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Chapter 10 – Preparing Data for Quantitative Analysis
Chapter 10
Preparing Data for Quantitative Analysis
Learning Objectives (PPT slide 10-2)
1. Describe the process for data preparation and analysis.
2. Discuss validation, editing, and coding of survey data.
Key Terms and Concepts
1. Coding
2. Cross-tabulation
3. Curbstoning
4. Data entry
Chapter Summary by Learning Objectives
Describe the process for data preparation and analysis.
The value of marketing research is its ability to provide accurate decision-making information to
the user. To accomplish this, the data must be converted into usable information or knowledge.
After collecting data through the appropriate method, the task becomes one of ensuring the data
Discuss validation, editing, and coding of survey data.
Data validation attempts to determine whether surveys, interviews, or observations were
conducted correctly and are free from fraud. In recontacting selected respondents, the researcher
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asks whether the interview (1) was falsified; (2) was conducted with a qualified respondent; (3)
took place in the proper procedural setting; (4) was completed correctly and accurately; and (5)
Explain data entry procedures and how to detect errors.
There are several methods for entering coded data into a computer. First is the PC keyboard. Data
also can be entered through terminals having touch-screen capabilities, or through the use of a
handheld electronic pointer or light pen. Finally, data can be entered through a scanner using
optical character recognition. Data entry errors can be detected through the use of error edit
routines in the data entry software. Another approach is to visually scan the actual data after it has
been entered.
Describe data tabulation and analysis approaches.
Two common forms of data tabulation are used in marketing research. A one-way tabulation
indicates the number of respondents who gave each possible answer to each question on a
questionnaire. Cross-tabulation provides categorization of respondents by treating two or more
variables simultaneously. Categorization is based on the number of respondents who have
responded to two or more consecutive questions.
Chapter Outline
Opening Vignette: Scanner Data Improves Understanding of Purchase Behavior
The opening vignette in this chapter describes Walmart’s use of scanners. With the information
supplied by the scanners, Walmart knows what is there, what is selling, and what needs
replenishment. The same scanners can scan customer cards so the customer is associated with his
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Chapter 10 – Preparing Data for Quantitative Analysis
I. Value of Preparing Data for Analysis (PPT slides 10-3 and 10-4)
Converting information from surveys or other data sources so it can be used in statistical analysis
is referred to as data preparation. The data preparation process typically follows a four-step
approach (PPT slide 10-3).
• Data validation
• Editing and coding
• Data entry
• Data tabulation
With traditional data collection methods, the data preparation process starts after the interviews,
questionnaires, or observation forms have been completed and returned to the field supervisor or
researcher. But new technology associated with online surveys and data collection methods
involving handheld terminals or scanners enables researchers to complete some data preparation
tasks in real time and also eliminate data collection errors. In fact, technology advances are
reducing and sometimes eliminating the need to manually code, verify, and enter data when
creating electronic files.
The stages of data preparation and analysis are shown in Exhibit 10.1 (PPT slides 10-4). Some data
collection methods require activities in all stages while other methods involve only limited data
preparation.
II. Validation (PPT slide 10-5 and 10-6)
The purpose of validation is to determine if surveys, interviews, and observations were conducted
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Chapter 10 – Preparing Data for Quantitative Analysis
In marketing research, interviewers submitting false data for surveys are referred to as
curbstoning. As the name implies, curbstoning is when interviewers find an out-of-the-way
To minimize fraudulent responses, marketing researchers target between 10 percent and 30
percent of completed interviews for “callbacks.” Specifically for telephone, mail, and personal
interviews, a certain percentage of respondents from the completed interviews are recontacted by
the research firm to make sure the interview was conducted correctly. Generally, the process of
validation covers five areas (PPT slide 10-6).
• Fraud: Was the person actually interviewed, or was the interview falsified? Did the
interviewer contact the respondent simply to get a name and address, and then proceed to
fabricate responses? Did the interviewer use a friend to obtain the necessary information?
• Screening: Data collection often must be conducted only with qualified respondents. To
ensure accuracy of the data collected, respondents are screened according to some
preselected criteria, such as household income level, recent purchase of a specific product or
brand, brand or service awareness, gender, or age.
III. Editing and Coding (PPT slides 10-7 to 10-9)
Following validation, the data must be edited for mistakes. Editing is the process where the raw
data are checked for mistakes made by either the interviewer or the respondent. By reviewing
completed interviews from primary research, the researcher can check several areas of concern
(PPT slide 10-7).
• Asking the proper questions
• Accurate recording of answers
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Chapter 10 – Preparing Data for Quantitative Analysis
• Correct screening of respondents
• Complete and accurate recording of open-ended questions
A. Asking the Proper Questions (PPT slide 10-7)
One aspect of the editing process especially important to interviewing methods is to make
certain the proper questions were asked of the respondent. As part of the editing process, the
B. Accurate Recording of Answers (PPT slide 10-7)
Completed questionnaires sometimes have missing information. The interviewer may have
accidentally skipped a question or not recorded it in the proper location. With a careful check of
all questionnaires, these problems can be identified. In such cases, if it is possible, respondents
are recontacted and the omitted responses recorded. This task is not necessary with online
surveys if they were designed to prevent respondents from skipping questions.
