Analyse data for marketing research
What is data?
3
Editing data
4
Field editing
4
Central editing (in-house editing)
4
Missing responses
5
Coding data
8
Pre-coded questions
8
Post-coded questions
9
Developing a code frame
9
Transcribing data
11
Computer assisted telephone interviewing (CATI)
11
Data cleaning
11
Statistically adjusting data
12
Data analysis: an introduction
14
Computer data analysis packages
Descriptive statistics
14
16
Univariate techniques
16
Multivariate techniques
16
Tabulations
16
Cross-tabulations
18
Correlation
20
Hypothesis testing
20
Other descriptive statistics
20
Other statistical techniques
22
Summary
23
Key terms for your glossary
Feedback to Activities and Check your progress exercise
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What is data?
Data becomes information when it is explained in a meaningful way for
decision-makers. When data is first collected, it is called raw data. Raw data
consists of responses contained in a questionnaire. It may be recorded in
ways such as codes, scales or words. In most cases, this raw data is not in a
format suitable for decision-making.
Once it is coded, it is known as data. Data is most often analysed by
computer. However, it can also be analysed manually, as in the case of
qualitative data and especially when there are only small numbers of
responses involved.
Information refers to the information gained from processing the data and
turning it into meaningful information or facts that can be used in decisionmaking.
Activity 1: Vital terms
For this activity, it would be useful for you to consult textbooks on the topic—preferably
the ones suggested for this module. Ensure you understand the vital terms below. Also
enter these terms in your personal glossary.
1 data coding
_____________________________________________________________________
2 data matrix
_____________________________________________________________________
3 data reduction
_____________________________________________________________________
To check your answers, refer to the feedback at the end of this topic.
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3
Editing data
The first step in data preparation is to ensure that questionnaire responses
have been recorded accurately and completely. This process is known as
editing. Effective editing will identify and rectify any inaccurate or
incomplete answers. Editing can be carried out either in the field or at a
central office or both.
Field editing
Field editing is carried out in the field either by the interviewer or, more
commonly, by a supervisor or by both. The purpose of a field edit is to
detect the most glaring omissions and inaccuracies in the data. The
questionnaires are checked for:

completeness

legibility

comprehensibility

consistency

uniformity.
Central editing (in-house editing)
Central editing, also referred to as in-house editing, is usually conducted
from the fieldwork office. It can begin as soon as questionnaires start
coming back from the field and continue until fieldwork is completed.
While field editing is important and can quickly identify and rectify obvious
problems, central editing by a single editor involves a more thorough and
exacting scrutiny and correction of completed data collection forms. The
main task of the editor is to identify and correct inconsistent or contradictory
responses to ensure there will be no problems at the coding or data entry
stage.
Editors will sometimes have to make decisions about responses where
answers appear to be incorrect. Ideally, they do this by going back to the
interviewer to check the correct response. However, if this is not possible, a
decision will need to be made about what the correct response would be.
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In these cases the editor must exercise caution and objectivity based on
predetermined principles or rules for making decisions.
These pre-determined principles should not involve the editor changing or
adding anything to the respondents’ answers that would change the results.
For instance, it would not be acceptable for an editor to add or change a
response to indicate that a respondent preferred one brand to another, if the
respondent had not clearly answered this question.
In editing questionnaires, mistakes must be rectified. In some cases, the
person editing the questionnaire can safely assume what the correct response
might be. In other cases, this is not so clear. Where possible, the interviewer
should be consulted to establish if they can remember the correct response
recorded. It may also be possible to return the questionnaire to the field and
contact the respondent to clarify the response.
However, in some situations, the questionnaire should simply be discarded.
Where there is so much missing information that the questionnaire is
invalid, or where responses are obviously incorrect, and then discarding the
questionnaire will be the preferred option.
Missing responses
Perhaps the most difficult editing question to answer is what to do with
missing responses from questionnaires. If contacting the respondent is not
possible and discarding the questionnaire is not desirable, a substitute
answer can sometimes be given by the editor or the researcher. A value such
as ‘99’ can be used to show that the respondent did not answer the question.
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Activity 2: Common mistakes in questionnaires
Some of the more obvious mistakes in questionnaires are outlined below. For each mistake,
determine a method for editing the error.
1
An interviewer mistakenly records a respondent’s year of birth as 1850, or the gender
of Mary Smith as male.
_____________________________________________________________________
_____________________________________________________________________
2
Contradictory responses may have been given. The respondent records that they don’t
consume soft drinks but they record the brand they most regularly consume.
_____________________________________________________________________
_____________________________________________________________________
3
Income is recorded as monthly income rather than annual income as stated on the
questionnaire.
_____________________________________________________________________
_____________________________________________________________________
4
The interviewer’s handwriting is illegible.
_____________________________________________________________________
_____________________________________________________________________
5
The interviewer meant to circle either (3) or (4) but the circle has mistakenly
overlapped both codes.
_____________________________________________________________________
_____________________________________________________________________
6
Response is incomplete. When asked to nominate three brands, only one brand has
been given.
_____________________________________________________________________
_____________________________________________________________________
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In response to the questions about age, occupation and place of residence, the
respondent claims to be a 101-year-old, one-legged clown in a travelling circus.
_____________________________________________________________________
_____________________________________________________________________
8
The demographics of the respondent do not match what is required for the sample or
quota for the questionnaire.
_____________________________________________________________________
_____________________________________________________________________
9
Inconsistency in responses in which a respondent gives their occupation as a doctor
records their income as $15 000.
_____________________________________________________________________
_____________________________________________________________________
10 Too much consistency. In answer to every multiple-choice question the respondent has
circled (4).
_____________________________________________________________________
_____________________________________________________________________
To check your answers, refer to the feedback at the end of this topic.
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Coding data
Before any data analysis can begin, a process called coding has to take
place. This involves:
1
coding the responses from questionnaires
2
sorting data into categories
3
allocating numerical codes to the categories
4
inputting the codes into a computer.
Questions are either pre-coded at the time of questionnaire design or they
will require coding after completion of the questionnaire.
Pre-coded questions
Pre-coded responses are suitable for the following types of questions:

