The case study discusses all the phases of survey work from problem statement to statistical analysis. Source: Research Methods in Marketing: Survey Research, Harvard Business Review, Rev. September 29, 1986.

1. Research Methods in Marketing: Survey
Research
Presented by
Vartika Kundu 24085
Swati Makhija 24037

2. Introduction
A clear logical thinker with a small kit of statistical concepts can
make useful inferences.
Survey Research Errors Research Design

3. Survey Research Errors
Truth Mistakes Data Collected
Mistake = Measurement Error + Sampling Error
Make the mistakes term as small as possible.

4. Survey Research Errors
Method vary in
Burden on
researcher
Accuracy
Mistakes can
increase
because
People lie
People make
mistakes
People are not
homogenous
We want to determine the percentage of
first-year MBA students owning
programmable calculator?
 Make phone calls
 Design a form
 Ask people
 Tune out

5. Research Design
A systematic approach.
Problem
Statement
• What decision is to be made?
• What information will assist in making decision?
Questionnaire
Design
• What information do we want to collect in interviews?
• What interview questions can get us that information from
respondents?
• How should those questions be phrased?
• How are we going to contact respondents?
Sampling
• Who should our respondents be?
• How many should we get?
Data Analysis • How do we tabulate, summarize, and draw inferences from our
data?

6. Stage 1: Problem Statement
Are we able
to find the
solution to
the problem?
Is survey
research
useful?
How to
proceed?

7. Stage 2: Questionnaire Design
Fundamental Laws: Control management errors.
Question and Answer Format:
 Does respondent have the information you are looking for?
 Will respondent give truthful answer?
 Multiple choice can be helpful.
Pre-test: Administer questionnaire to small group of people.
Corollary A Truthful answers provide
useful information.
Corollary B If more than one way, pick
questions to which
respondents:
a. Know the answer
b. Willing to answer

8. Stage 2: Questionnaire Design
Biased Result: Small differences in response coding can bias data.
“What was the approximate annual income for all members of your family before taxes
during 1974?
Communication Mode: Number of ways to communicate questionnaire to
respondents.
Form 2 (until yes)
<25000 Yes
20000 Yes
15000 Yes
10000 Yes
7500 Yes
5000 Yes
No
Per cent of respondents Form 1 Form 2
<5000 12.4% 5.9%
5000-7499 4. 6.1
7500-9999 17.7 5.0
10000-14999 28.4 19.5
15000-19999 18.8 30.9
20000-24999 5.7 11.4
>25000 13.0 21.4
Form 1 (until no)
< 5000 No
7500 No
10000 No
15000 No
20000 No
25000 No
-- Yes

9. Stage 3: Sampling
Target Population: Group of people to which
 Specific and clear
 List readily available
 All necessary data available
Sampling Frame: Representing member of target population. A perfect frame is
one where every member of population is represented once.
Sample Selection: Mechanism for selecting population.
 Probability Sampling
 Nonprobability Sampling
Sample Size: Determining the number of respondents entitled to predict a given
situation.

10. Stage 3: Sampling
Sample Distribution: Sample size determination also determine sampling
distribution.
Frequency Distribution: Approximations of sampling
Sampled Person 2
Sampled Person 1 Programmable Not Programmable
Programmable 2Ps 1P, 1NP
Not Programmable 1P, 1NP 2NPs
0
5
10
15
20
1 2 3 4 5 6 7 8 9 10 11
Frequency
-5
0
5
10
15
20
0 2 4 6 8 10 12
Frequency

11. Stage 3: Sampling
Standard Error: Standard deviation of sample mean.
Ꝺ=√p(1-p)/n
P= Observed Probability
Prediction Accuracy: Determine how large sample is required to obtain a
specified level of accuracy.
“An automobile dealer wants to know what proportion of customers consults Consumer
Reports before shopping for a car.”
Sample Size and Confidence: Determining the sample size required for
given levels of precision and confidence.
X=2√p(1p)/n
Sample of 100, 43 says yes, 57
says no.
Ꝺ=√(0.43)(0.57)/100 = 0.0495 95% confident that true
percentage of population is in
range of 0.331-0.529
Sample of 400, 172 says yes,
228 says no.
Ꝺ=√(0.43)(0.57)/400 = 0.02475 95% CI consults to 0.308-
0.4795.

12. Stage 3: Sampling
Permissible Error: No rule on how big an error is permissible, however
reliability depends upon the economic stake of decision to be made.
Continuous Data: The distribution of sample mean follows normal distribution.
Only calculation of standard error changes.
Ꝺ=√(∑ x1-X)/n-1
Precision is related to the variability and sample size used.

13. Stage 4: Data Analysis
Transform, summarize and “massage” the data.
Simple Count: Two major steps to make result clear:
1. Convert frequencies into percentages.
2. Construct histogram.
“In last six months, what proportion of your coffee purchases involved redemption of a
cents-off coupon?”
Response Frequency Percentage
None 115 14.4
Some but less than half 480 60
More than half 180 22.5
No response 25 3.1 0
100
200
300
400
500
600
None Some but less than
half
More than half No response

14. Stage 4: Data Analysis
Indexed Numbers: See extent and nature of variability in a set.
“Tabulating average consumption levels of cereal and record albums by group.”
Measures of Central Tendency and Variability: Mean, median and
mode are convenient mode of summarizing data. We need a summary statistics which
indicate variability.
Age
Cereal consumption in
ounces per year Cereal index
Record consumption in
albums per year Record index
>18 190 100 22 100
18-24 84 44 28 127
25-34 123 65 6 27
35-44 104 55 6 27
44< 72 38 5 23

15. Stage 4: Data Analysis
Cross Tabs: Displaying result involving more than one variable.
Coupon Use
Marital Status Single Some More
Single 24.5 17.5 24
Married without 27 28.5 27
Married With 21 24 30

16. Thank You!