Preparing Data for Quantitative Analysis Copyright © 2010 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 Wal-Mart and Scanner Technology

10-4 Data Preparation Process Data validation Editing and coding Data entry Data tabulation

10-5 Exhibit 10.1 Overview Error detection Validation Editing and coding Data entry Data tabulation Data analysis

10-6 Exhibit 10.1 Overview_2 Data analysis Interpretation Univariate and bivariate analysis Descriptive analysis Multivariate analysis

10-7 Data Validation  Data validation is the process of determining to the extent possible whether the interviews or observations were correctly conducted and free of fraud or bias

10-8 Primary Areas of Validation Fraud Screening Procedure Completeness Courtesy

10-9 Areas of Editing Concern  Asking the proper questions  Recording answers accurately  Screening questions correctly  Recording open-ended answers completely and accurately

10-10 Coding  Coding involves grouping and assigning value to various responses from the survey instrument

10-11 Developing Response Codes Generate list of potential responses and assign values Consolidate responses Assign numerical value as a code Assign a coded value to each response

10-12 Data Entry  Data entry includes tasks involved with the direct input of the coded data into some specified software package that will ultimately allow the research analyst to manipulate and transform the raw data into useful information

10-13 Methods of Error Detection  Determine if the software used will allow the user to perform “error edit routines”  Scan the actual data that was entered  Produce a data/column list procedure for the entered data

10-14 Exhibit 10.5 SPSS Data View of Coded Values

10-15 One-Way Tabulation  Categorization of single variables in study  Illustrate one-way tabulation by constructing a one-way frequency table  Used to calculate summary statistics on questions  Averages  Standard deviations  Percentages

10-16 Exhibit 10.6 One-Way Frequency Distribution

10-17 Exhibit 10.6 One-Way Frequency Table with Missing Data

10-18 Cross-Tabulation  Simultaneously treat two or more variables in the study  Purpose is to determine if certain variables differ when compared among various subgroups of the total sample  Main form of data analysis in most research projects

10-19 Marketing Research in Action: Deli Depot  How could Deli Depot’s survey and questionnaire be improved?  What are the competitive advantages and disadvantages of Deli Depot over Subway?