1. Preparing Data for Quantitative Analysis
Chapter 10
Preparing Data for
Quantitative Analysis
McGraw-Hill/Irwin
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.

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

3. Value of Preparing Data for Analysis
Data preparation process follows a four-step approach:
Data validation
Editing and coding
Data entry
Data tabulation

4. Exhibit 10.1 - Overview of Data Preparation and Analysis

5. 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

6. Validation Covers five areas: Fraud Screening Procedure Completeness
Courtesy

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

8. 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

9. 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

10. 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

11. 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

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

13. 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

14. 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

15. 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

16. Exhibit 10.6 - Example of One-Way Frequency Distribution

17. One-Way Tabulation In reviewing the output, look for:
Indications of missing data
Determining valid percentages
Summary statistics

18. Exhibit 10.7 - One-Way Frequency Table Illustrating Missing Data

19. Descriptive Statistics
Used to summarize and describe the data obtained from a sample of respondents
Measures used to describe data:
Central tendency
Dispersion

20. Graphical Illustration of Data
Next step following development of frequency tables is to translate them into graphical illustrations

21. Marketing Research in Action: Deli Depot
Run a frequency count on variable X3–Competent Employees.
Do the customers perceive employees to be competent?