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Transcript of Chapter 10 Preparing Data for Quantitative Analysis Copyright © 2013 by The McGraw-Hill Companies,...
Chapter 10
Preparing Data for Quantitative Analysis
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin
10-2
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
10-3
Value of Preparing Data for Analysis
• Data preparation process follows a four-step approach:– Data validation– Editing and coding– Data entry– Data tabulation
10-4
Exhibit 10.1 - Overview of Data Preparation and Analysis
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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
10-7
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
10-8
• 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
Coding
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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
10-12
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
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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
10-19
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
10-21
Marketing Research in Action: Deli Depot
• Run a frequency count on variable X3–Competent Employees.– Do the customers perceive employees to be
competent?