scale

multiple-choice

dichotomous.
Pre-coding can be used when the researcher can confidently predetermine
one of a number of responses to a question. Personal details such as age,
gender or income are often asked in a ‘structured’ or ‘closed’ question
format as the researcher knows the respondent will fit into one of the predetermined categories.
Sometimes, questions can be partially pre-coded. This can be done through
the use of the category ‘other’ where respondents are asked to specify their
reason or choice. If a large number of respondents give ‘other’ reasons, then
these responses must also be coded at a later stage.
Pre-coding saves time and allows for quick and convenient editing of
questionnaires and speedy computer input.
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Post-coded questions
Post-coding occurs with unstructured questions. The purpose of coding is to
reduce the large number of different types of answers from respondents to a
more manageable number of categories and to sort them into general
categories. These categories are then allocated a numerical code for the
purposes of data entry.
Developing a code frame
This initial step in coding involves developing a code frame. This is usually
done in batches of 50 questionnaires. (The total number of questionnaires in
a survey could be 500 or more.)
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Activity 3: Developing a code frame
Consider the following unstructured question.
What do you like about this shopping centre?
The range of responses to this question could include:
1
I like it because it is close to home.
2
I can catch public transport here.
3
I like the range of shops available.
4
It’s just my local shopping centre.
5
I can always find what I want.
6
I can catch the train there.
7
I like all the shops that are here.
8
I just come here to meet my sister.
9
It’s just a pleasant atmosphere.
10
The people in the shops are friendly.
11
I can always get what I need.
12
It’s convenient—that’s all.
13
There’s a nice café to have lunch in.
14
It only takes me five minutes to drive here.
15
I meet a lot of my friends here.
Can you see any similar responses that might be grouped together?
_________________________________________________________________________
_________________________________________________________________________
_________________________________________________________________________
To check your answers, refer to the feedback at the end of this topic.
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Transcribing data
Once the responses from the questionnaires have been edited and coded, the
data is ready for input into the computer. Transferring the raw data from the
questionnaires into a computer program is known as transcribing.
Transcribing of data provides information for researchers to statistically
describe various results from the survey.
Computer assisted telephone
interviewing (CATI)
CATI is a procedure in which interviewing respondents and entering data is
conducted simultaneously. The computer screen replaces the paper
questionnaire and there is no need to physically record the responses on the
questionnaire. Instead, the interviewer sits at a computer and records the
responses directly into the questionnaire on-screen while conducting the
interview with the respondent.
One question at a time is shown on-screen and questions are skipped as
appropriate. The computer also analyses the data as the interview is being
conducted. CATI is a popular form of interviewing as it cuts down on
coding and data entry time as well as ensuring that data quality is enhanced.
Data cleaning
While the data has already been checked for consistency and correct answers
during the editing process, the computer program further enhances the
editing process during the data entry stage.
For example, the computer can easily identify any responses that are out of
range: if there are five pre-coded categories but the number 6 appears as a
response, then the computer will identify this incorrect response as 6 is out
of the range for responses.
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Statistically adjusting data
Weighted data
Data can be adjusted statistically to enhance the quality of the analysis by a
procedure known as weighting.
Weighting is often used to make the sample more representative of the
population of interest. For example, if males 18–24 were of particular
interest to the researcher but the sample provided insufficient respondents
within this age group, a weighting factor may be assigned to this quota to
reflect the significance of this group of respondents.
Variable re-specification
When the initial run of computer tables has been completed, the researcher
can make some decisions about the information. For example, some of the
code frames may be changed or amalgamated to form more meaningful
information. If there is a table showing computer ownership cross-tabulated
by age groups, the researcher may decide to amalgamate some of the age
groups.
In addition, the researcher may consider that there is not enough difference
between the age groups 51–65 and 65+ to warrant separate categories. You
can therefore amalgamate or collapse the two codes into one new code,
identified as 51+. There is little point in having additional categories if the
information gained offers no meaningful insights into the subject.
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Activity 4: Transcribing data
What would you do if you were presented with the three problem situations below:
1
What can be done if the income bracket $100 000+ is of particular interest to the
researcher, but there are insufficient of these respondents in the sample?
_____________________________________________________________________
2
If initial computer tabulations indicate little difference between respondents attending
the cinema once a week and those respondents attending twice a week, what can be
done to make the information more manageable and meaningful to the decisionmakers?
_____________________________________________________________________
3
If the editor of a particular questionnaire fails to identify a respondent who claims not
to own a motor vehicle but nominates a model that they own, where will this most
likely be identified?
_____________________________________________________________________
To check your answers, refer to the feedback at the end of this topic.
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Data analysis: an introduction
Data analysis is about turning the raw data from the questionnaires into
information that is presented in a useable and easily understood format.
Analysis is also sometimes defined as the process of resolving complex
issues into their simplest and most manageable elements. Ultimately,
analysis should provide the researcher and the client with the direction to
make effective decisions.
As a decision-maker, which of the following would you prefer to know?

Respondent 1 answered ‘yes’ to Question 1, respondent 2 answered
‘no’, respondent 3 answered ‘no’ and so on.

47% answered ‘yes’ and 53% answered ‘no’.
Analysis turns your collected data into understandable information. The
purpose of data analysis is to produce information that will assist decisionmakers.
In deciding how the data will be analysed, the researcher must consider:

what information is required

what information will be most meaningful and useful in the decision
process.
In selecting a data analysis strategy, factors such as research design,
measurement scales and other characteristics of the data have to be taken
into consideration. Different statistical techniques are appropriate for
different types of data analysis.
Computer data analysis packages
Several computer packages are available for data analysis. Four popular
packages used by the market research industry are SPSS (Statistical Package
for the Social Sciences), SAS (Statistical Applications System), Minitab and
Surveycraft. If you are purchasing a package for your firm, check whether
you can buy the package outright. For many practitioners, a simple
Microsoft Excel spreadsheet may suffice.
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These packages have some differences but they all provide a means of
turning raw data into information in a useable format. Essentially SPSS
allows for more complex statistical analysis; however, Surveycraft is very
popular as it provides all the main types of analysis that are of interest to
marketers.
Activity 5: Statistical packages
Visit the websites below for descriptions of the basic operations of some of the major
statistical packages.

