Logic of Sampling (Babbie, E. & Mouton, J. 2005. The Practice of Social Research. Cape Town:Oxford). C Hart February 2007

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

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.

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

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.

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.167-183)

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

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

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