C. Correct Screening Questions (PPT slide 10-7)
The first two questions on the Santa Fe Grill employee questionnaire shown in Exhibit 10.2 are
actually screening questions that determine whether the respondent is eligible to complete the
survey. During the editing phase, the researcher makes certain only qualified respondents were
included in a survey. It is also critical in the editing process to establish that the questions were
asked and (for self-administered surveys) answered in the proper sequence. If the proper
sequence is not followed in self-completion surveys, the respondent must be recontacted to
verify the accuracy of the recorded data.
D. Responses to Open-Ended Questions (PPT slide 10-7)
Responses to open-ended questions often provide very meaningful data. Open-ended questions
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Chapter 10 – Preparing Data for Quantitative Analysis
may provide greater insight into the research questions than forced-choice questions. A major
part of editing the answers to open-ended questions is interpretation. Exhibit 10.3 shows some
typical responses to an open-ended question and thus points to problems associated with
interpreting these questions.
E. The Coding Process (PPT slides 10-8 and 10-9)
Coding involves grouping and assigning values to various responses from the survey
instrument (PPT slide 10-8). Typically, the codes are numerical—a number from 0 to
9—because numbers are quick and easy to input, and computers work better with numbers than
alphanumerical values. Like editing, coding can be tedious if certain issues are not addressed
prior to collecting the data. A well-planned and constructed questionnaire can reduce the
amount of time spent on coding and increase the accuracy of the process if it is incorporated
into the design of the questionnaire.
Researchers typically use a four-step process to develop codes for responses (PPT slide 10-9):
• The procedure is similar for all types of data collection and begins by generating a list of
as many potential responses as possible. Responses are then assigned values within a
range determined by the actual number of separate responses identified.
• Consolidation of responses is the second phase of the four-step process. Exhibit 10.4
illustrates several actual responses to the question “Why are you dining less frequently at
the _____ restaurant?” Four of these—related to not liking the food—can be consolidated
into a single response category because they all have the same shared meaning.
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Chapter 10 – Preparing Data for Quantitative Analysis
to missing data is very important. The best way to handle the coding of omitted responses
is first to check on how your data analysis software treats missing data.
• The fourth step in the coding process is to assign a coded value to each response. This is
probably the most tedious process because it is done manually. Each questionnaire is
assigned a numerical value. The numerical value typically is a three-digit code if there are
fewer than 1,000 questionnaires to code, and a four-digit code if there are 1,000 or more.
IV. Data Entry (PPT slide 10-10)
Data entry follows validation, editing, and coding. Data entry refers to the tasks involved with the
direct input of the coded data into some specified software package that ultimately allows the
research analyst to manipulate and transform the raw data into useful information (PPT slide
10-10). This step is not necessary when online data collection is used.
Online surveys are becoming increasingly popular for completing marketing research studies.
Indeed, online surveys now represent almost 60 percent of all data collection approaches. They not
only are often faster to complete but also eliminate entirely the data entry process.
A. Error Detection (PPT slide 10-11)
Error detection identifies errors from data entry or other sources (PPT slide 10-11). The first
step in error detection is to determine whether the software used for data entry and tabulation
performs “error edit routines” that identify the wrong type of data.
B. Missing Data (PPT slide 10-12 and 10-13)
Missing data are often a problem in data analysis. Missing data are defined as a situation in
which respondents do not provide an answer to a question. Sometimes respondents purposely
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Chapter 10 – Preparing Data for Quantitative Analysis
do not answer a question creating a missing data situation. This most often arises when
questions of a sensitive nature are asked, such as asking for a respondent’s age or income. It
also may occur because a respondent simply does not see a question, or is in a hurry to complete
If missing data are encountered, there are several ways to deal with it.
• One approach is to replace the missing value with a value from a similar respondent.
• Another approach, if there are other similar questions to the one with missing data, is to
use the answers to the other similar questions as a guide in determining the replacement
C. Organizing Data
Several useful data organizing functions are available in SPSS. One function under the Data
pull-down menu is Sort Cases. This can be used to sort your data cases (observations) in either
ascending or descending order. Another is the Split File function, which can be used for
V. Data Tabulation (PPT slide 10-14)
Tabulation, sometimes referred to as a frequency count, is the simple process of counting the
number of observations (cases) that are classified into certain categories. Two common forms of
tabulation are used in marketing research projects.
• One-way tabulation: It categorizes single variables existing in a study (PPT slide 10-14). In
most cases, a one-way tabulation shows the number of respondents (frequency count) who
gave each possible answer to each question on the questionnaire. The number of one-way
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Chapter 10 – Preparing Data for Quantitative Analysis
tabulations is determined by the number of variables measured in the study.
• Cross-tabulation: It simultaneously treats two or more variables in the study (PPT slide
10-14). It categorizes the number of responses to two or more questions, thus showing the
relationship between those two variables. It is most often used with nominal or ordinal
scaled data.
One-way and cross-tabulation are considered descriptive statistics.