www.spss.com

www.sas.com/products/stat/
Have a look around and check out some of the differences between them. Almost all the
major packages, including Microsoft Excel, have the same range of basic statistics. Where
they vary is in their specialist statistical tools. Investigate what sort of additional support
you can get for each program.
Also have a look at www.intelliquest.com for further resources.
Using a statistical package
If you have a statistical package such as SPSS, install the program and load
one of the trial data sets you would have received with it. As we move
through this topic, try out some of the data analysis techniques we have
discussed.
The information you obtain from these statistical tools can sometimes be
confusing. If you want to learn what the numbers mean then read through
your textbook(s). It has very comprehensive content on most of the data
analysis techniques we will discuss. The help files contained within SPSS
are also an excellent resource.
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Descriptive statistics
Descriptive statistics are the most basic form of data analysis. They aim to
describe the data in its simplest form. Descriptive statistical techniques can
be classified as one of the following:

univariate

multivariate.
Univariate techniques
Univariate techniques (that is, techniques referring to one variable only) are
used in analysing data when each respondent is analysed using one
measurement only, for example, age. Univariate techniques are also used
when a respondent is analysed using more than one variable; for example,
age and income, but again, these variables are analysed separately.
Multivariate techniques
Multivariate techniques refer to the analysis of two or more variables at the
same time, for example, age and income. These techniques would allow us
to identify the ownership of personal computers among people aged 35–50
within the income bracket $50 000 to $100 000. They also allow us to
compare different groups. For example, we can compare the characteristics
of people who own computers with those people who do not own
computers, across multiple variables.
Tabulations
Usually the first analysis of the data comprises a computer count of the
number of specific responses to each question and a calculation of the
percentages. This computer output is usually known as tabulation.
Percentages are widely used in computer analysis as they are simple to
calculate and easy to understand.
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Example
An example of computer tabulation from a survey of 500 may look
something like the one below.
1 Which age group do you fall into?
Less than 18
(1)
120 responses
24%
18–34
(2)
80 responses
16%
35–50
(3)
70 responses
14%
51–65
(4)
150 responses
30%
Over 65
(5)
80 responses
16%
500 responses
100%
Total
You can see from this tabulation that 150 respondents are aged between 51
and 65 and that this group represents 30% of the total number of
respondents. This is an example of a simple tabulation or a frequency count,
as it counts the number of responses to each code. Responses to a scaled
question would generally be tabulated in the same way; however sometimes
it may be more useful to combine some of the categories when reporting
findings.
Example
1 Do you think we should abolish daylight savings?
Strongly disagree
40%
Disagree
30%
Don’t know
10%
Agree
10%
Strongly agree
10%
In this instance you may want to ‘collapse’ the categories of ‘disagree’ and
‘strongly disagree’, and likewise for those who agree or strongly agree with
the statement. You could then report that ‘70% of those surveyed disagreed
with the idea of abolishing daylight savings, while 20% of those surveyed
thought it should be abolished’. This allows for clearer communication of
the findings, rather than just reporting raw figures.
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Cross-tabulations
Cross-tabulation of data covers two or more variables at the same time. This
can give us a table that shows the joint distribution of the two or more
variables. The category responses from one question are cross-classified
with the category responses from another question. Many market research
surveys do little more than cross-tabulations simply because they provide a
large amount of useful information with no need for complex calculations or
statistics.
Example
The Swallow Drinks Company may want to know how many people in the
each age group have purchased a single serve drink in the last three months.
To do this they would cross-tabulate the results of a question about purchase
frequency with a question about age of respondents, as follows.
Yes
No
Total
<18
18–34
35–50
51–65
>65
Total
70
110
120
30
20
350
78%
96%
96%
33%
25%
70%
20
5
5
60
60
150
22%
4%
4%
67%
75%
30%
90
115
125
90
80
500
From the above cross-tabulation, you can gain meaningful information such
as:
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
96% of respondents between the ages of 18 and 34 have purchased a
single serve drink in the last three months

