Transcript IPPTChap010

Chapter 10
Preparing Data for
Quantitative Analysis
McGraw-Hill/Irwin
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.
Learning Objectives
• Describe the process for data preparation and
analysis
• Discuss validation, editing, and coding of
survey data
• Explain data entry procedures as well as how
to detect errors
• Describe data tabulation and analysis
approaches
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Value of Preparing Data for Analysis
• Data preparation process follows a four-step
approach:
– Data validation
– Editing and coding
– Data entry
– Data tabulation
10-3
Exhibit 10.1 - Overview of Data
Preparation and Analysis
10-4
Validation
• Determines whether a survey’s interviews or
observations were conducted correctly and
are free of fraud or bias
– Curbstoning: Cheating or falsification in the data
collection process
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Validation
• Covers five areas:
– Fraud
– Screening
– Procedure
– Completeness
– Courtesy
10-6
Editing
• Raw data is 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:
– Asking the proper questions
– Accurate recording of answers
– Correct screening of respondents
– Complete and accurate recording of open-ended
questions
10-7
Coding
• Grouping and assigning values to various
responses from the survey instrument
– Codes are numerical
– Can be tedious if certain issues are not addressed
prior to collecting the data
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Coding
• Four-step process to develop codes for
responses:
– Generate a list of as many potential responses as
possible
– Consolidate responses
– Assign a numerical value as a code
– Assign a coded value to each response
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Data Entry
• 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
• Involves:
– Error detection
– Missing data
– Organizing data
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Error Detection
• Identifies errors from data entry or other
sources
• Approaches
– Determine if the software used will allow the user
to perform “error edit routines”
– Review a printed representation of the entered
data
– Run a tabulation of all survey questions so
responses can be examined for completeness and
accuracy
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Missing Data
• A situation in which respondents do not
provide an answer to a question
• Approaches to deal with missing data:
– Replace missing value with a value from a similar
respondent
– Use answers to the other similar questions as a
guide in determining the replacement value
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Missing Data
– Use mean of a subsample of the respondents with
similar characteristics that answered the question
to determine a replacement value
– Use mean of the entire sample that answered the
question as a replacement value
• Mot recommended as it reduces overall variance in the
question
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Data Tabulation
• The counting the number of observations
(cases) that are classified into certain
categories
– One-way tabulation: Categorization of single
variables existing in a study
– Cross-tabulation: Simultaneously treating two or
more variables in the study
• Categorizing the number of respondents who have
answered two or more questions consecutively
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One-Way Tabulation
• Purposes
– Determine the amount of nonresponse to
individual questions
– Locate mistakes in data entry
– Communicate the results of the research project
• Illustrated by constructing a one-way
frequency table
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Exhibit 10.6 - Example of One-Way
Frequency Distribution
10-16
One-Way Tabulation
• In reviewing the output, look for:
– Indications of missing data
– Determining valid percentages
– Summary statistics
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Exhibit 10.7 - One-Way Frequency
Table Illustrating Missing Data
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Descriptive Statistics
• Used to summarize and describe the data
obtained from a sample of respondents
• Measures used to describe data:
– Central tendency
– Dispersion
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Graphical Illustration of Data
• Next step following development of frequency
tables is to translate them into graphical
illustrations
10-20
Marketing Research in Action:
Deli Depot
• Run a frequency count on variable X3–
Competent Employees.
– Do the customers perceive employees to be
competent?
10-21