1. Objectives and data needs
SADC Course in Statistics
Objectives and data needs
(Session 01)

2. Module overview Basic concepts and definitions
Sampling methods – simple random, stratified, cluster, multi-stage, etc
Designing a sampling scheme for relatively simple scenarios in accordance with objectives and available resources
How to produce estimates for population characteristics with measures of precision
Sample size determinations
An appreciation of what is meant by sampling weights

3. Module aims By the end of this module, you will be able to
explain what is meant by sample, population, sampling frame, sampling units
explain the notions of representativeness and generalisability of results
design sampling schemes for simple scenarios
produce population estimates and associated measures of precision
discuss options for calculation of sample sizes with a good understanding of general issues involved

4. Aims – this session… By the end of this session, you will be able to
appreciate the different type of objectives that may arise in real life surveys
critically assess the type of data needed to address questions of interest
explain the benefits of sampling
recognise importance of utilising existing knowledge about the population sampled
We begin with some general remarks, then move to survey objectives & other issues.

5. Some general remarks
This module is on sampling ideas and issues arising from sampling procedures when conducting a survey
It is not about survey methods and analysis – which are covered in Module H7
As such, there will not be coverage of the survey process except where it relates to sampling
The accompanying handout gives an outline of the survey process so the context is clear – useful to read…

6. Survey Objectives
Decisions regarding the sampling process cannot be made rationally unless we are clear about the survey objectives.
Surveys conducted by national statistical offices are often done to provide information on which policy decisions can be made
The stated objectives are often vague, e.g. “the objective of this survey is to collect information about…….”
OK as a starting point, but need to be more specific if a sensible sampling procedure is to be used – some examples follow…

7. Objectives related to estimation
To estimate
median income of dwellings in slum areas of a city
proportion of rural households that have no access to a medical facility within 3 kms
maternal mortality rate, i.e. deaths per 1000 live births of mothers from puerperal causes
mean yield per hectare of pigeonpea production in small-holder commercial farms

8. Objectives related to comparisons
Questions of interest may be:
does a newly introduced farming practice for managing banana plants result in higher yields compared to a standard practice?
is there a difference in access to health facilities between rural & urban areas?
is there evidence that children from poorer families have less opportunities for entering higher educational institutions?

9. Objectives related to relationships
Is there a relationship between
consumption expenditure (as a proxy for income) and household demographics and assets?
children’s enrolment in primary school and educational level of household head?
mean number of visits by household members to a health clinic and their level of access to clean water and adequate sanitary facilities?

10. Sampling units and data
Sampling is a first step in any survey study.
There is always a need, before data collection,
to identify (amongst other things):-
the ultimate sampling unit on which measurements are to be made
actual measurements needed, plus clarity on the calculation of any derived variable(s)
sampling procedure to use to select sampling units
Two examples from slides 7 and 9 follow…

11. Initial steps – some examples
Example 1. Estimating the proportion of rural households that have no access to a medical facility with qualified personnel within 3 kms of their homestead.
Unit: Household
Measurement: Distance to the closest medical facility (latter appropriately defined)
Derived variable: Coded as 1 if above measurement > 3 kms, 0 otherwise

12. An example with a hierarchy
Example 2. Determining if there is a relationship between number of visits by household members to a health clinic and their level of access to clean water and adequate sanitary facilities?
Units (within HH): Household members
Measurements: Visits made by each member in HH, how clean water is accessed, sanitary facilities
Derived variable: Mean number of visits = Sum of visits by all members divided by HH size (needed as objective is at the HH level, but measurement is at person level)

13. Selection of households?
Another aspect needed before data collection is to plan the sampling procedure, i.e. how the households can be selected.
Here it is important to study the sampling setting to understand better the structure (social, geographical etc) of the “target” population from which households are to be drawn.
Here “target” population refers to the group of households to which the survey results are intended to generalise.

14. Using existing knowledge
Examining the literature should also point to existing data sources – need to avoid duplicate data collection. Use what is relevant.
Also use general knowledge about the target population. Often much is on record: it would be desirable to use this information
Administrative structure: e.g. districts, counties, subcounties, parishes, villages in Uganda
Agro-ecological regions
Rural/urban divide, etc
Build knowledge about existing data sources and population into the sampling and data collection process

15. Why Sample? The whole population is rarely measurable
An exception is the census, e.g. usually population censuses are done once every 10 years
A well-designed sample enables us to extrapolate our results to the population
Statistical methods enable us to measure the reliability of our conclusions
Though they were covered in Module H2, we reiterate benefits below for completeness and as a reminder.

16. Benefits of sampling
Cheaper, quicker and administratively easier than census
Less prone to errors – and those that do occur are more easily identified
A well-thought out sampling procedure can ensure proper coverage of major population characteristics
If suitably structured, the sample survey can (i) take account of varying sizes of units, e.g. farms, and (ii) correct for under-enumeration and some sorts of non-response.

17. Limitations of sampling
Sound sample surveys require considerable time and effort to plan and run. If tasks entailed and resources needed are under-estimated, the results will be poor
Unless a pre-determined data analysis plan is in place at the start, data relevant to objectives may not be collected, or too much unnecessary data will be collected
Training survey staff is crucial. Ill-phrased questions, poorly linked to objectives, can lead to non-informative results

18. References
De Vaus, D.A. (2001) Research Design in Social Research. Sage Publications, London. ISBN
KALTON, G. (1990) Introduction to Survey Sampling. Sage Publications. ISBN