75% of respondents over the age of 65 have not purchased a single
serve drink in the last three months.
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Activity 6: Jonah’s Jam patrons
Look at the data set below. Jonah’s Jam Company are exploring the age of the people who
patronise their retail shop and what methods they use to pay.
Payment method
Age
Payment method
Age
Cheque
18
Cheque
66
Cheque
27
Credit card
58
Credit card
95
Credit card
57
Credit card
32
Credit card
74
Cash
16
Cash
40
Cheque
80
Cheque
44
Cash
45
Credit card
55
Credit card
32
Credit card
71
Cheque
51
Cash
13
Cheque
70
Cash
24
Credit card
61
Cash
23
Cheque
47
Cheque
26
Carry out a cross-tabulation on the data to see if there are any differences between the age
of the shopper and how they pay for their purchase. Use the table below for your crosstabulation.
To check your answers, refer to the feedback at the end of this topic.
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Correlation
Marketers often want to know the relationship between two factors that may
have an effect on their market.
Examples of two factors marketers might want to examine are:

the relationship between perception of quality and sales

the relationship between advertising and sales.
In these cases, a statistical technique known as product moment correlation
is used.
This technique is used to illustrate and measure the relationship between
two factors. For example, use of this technique would measure how strongly
levels of advertising are linked to changes in sales levels, and the extent to
which one would change of the other was changed. The relationship
between these two factors is known as covariance.
Hypothesis testing
A hypothesis is simply a belief that is held at the start of the study that the
researcher wants to prove or disprove.
Here is an example of a hypothesis:
50% of my customers are aged over 45
The research then sets out to prove if this statement is right or wrong.
Other descriptive statistics
These basic statistics are used to represent some common forms of
summarising information, or to make a lot of data actually mean something.
Common descriptive statistics include:
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
mode: the value that occurs the most often

median: the midpoint—the value below which the values in a
distribution fall

mean: the arithmetic average

range: the distance between the smallest and the largest values in a
frequency distribution

