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.

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

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

Exhibit 10.1 - Overview of Data Preparation and Analysis

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

Validation Covers five areas: Fraud Screening Procedure Completeness Courtesy

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

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

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

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

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

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

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

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

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

Exhibit 10.6 - Example of One-Way Frequency Distribution

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

Exhibit 10.7 - One-Way Frequency Table Illustrating Missing Data

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

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

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