1. MISUNDERSTOOD AND MISUSED
SAMPLING
MISUNDERSTOOD AND MISUSED

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. Stages in Selection of a Sample
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

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 = 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