standard deviation: based on the deviations from the mean,
quantitative index of the distribution’s spread or variability.
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Activity 7: Analysing Jonah’s jam shoppers
Using the data you previously grouped, calculate the mean, median, standard deviation and
range for the age of the shoppers at Jonah's Jam Shop.
1
mean
_____________________________________________________________________
2
median
_____________________________________________________________________
3
range
_____________________________________________________________________
4
standard deviation
_____________________________________________________________________
To check your answers, refer to the feedback at the end of this topic.
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Other statistical techniques
Most of the other statistical techniques used in data analysis are designed to
prove (in one way or another) that the results produced in your sample
would be the same, or similar to, the results that would occur if you
administered the survey or interview to the entire population.
The process of testing how closely the results from the sample match the
results from the population is called significance.
For example, you may have found that 74% of your sample purchase their
music on the Internet. A test of significance would measure how confident
you were that 74% of the population would purchase their music on the
Internet.
Significance is usually measured in terms of a percentage. If you were
testing at 95% significance, it means that the result you obtained from your
sample is within plus or minus 2.5% of the population. If you were testing at
99% significance, that means the results you obtained were accurate for the
population, plus or minus 0.5%.
Many of the remaining statistical techniques are connected to the process of
testing significance. Some are designed to predict a future occurrence based
on past data while others explore the relationships that exist between
variables.
Your suggested readings have a number of chapters on these techniques, and
you should have an understanding of what these techniques are and what
they are used for.
If you have a version of SPSS and have installed it, you’ll find all of these
tools on the pull-down menu. Load the sample data sets and look at the
output produced using the different analysis techniques.
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Summary
In this topic we have been introduced to some basic concepts of statistical
analysis. You should now be familiar with concepts such as data coding,
data matrix and data reduction, and be able to perform them. You should
also be able to use the techniques associated with tabulations, crosstabulations and correlations.
Check your progress
Here are 10 multiple-choice questions to test your knowledge of what you should have
learnt in this topic. If you answered fewer than eight out of ten questions correctly, then
you should go back over the section.
1
The purpose of _______ is to ensure completeness, consistency, and readability of the
data to be transferred to data storage.
(a) editing
(b) coding
(c) keypunching
(d) checking.
2
Pre-coding is desirable because it:
(a) allows data from questionnaires to be entered directly
(b) facilitates statistical analysis
(c) reduces the length of questionnaires
(d) eliminates the need for interviews.
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3
The transformation of raw data (by rearranging, ordering, or manipulating) into a form
that will make them easy to understand is:
(a) inferential statistics
(b) univariate statistics
(c) data processing
(d) descriptive analysis.
4
A survey samples both men and women. The researcher wishing to analyse the data by
separating the groups by sex will perform:
(a) an analysis of central tendency
(b) a simple tabulation analysis
(c) a gender analysis
(d) a cross-tabulation analysis.
5
The choice of the method of statistical analysis depends upon all of the following
except:
(a) the question to be answered
(b) the number of variables
(c) the scale of measurement
(d) the questionnaire design.
6
One of the simplest techniques for describing sets of relationships is:
(a) cross-tabulation
(b) chi-square
(c) regression
(d) correlation.
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7
A correlation coefficient:
(a) is a statistical measure of co-variation between variables
(b) indicates the magnitude of the relationship between variables
