1. AC 1.2 present the survey methodology and sampling frame used
Census and Sampling
What is a census?
What is sampling?
AC 1.2 present the survey methodology and sampling frame used
Assignment Task 1.2

2. Learning Outcomes
By the end of the session, learners would be able to describe:
Sampling and sampling terms
Types of sampling methods
Rationale for sampling
Sampling methods
Sampling errors and biasness

3. POPULATION & SAMPLING
Census or population refers to the entire market segment we want to get information from.
Sampling is a representative of the market segment or population
The sampling method selected must be fair & accurate for the information to be statiscally reliable
If the sampling method is incomplete and inaccurate, it is said to be biased.

4. What exactly is a “sample”?
A subset of the population, selected by either “probability” or “non-probability” methods. If you have a “probability sample” you simply know the Probablity of any member of the population being included (not necessarily that it is “random.”)

5. Sampling Who do you want to generalize to? The Theoretical Population
What population can
you get access to?
The Study
Population
How can you get
access to them?
The Sampling
Frame
Who is in your study?
The Sample

6. Sample Design Benefits of Sampling Aim and Stages of Sampling
Aims at avoiding bias and achieving maximum precision from a given outlay of time and money
Stages in sample design:
Decide the objective of the research
Define the study population
Choose a sample method
Decide sample size
Saves time
Faster results
Saves money
Enables more surveys to be carried out
Can concentrate on a small number of units
A carefully chosen sample can yield statistically valid information about the population as a whole

7. Sampling Methods Nature of the population, time and money
Unbiased (each object in the population to be equally chosen as part of the sample)
Representative of the population (e.g. more female than male …)
Population
Sample

8. Sampling frame Random sampling requires a sampling frame.
The means of identifying sampling units.
“A list of members of a population…” (Morris, 2000, p41) from which a sample can be chosen. Eg: Electoral Roll, telephone directory, etc.
It should be comprehensive, complete, accurate and up to date.
Care should be taken for any ‘Bias’: eg: ownership of telephone may indicate certain social class only.

9. Sample Bias
A Sample is Biased if it differs from the population in a systematic way
To be able to generalize from a sample population without bias, select random sample
Selection Bias (exclude or under-represent part of the population)
Measurement or response bias (measurement faulty )
Non response bias

10. Choice of Sampling Method
Is a sample frame available? If so, random sampling is possible
Is the population homogeneous or heterogeneous?
What degree of precision is required? To lower the margin of error the sample size must be increased
What is the budget for the exercise?
Available budget
Accuracy required
How quickly the information is needed
Accessibility of the population

11. Types of Sampling Non random sampling Involves human judgement
Probability Methods
Non-probability Methods
Non random sampling
Involves human judgement
No sampling frame required
Quota sampling
Convenience sampling
Judgement sampling
Random sampling
Does not involve human judgement
Requires a sampling frame
Simple random sampling
Systematic sampling
Stratified random sampling
Cluster sampling

12. RANDOM SAMPLING METHODS
SIMPLE RANDOM SAMPLING: This means selecting a sample with every item or respondent in the sampling frame having an equal chance of being selected.
SYSTEMATIC SAMPLING: selecting items from the list at regular intervals e.g. every 5th customer that buys hamburger or every 10th car that buys petrol.

13. Cluster Sampling
Population is divided into clusters then chosen at random (e.g. department of a business)
Within a cluster all the objects are included in the sample
If clusters are different from each other regarding to the element we measure it can introduce bias or non representative.
More convenient than simple random sampling

14. Stratified Sampling Students Strata Random subsamples of n/N
Similar to Cluster
Complex to administer
To make the sample more representative the population is divided into a number of strata (groups or levels). If the list of 600 students consists of 360 males and 240 females, the sample of 50 students is more representative if it reflects these proportions as follows:
Number of males  360/600x50 =30
Number of females  240/600x50 = 20
Students
Females
Males
Strata
Random subsamples of n/N

15. Stratified Sampling
Advantages • Every unit in a stratum has the same chance of being selected. • Using the same sampling fraction for all strata ensures proportionate representation in the sample of the characteristic being stratified. • Adequate representation of minority subgroups of interest can be ensured by stratification and by varying the sampling fraction between strata as required.
Disadvantages • The sampling frame of the entire population has to be prepared separately for each stratum. • Varying the sampling fraction between strata, to ensure selection of sufficient numbers in minority subgroups for study, affects the proportional representativeness of the subgroups in the sample as a whole.

16. Non-probability Methods
Convenience Sampling
Quota Sampling
This involves gathering information from anyone available for the interview, no matter their background, this is not a very reliable method because the respondents may not really be the ideal people to provide the information we require.
Getting information only from respondents exhibiting certain characteristics e.g. sex, age, socio-economic group or other demographic details up to a maximum quota or number

17. Judgement Sampling
This means the researcher using his or her own judgement to select respondents based on his belief that they fit quite into the profile of the people he wishes to get information from

18. HOW TO HAVE A GOOD SAMPLE
Have an accurate and comprehensive sample frame or records such as electoral or census data, sales data etc.
The sample population must be potential customers
Choose a suitable sample size
Identify the person who buys the product (always differentiate between customers & consumers)

19. Errors in research Sampling error Non response error
Data collection error
Data analysis error

20. Sampling Error
The difference between the estimate of value obtained from a sample and the actual value
Arises because a sample cannot exactly represent the population as a whole
Bias as a consequence of the way in which a sample is structured or the way it was selected
Sampling error can be reduced either by increasing the size of the sample or by improving the amount of knowledge on the structure of the market prior to drawing up the sample

21. Inarticulate respondents Ambiguous questions
Data Collection Errors
Errors in Analysis
Leading questions
Clerical errors
Misrepresentation
Inarticulate respondents
Ambiguous questions
Unintended interviewer bias
Errors in completing pre-coded answers
Omission of important factors in questionnaire design
Error in statistical analysis
Drawing the wrong conclusion
Confusion between cause and correlation
Misrepresentation of the data
False definitions