1. CONSUMER SURVEY RESEARCH
Collecting
Primary
Data

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

3. Survey Research Design
• The way the environment is controlled or
organized
• Environmental variables to control
When the survey is given
How the survey is given
▫ The sample size
▫ Number of groups
• The more environmental control, the more
accurate the results will be

4. What questions do you have?
Selecting a survey research design
should be based on the survey
question(s) you are trying to
answer

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

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

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

8. NonResponse Error
The difference between a survey that includes only respondents and a perfect survey that also includes nonrespondents

9. Response Bias
Where respondents answer survey questions with a bias that misrepresents truth

10. Administrative Error
- data processing error
- sample selection error
- interviewer error
- interviewer cheating

11. Asking Consumers Questions

12. Major Decisions What to ask How questions are phrased
Sequence of questions
Layout
Pretesting

13. What Questions? … will be determined by Type of Marketing Decision
Problem definition
Primary research objectives

14. Two Main Types of Question:
Closed
Open

15. Closed-ended questions can be:
Dichotomous
Multiple
Likert scale
Semantic differential
Rank order
Numeric

16. Dichotomous Description: Question offering two choices Example:
Did you watch television at all yesterday?
Yes / No

17. Multiple Description: Question offering three or more choices Example:
Which of these shops do you prefer?
Next / River Island / Gap Top Shop/ Top Man

18. Likert scale Description: Statement with which respondent shows
the amount of agreement / disagreement
Example:
Assessment by course-work is easier than assessment
by examination
Strongly agree
Agree
Neither agree nor disagree
Disagree 
Strongly disagree

19. Semantic differential
Description:
Scale is inscribed between two bipolar words and respondent selects the point that most represents the direction and intensity of his / her feelings
Example:
The course I am taking is
Interesting :_____:_____:_____:_____:_____: Boring
Useful :_____:_____:_____:_____:_____: Useless
Easy :_____:_____:_____:_____:_____: Difficult

20. Rank order Description:
Respondent is asked to rate or rank each option that applies. This allows the researcher to obtain information on relative preferences, importance etc.
Example:
Please indicate, in rank order, your preferred chocolate bar, putting 1 next to your favourite through to 5 for your least favourite.
Snickers Bar
Dove bar
Wispa
Mars Bar
Creme Egg

21. Numeric
Description:
Respondent specifies a particular value (can include decimal places)
Example:
How far (to the nearest kilometre) did you travel today to reach this College?
________km

22. Advantages of Closed-Ended Questions
Quick to answer
Easy to code
No difference between articulate
and inarticulate respondents
Disadvantages of Closed-Ended Questions
Can draw misleading conclusions because of limited range of
options
Researcher / interviewer cannot deal with qualifications to
responses
e.g. "Yes, but….." or "It depends" where only Yes/No are
given as options

23. Open-ended questions Unstructured Word Association Sentence completion
Story completion

24. Unstructured Description:
Question that respondents can answer in an unlimited number of ways?
Example:
Why did you enrol for this course at HAUT?
……………………………………………………………………………… …………………………………..
…………………………………………………………

25. Word Association Description:
Words are presented one at a time and respondents
give the first word that comes to mind
Example:
What is the first thing that comes to mind when you
hear the following?
Lecture
Interesting 
Computer
Exciting 
Exam
Challenge 
Tutorial
Rewarding

26. Sentence completion Description:
Incomplete sentences are presented, one at a time, and respondents are asked to complete the sentence
Example:
My worst shopping experience while visiting ZhengZhou happened when……………….

27. Story completion Description:
An incomplete story is presented and respondents asked to complete it
Example:
I sat down at the kitchen table, picked up a spoon, then looked at the Chicken and Mushroom flavour Instant Noodles in front of me…….
NOW COMPLETE THE STORY

28. Advantages of Open-Ended Questions Greater freedom of expression
No bias due to limited response ranges
Respondent can qualify their answers
Disadvantages of Open-Ended Questions
Time consuming to code
Researcher / interviewer may misinterpret
and therefore misclassify) a response

29. Questionnaire Layout Always Introduce questionnaire
Move from general to specific
Use “filter” questions

30. Checklist for Questionnaires
1. Have you avoided all leading questions?
2. Is the question as specific as possible? .
3. Are the questions going to be understood by all respondents?
4. Is each question applicable to all respondents?
5. Are any of your questions double - barreled?

31. SAMPLING

32. 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.

33. Bias The one thing above all to avoid.
There is usually no way to fix a biased sample
and no way to salvage useful information from it.
The best way to avoid bias is to select individuals
for the sample at random.
The value of deliberately introducing randomness is one of the great insights of Statistics

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

35. Probability Sampling Methods
Probability or random sampling gives all members of the population a known chance of being selected for inclusion in the sample and this does not depend upon previous events in the selection process.
The selection of individuals does not affect the chance of anyone else in the population being selected. Many statistical techniques assume that a sample was selected on a random basis

36. Randomize
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.
There are tables and books of random digits available for random sampling. Statistical software can generate random digits (e.g., Excel)

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

38. 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.

39. Simple Random Samples
To select a sample at random, we first need to define where the sample will come from.
The sampling frame is a list of individuals from which the sample is drawn.
E.g., To select a random sample of students from a college, we might obtain a list of all registered full-time students.
When defining sampling frame, must deal with details defining the population; are part-time students included? How about current study-abroad students? Once we have our sampling frame, the easiest way to choose an SRS is with random numbers.

40. 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 Consumer Research the most frequently-adopted form of non-probability sampling is known as quota sampling.

41. Quota Sampling
Similar to cluster sampling in that it requires the definition of key subgroups.
Main difference lies in the fact that quotas (i.e. the amount of people to be surveyed) within subgroups are set beforehand (e.g. 25% yr olds, 30% yr olds, 20% yr olds, and 25% 56+ yr olds)
Usually proportions are set to match known population distributions.
Interviewers then select respondents according to these criteria rather than at random.
The subjective nature of this selection means that only about a proportion of the population has a chance of being selected in a typical quota sampling strategy.