(c) indicates the direction of the relationship between variables
(d) all of the above.
8
Constructing a frequency table:
(a) is one of the most common means of summarising data.
(b) begins by recording the number of times a particular value occurs.
(c) is the basis for construction of a percentage distribution.
(d) all of the above.
9
Which of the following is not a measure of central tendency?
(a) range
(b) mode
(c) median
(d) mean.
10 Counting the number of responses to a question and putting them into a frequency
distribution is a:
(a) summary statistic
(b) univariate analysis
(c) simple tabulation
(d) percentage.
To check your answers, refer to the feedback at the end of this topic.
Key terms for your glossary
Research terminology is often very specific and frequently uses words in a
different way to their common meaning. Write definitions for these key
terms and place them in your personal glossary.
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coding
editing
data cleaning
transcribing data
analysis
descriptive statistics
mean
median
mode
range
standard deviation
cross-tabulation
hypothesis testing
chi-square
correlation co-efficient
simple regression
multiple regression
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Feedback to Activities and Check
your progress exercise
Activity 1
Your definitions should mention the following
1
data coding: create a code that can be applied to the possible responses
to a questionnaire
2
data matrix: coded raw data from a survey
3
data reduction: condenses the data matrix using measures that
characterise the data set
If you haven’t already done so, enter these terms in your personal glossary.
Activity 2
1
You can safely assume that the date should be 1950 and that the gender
should be female. Change the responses accordingly.
2
This situation is more difficult. If possible, contact the interviewer for
clarity and check for consistency with other questions. You will need to
make a decision on how to change this information once you have
checked these sources.
3
Change the monthly income to annual income (multiply by 12).
4
Check with interviewer for translation. This should be picked up during
the field edit and the interviewer asked to write more clearly.
5
Check with interviewer. If this is not possible, make a decision based on
consistency with other answers.
6
Record that only one brand was given. Do not be tempted to add other
brands to complete the answer.
7
If this is the calibre of all of the answers in the questionnaire then
discard it. There is always at least one joker in every project.
8
Discard the questionnaire. This respondent is not part of the research
study.
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9
You could simply accept the inconsistency or check with the
interviewer. In this case, you could assume that the income should be
$150 000.
10 Again you could simply accept the answers. If you suspect that the
respondent was uninterested and simply selected 4 to complete the
questionnaire quickly, then discard it.
Activity 3
From these responses a number of different categories could be created to
develop a code frame.
1
Convenience or proximity: Numbers 1, 4 and 12 all relate to the
proximity and convenience of the shopping centre.
2
Ease of travel: Numbers 2, 6, and 14 all relate to how easily customers
can travel to the shopping centre.
3
Good range of shops: Numbers 3, 5, 7, 11 and 13 all relate to the appeal
of the shopping centre.
4
Other: Numbers 8, 9 and 15 don’t relate to any of the above categories
and they all say something slightly different.
Activity 4
1
The sample can be weighted.
2
Code frames can be collapsed.
3
During data cleaning.
Activity 6
Your table should look like this.
Age group
Cheque
Under 18
18-30
Cash
Credit card
2
3
31-40
2
1
2
41-50
2
1
51-60
1
3
Over 60
3
3
Total
9
6
8
Overall most respondents preferred to pay by cheque or credit card. The
majority of respondents aged over 51 preferred to pay by credit card, while
respondents aged under 30 preferred to pay by cash or cheque.
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9775J: 11 Marketing Research
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Activity 7
1
mean: 46.9 years
2
median: 46 years
3
range: 82 years
4
standard deviation: 22.4 years
Note that the years are calculated to one decimal place: this is because any
more information is not useful to the decision-maker.
Check your progress
1
(a)
2
(a)
3
(d)
4
(d)
5
(d)
6
(a)
7
(d)
8
(d)
9
(a)
10 (c)
9775J: 11 Marketing Research
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