1. Week Three
Review

2. SURVEY RESEARCH

3. Based on simple idea:
“”… the best way to find out what consumers think is to ask them.”
Zikmund

4. Survey Research
A method of collecting primary data by communicating with a representative sample of people

5. Properly conducted Surveys can be:
Quick
Inexpensive
Efficient
Accurate
Flexible

6. Problems with Surveys come from:
Nonresponse error
Response bias
Administrative error

7. Types of Sampling:
Personal Interviews
Intercepts
Telephone interviews
Self administered questions
Mail questionnaires

8. SAMPLING
(Zikmund,
Chapter 12)

9. Examine a Part of the Whole
In most surveys access to the entire population is near impossible,
The results from a survey with a carefully selected sample will reflect extremely closely those that would have been obtained had the population provided the data.

10. There are essentiality two types of sampling:
probability
non-probability
sampling.

11. Randomisation - Individuals are randomly selected.
- No one group should be over-represented.
- Sampling randomly gets rid of bias.
Random samples rely on the absolute objectivity of random numbers.

12. Four basic types of random sampling techniques:
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster or Multi-stage Sampling

13. Simple Random Sampling
This is the ideal choice as it is a ‘perfect’ random method.
Using this method, individuals are randomly selected from a list of the population and every single individual has an equal chance of selection.

14. Systematic Sampling
Systematic sampling is a frequently used variant of simple random sampling. When performing systematic sampling, every kth element from the list is selected (this is referred to as the sample interval) from a randomly selected starting point.
For example, if we have a listed population of 6000 members and wish to draw a sample of 200, we would select every 30th (6000 divided by 200) person from the list. In practice, we would randomly select a number between 1 and 30 to act as our starting point.

15. Cluster or Multi-stage Sampling
Is particularly useful in situations for which no list of the elements within a population is available and therefore cannot be selected directly.
As this form of sampling is conducted by randomly selecting subgroups of the population, possibly in several stages, it should produce results equivalent to a simple random sample

16. Cluster samples are generally used if: - No list of the population exists.
- Well-defined clusters, which will often be
geographic areas,exist.
- A reasonable estimate of the number of
elements in each level of clustering can be
made.
- Often the total sample size must be fairly
large to enable cluster sampling to be
used effectively.

17. Non-probability Sampling Methods
Non-probability sampling procedures are much less desirable, as they will almost certainly contain sampling biases. Unfortunately, in some circumstances such methods are unavoidable.
In Marketing Research the most frequently-adopted form of non-probability sampling is known as quota sampling.

18. Calculating a Sample Size:
Calculation of an appropriate sample size depends upon a number of factors unique to each survey and it is down to the researcher to make decisions regarding these factors. The three most important are:
- How accurate you wish to be - How confident you are in the results - What budget you have available

19. The required formula is: s = (z / e)2 Where: s = the sample size
z = a number relating to the degree of confidence you wish to have in the result. 95% confidence* is most frequently used and accepted. The value of ‘z’ should be 2.58 for 99% confidence, 1.96 for 95% confidence, 1.64 for 90% confidence and 1.28 for 80% confidence.
e = the error you are prepared to accept, measured as a proportion of the standard deviation (accuracy)

21. (re. Zikmund,
Chapter 14)

22. Why we need Statistics
The main purpose of statistics is to accurately summarise the data into easily interpretable fewer numbers

23. Average (or mean)
The average is one kind of descriptive statistic, which indicates a ‘typical’ or ‘central’ figure for a group of numbers. It is officially called a ‘measure of central tendency’.

24. Median:
If you have a set of values, and wish to obtain a figure which represents the central point, then a sensible way of doing this may be to arrange the numbers in order of size and pick the number which falls in the middle as being of typical value. 

25. Mode:
It is simply the value in any set of scores that occurs most often

26. The Range
The range tells you over how many numbers altogether a distribution is spread over. 
It is easily obtained by subtracting the smallest score from the largest. 

27. The Standard Deviation
In principle, the standard deviation (often shortened to ‘sd’) is very similar to the mean deviation. 
It summarises an average distance of all the scores from the mean of a particular set.

28. Normal Distribution