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Chapter 10
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Write My Essay For MePreparing Data for
Quantitative Analysis
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Learning Objectives
• Describe the process for data preparation and analysis
• Discuss validation, editing, and coding of survey data
• Explain data entry procedures and how to detect errors
• Describe data tabulation and analysis approaches
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Value of Preparing Data for Analysis
• Data preparation process – Converting data from a source into usable code for data analysis
– Important in:
• Assessing and controlling data integrity
• Ensuring data quality by detecting potential response and nonresponse biases created by interviewer or respondent errors
• Dealing with inconsistent data from different sources
• Converting data in multiple formats to a single format that can be analyzed
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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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Areas Covered by Validation
Fraud Screening Procedure
Completeness Courtesy
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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 proper questions and recording answers accurately
– Correct screening of respondents and complete and accurate recording of open-ended questions
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Coding
• Grouping and assigning values to various responses from survey instruments
– Codes are numerical
– Gets tedious if certain issues are not addressed prior to collecting the data
• Well-planned and constructed questionnaire reduces the time spent on coding
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Coding (continued)
• 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
– 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 perform error edit routines
– Review a printed representation of the entered data
– Run a tabulation of all survey questions to examine the responses for completeness and accuracy
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Missing Data
• Situation in which respondents do not provide an answer to a question
• Counter approaches
– Replace missing value with a value from a similar respondent
– Use the answers to other similar questions as a guide in determining the replacement value
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Missing Data (continued)
– 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
• Not recommended as it reduces overall variance in the question
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Data Tabulation
• Counting the number of observations or cases that are classified into certain categories
• Forms
– One-way tabulation: Categorization of single variables existing in a study
– Cross-tabulation: Simultaneously treating two or more variables in a study
• Categorizes the number of respondents who have answered two or more questions
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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 a research project
• Illustrated by constructing a one-way frequency table
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One-Way Tabulation (continued)
• In reviewing the output, look for:
– Indications of missing data
– Determining valid percentages
– Summary statistics
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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
• Powerful for communicating key research results generated from preliminary data analysis
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Marketing Research in Action Deli Depot
• Should the Deli Depot questionnaire have screening questions?
• Run a frequency count on variable X3– Competent Employees
– Do the customers perceive employees to be competent?
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Marketing Research in Action Deli Depot (continued)
• Consider the guidelines on questionnaire design
– How would you improve the Deli Depot questionnaire?
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