every kth element in the total is is chosen – careful of periodicity error (cycle pattern coinciding with sample interval)
Stratified sampling
Not alternative to above two – represents modification to the above to ensure greater degree of representation
Focused on homogeneous population of a sample (age, gender, seniority, etc.)
Implicit stratification in systematic sampling
Whenever the list produces an implicit stratification

9. Multistage Cluster Sampling
When impossible/impractical to compile exhaustive list of the elements composing the target population
Initial sampling of groups of elements (clusters) followed by selection of elements within each of the selected clusters (i.e. Universities in SA)
General guideline – maximize no. of clusters selected while decreasing the no. of elements within each cluster