A. One-Way Tabulation (PPT slides 10-15 to 10-18)
One-way tabulations serve several purposes.
• They can be used to determine the amount of nonresponse to individual questions. Based
on the coding scheme used for missing data, one-way tabulations identify the number of
respondents who did not answer various questions on the questionnaire.
• They can be used to locate mistakes in data entry.
The most basic way to illustrate a one-way tabulation is to construct a one-way frequency table.
A one-way frequency table shows the number of respondents who answered each possible
response to a question given the available alternatives. An example of a one-way frequency
table is shown in Exhibit 10.6 (PPT slide 10-16). In addition to listing the number of responses,
one-way frequency tables also identify missing data and show valid percentages and summary
statistics.
B. Descriptive Statistics (PPT slide 10-19)
Descriptive statistics are used to summarize and describe the data obtained from a sample of
respondents. Two types of measures are often used to describe data.
• Measures of central tendency
C. Graphical Illustration of Data (PPT slide 10-20)
The next logical step following development of frequency tables is to translate them into
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graphical illustrations. Graphical illustrations can be very powerful for communicating key
research results generated from preliminary data analysis.
Marketing Research in Action
Deli Depot (PPT slides 10-21 and 10-22)
The Marketing Research in Action in this chapter provides a questionnaire used by Deli Depot, a
restaurant selling cold and hot sandwiches, soup and chili, yogurt, and pies and cookies. The
exhibit provides the variables measured, the sample questions, and coding.
Answers to Hands-On Exercises
1. Should the Deli Depot questionnaire have screening questions?
Screening questions are used in a study or research to make certain that only qualified
respondents are included in the survey. The three screening questions provided in the Deli
2. Run a frequency count on variable X3–Competent Employees. Do the customers perceive
employees to be competent?
Students’ responses will vary for this question.
3. Consider the guidelines on questionnaire design you learned in Chapter 8. How would you
improve the Deli Depot questionnaire?
Students’ answers for this question will differ.
Suggestive answer:
They want to know the important factors that people use to select a place to eat and how Deli
Depot compares on these important factors with their direct competitors. How do they know
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Chapter 10 – Preparing Data for Quantitative Analysis
like “How does Deli Depot stack up against Subway and the other local competitors?”
Finally, once that data has been evaluated, Deli Depot can focus on emphasizing their
strengths and fixing their important weaknesses.
Answers to Review Questions
1. Briefly describe the process of data validation. Specifically discuss the issues of fraud,
screening, procedure, completeness, and courtesy.
Data validation is something akin to a system of “checks and balances” which allows the
research team to determine if interviews and observations were conducted with accuracy and
precision. It is important for the team to audit the data, with a keen eye regarding the
following five issues.
(i) Fraud: Was the person actually interviewed, or was the interview falsified?
(ii) Screening: Was a qualified respondent interviewed?
(iii) Procedure: Was the data collected in the correct area?
2. What are the differences between data validation, data editing, and data coding?
The process of validating data is all about ensuring the interviews or observations were
conducted correctly and free of fraud on the part of interviewers. Data editing and data
coding, unlike data validation, approaches the data captured from respondents in a “what we
see is what we get” fashion. In other words, the focus of error hovering above the editing and
3. Explain the differences between developing codes for open-ended questions and for
closed-ended questions.
Open-ended questions are useful to researchers and clients since they allow us to probe
answers more deeply for clarity and specifics. The downside of establishing codes for
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open-ended questions is that the process of analysis is far more protracted. As well,
open-ended responses are always open to “interpretation” on the part of the team analyzing
the data. Ultimately, the analysis of an open-ended question ends up in the same spot as
4. Briefly describe the process of data entry. What changes in technology have simplified this
procedure?
Data entry has often been characterized as “grunt work” in the marketing research industry.
It is quite likely class participants will approach this topic (and actually do it) with similar
perceptions. However, changes in technology such as touch-screen capability, hand-held
electronic pointers, hand-held (e.g., palm pilots) and optical scanners have done a lot to alter
5. What is the purpose of a simple one-way tabulation? How does this relate to a one-way
frequency table?
One-way tabulations can be used to:
• determine the degree of nonresponse to each question in the survey (nonresponse can
suggest either a “bad” question, or that the survey was given to a respondent not
aligned with the target population)
Answers to Discussion Questions
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Chapter 10 – Preparing Data for Quantitative Analysis
1. Explain the importance of following the sequence for data preparation and analysis
described in Exhibit 10.1.
The sequence moves from error detection through validation, editing, coding, data entry,
2. Identify four problems a researcher might find while screening questionnaires and preparing
data for analysis.
The four areas that researchers must consider when preparing the data are listed below.
• Were the proper questions asked?
• Were the answers recorded accurately?
3. How can data tabulation help researchers better understand and report findings?
Data tabulations help researchers to find indications of missing data, determine valid
4. SPSS Exercise. Using SPSS and the Santa Fe Grill employee database, develop frequencies,
means, modes, and medians for all the relevant variables on the questionnaire.
This is an excellent way to introduce students to using the SPSS software.