Higher Level Module H6
Module H6
Sampling Concepts in Survey Work
Synopsis
Surveys form the most typical mode for data collections undertaken by National Statistical
Systems in SADC Member States. Planning these carefully, with survey objectives in mind,
is crucially important if meaningful and unbiased results are to emerge from the survey.
Giving sufficient attention to the design aspects before survey implementation will enable a
good database of survey information to be created, thus, maximising the value that can be
drawn from data analysis.
Efficient sampling procedures and well-selected survey implementation tools are key
elements of the planning process. The latter will be dealt with in the parallel module
entitled Survey Methods and Analysis. This module will concentrate on the former and is
aimed at providing a good understanding of decisions to be made before survey
implementation, and reasons for these decisions, so that they are defensible in the light of
survey objectives and available resources. Estimation approaches and approaches to
sample size determinations form a key component, balancing the need to get optimum
results against the feasibility of acquiring these within time and budget constraints. There
will also be an emphasis on combining knowledge of the target population together with
requirements imposed by statistical theory for producing reliable estimates having high
levels of precision.
Objectives
Successful students will be able to

Explain what is meant by the terms sample, population, target population and
sampling units, sampling frame

Explain the notion of representative and generalisable results
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
Distinguish the circumstances under which different sampling schemes are suitable
according to survey objectives and available resources.

Design a sampling scheme for a given scenario with clearly specified survey
objectives, and in so doing, recognise (a) the types of information they need to seek
for this purpose, (b) the resource implications, (c) the need to think critically about
the reliability of the sampling frame being used, (d) practical difficulties that may
arise during field implementation, and (e) other limitations associated with the
sampling scheme.

Produce sample estimates for population means and proportions with respect to
simple random sampling and stratified random sampling schemes, together with
associated estimates of precision.

Discuss the options for calculation of sample sizes for simple sampling designs, as
well as a good understanding of general issues and difficulties associated with
determining sample sizes for large scale multi-stage sampling procedures.
Pre-requisites
Intermediate level modules and Module H2.
Content
Session 1. Objectives and Data needs
Study of survey objectives. Identifying data needs. Importance of examining the literature
to determine existing data sources, their appropriateness and their reliability. Using
knowledge concerning the target population.
Session 2. Basic Sampling Concepts
Samples, target population, study population, sampling frames, sampling units. What is
meant by “representativeness”? The importance of getting results that are generalisable.
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Sessions 3. Overview of sampling methods I
What is meant by simple random sampling and stratified random sampling. How to draw
such samples. Benefits and limitations.
Sessions 4. Overview of sampling methods II
Probability versus non-probability sampling methods. A brief overview of quota sampling,
purposive sampling, systematic sampling, cluster sampling and multi-stage sampling.
Session 5. Developing a sampling strategy
Developing a sampling strategy for a given problem. Identifying information needs.
Discussing alternative sampling schemes as presented by different groups.
Sessions 6. Estimating population characteristics with simple random
sampling
Estimating a population mean, a population proportion. Distinguishing between “with”
and “without” replacement sampling. Computing measures of precision.
Sessions 7. Stratified random sampling
How to take a stratified random sample. Advantages of stratification. Sample sizes using
proportional allocation or Neyman’s allocation. Deriving estimates for a population mean,
total and proportion.
Sessions 8 & 9. To the Woods: a statistical game
The aim of this game will be to revise the material covered in sessions 3 to 7, while at the
same time emphasising the conditions under which stratified sampling is beneficial over
simple random sampling.
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Session 10. “To the Woods” discussion
Discussion of results with a selected data set. Review of ideas concerning simple and
stratified sampling. Post-stratification ideas. Distinguishing a ratio estimate from a
proportion based on a sample size fixed by the investigator.
Sessions 11. Sample size determinations
Formulae for determining the sample size based on simple random sampling for estimating
a population mean or population proportion.
Session 12. General approaches to sample size determinations
Difficulties associated with use of formulae. Recognising broad issues that enter into
sample size determinations. Key considerations needed to make decisions about sample
sizes.
Sessions 13 & 14. Multi-stage sampling
Cluster and multi-stage sampling. Probability proportional to size (PPS) sampling. Selfweighting designs. Brief introduction to the role of design effects.
Sessions 15 & 16. Sampling design using the Paddy Game
Developing skills in sampling design. Collecting and analysing the data, and reporting the
results appropriately.
Sessions 17. Discussing Paddy results
Participants present their sampling schemes and analyses for discussion.
Session 18. Hierarchies of units and non-traditional sampling approaches
Disposition of resources, sampling up the hierarchy, sampling down the hierarchy.
Objectives at the different hierarchical levels. Combining or splitting studies. Targeting
special groups, replicated sampling.
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Session 19. Sampling weights: an appreciation
Role of sampling weights in estimation. Calculation of weights for simple scenarios.
Session 20. Non-sampling errors
Brief overview of different types of non-sampling errors. Discussion of how non-sampling
errors can be minimised.
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