Chapter 10

Preparing Data for Quantitative Analysis

Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin

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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 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

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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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Validation

• Covers five areas:– 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 the proper questions– Accurate recording of answers– Correct screening of respondents– Complete and accurate recording of open-ended

questions

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• 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

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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

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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

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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

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Marketing Research in Action: Deli Depot

• Run a frequency count on variable X3–Competent Employees.– Do the customers perceive employees to be

competent?