Page 100 - DLIS401_METHODOLOGY_OF_RESEARCH_AND_STATISTICAL_TECHNIQUES

Unit 6: Sampling Techniques

Having established the frame, there are a number of ways for organizing it to improve efficiency Notes
and effectiveness.
It’s at this stage that the researcher should decide whether the sample is in fact to be the whole
population and would therefore be a census.

6.3 Probability and Non-probability Sampling

A probability sampling scheme is one in which every unit in the population has a chance
(greater than zero) of being selected in the sample, and this probability can be accurately
determined. The combination of these traits makes it possible to produce unbiased estimates
of population totals, by weighting sampled units according to their probability of selection.
Example: We want to estimate the total income of adults living in a given street. We visit each
household in that street, identify all adults living there, and randomly select one adult from
each household. (For example, we can allocate each person a random number, generated from
a uniform distribution between 0 and 1, and select the person with the highest number in each
household). We then interview the selected person and find their income.
People living on their own are certain to be selected, so we simply add their income to our
estimate of the total. But a person living in a household of two adults has only a one-in-two
chance of selection. To reflect this, when we come to such a household, we would count the
selected person’s income twice towards the total. (In effect, the person who is selected from
that household is taken as representing the person who isn’t selected.)
In the above example, not everybody has the same probability of selection; what makes it a
probability sample is the fact that each person’s probability is known. When every element in
the population does have the same probability of selection, this is known as an ‘equal probability
of selection’ (EPS) design. Such designs are also referred to as ‘self-weighting’ because all
sampled units are given the same weight.
Probability sampling includes: Simple Random Sampling, Systematic Sampling, Stratified Sampling,
Probability Proportional to Size Sampling, and Cluster or Multistage Sampling. These various
ways of probability sampling have two things in common: (1) Every element has a known
nonzero probability of being sampled and (2) involves random selection at some point.
Nonprobability sampling is any sampling method where some elements of the population
have no chance of selection (these are sometimes referred to as ‘out of coverage’/’undercovered’),
or where the probability of selection can’t be accurately determined. It involves the selection
of elements based on assumptions regarding the population of interest, which forms the criteria
for selection. Hence, because the selection of elements is nonrandom, nonprobability sampling
does not allow the estimation of sampling errors. These conditions place limits on how much
information a sample can provide about the population. Information about the relationship
between sample and population is limited, making it difficult to extrapolate from the sample
to the population.

Example: We visit every household in a given street, and interview the first person to answer
the door. In any household with more than one occupant, this is a nonprobability sample,
because some people are more likely to answer the door (e.g. an unemployed person who
spends most of their time at home is more likely to answer than an employed housemate who
might be at work when the interviewer calls) and it’s not practical to calculate these probabilities.
Nonprobability Sampling includes: Accidental Sampling, Quota Sampling and Purposive Sampling.
In addition, nonresponse effects may turn any probability design into a nonprobability design
if the characteristics of nonresponse are not well understood, since nonresponse effectively
modifies each element’s probability of being sampled.

LOVELY PROFESSIONAL UNIVERSITY 95