1. SAMPLING

2. FACTORS AFFECTING SAMPLE SIZE • OBJECTIVE OF RESEARCH • DESCRIPTION • INFERENCE • HOMOGENEITY OF POPULATION • SIZE OF POPULATION • MARGIN OF ERROR

3. SAMPLING TERMS • SAMPLE – some part of a “whole” • ELEMENT – that unit about which information is collected and which provides the basis for analysis • POPULATION – the theoretically specified aggregate of elements • REPRESENTATIVENESS – the extent to which the sample “mirrors” the population • EPSEM – Equal Probability of Selection Method

4. Sampling Terms (cont) • SAMPLING UNIT – that element or set of elements considered for selection in some stage of sampling • SAMPLING FRAME – the actual list of sampling units from which the sample, or some stage of the sample, is selected • OBSERVATION UNIT – (unit of data collection) is an element or aggregation of elements from which information is collected • SAMPLE SIZE – the number of elements selected

5. TYPES OF SAMPLES • NON-PROBABILITY • PROBABILITY

6. NON-PROBABILITY SAMPLES • CONVENIENCE - procedure of obtaining those sampling units/elements most conveniently available • Judgment – an experienced researcher selects the sample based on appropriate characteristics of the sample • Quota – ensures that various subgroups of a population • SNOBALL – initial respondents are selected by some method and then additional respondents are obtained from information provided by the initial respondents

7. Why Probability Samples? • Typically more representative than other types of samples – bias • Permit the researcher to estimate the accuracy or representativeness of the sample • Saves time/money

8. Sampling Error • Biased Selection – misses and/or over represents categories of elements • Chance Variability – a sample deviates from the population value as a result of chance – increasingly problematic as sample size decreases

9. Define the Target Population Select a Sampling Frame Determine Sampling Method Plan Procedure for selecting elements Estimate Sampling Size * Draw Sample Conduct Field Word Check Sample against the Population or Sampling Frame * * If probability sample Stages in Selection of a Sample

10. Probability Sampling • Simple Random – technique which assures that each selected element in the population has an equal chance of being included in the sample • Systematic – an initial starting point is selected by a random process and then every nth numbered element in the frame is selected • Stratified – random subsamples are drawn from within each stratum. The sub samples may be proportional or disproportional to the number of elements in each stratum

11. Systematic Sample • Distance between elements = SAMPLING INTERVAL = K • e.g. we want a sample of 144 = n, where N = 1300 • N/n or 1300/144 = 9.02 this then is the Sampling Interval K = 9 Using a random start, every 9th element would be selected • Sampling Ratio = proportion of population to be selected (N/n) where n = the desired sample size • N/n • e.g. N = 1000 and n = 100 • Sampling Ratio = 1000/100 • Sampling Ratio = 1/10th or as per above 1/9th A random sample of 100 or 144 elements would be selected

12. Probability Sampling (cont) • Cluster – large clusters of elements, not individual elements, are selected in the first stage of sampling • Area – Cluster sampling when the cluster consist of a geographical area • Multistage Area – Cluster sampling that involves a combination of two or more probability sampling techniques