Chapter 10 Preparing Data for Quantitative Analysis Mc

Chapter 10 Preparing Data for Quantitative Analysis Mc. Graw-Hill/Irwin Copyright © 2013 by The Mc. Graw-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 10 -2

Value of Preparing Data for Analysis • Data preparation process follows a four-step approach: – Data validation – Editing and coding – Data entry – Data tabulation 10 -3

Exhibit 10. 1 - Overview of Data Preparation and Analysis 10 -4

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 10 -5

Validation • Covers five areas: – Fraud – Screening – Procedure – Completeness – Courtesy 10 -6

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 10 -7

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 10 -8

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 -9

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 10 -10

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 10 -11

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 10 -12

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 10 -13

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 10 -14

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 10 -15

Exhibit 10. 6 - Example of One-Way Frequency Distribution 10 -16

One-Way Tabulation • In reviewing the output, look for: – Indications of missing data – Determining valid percentages – Summary statistics 10 -17

Exhibit 10. 7 - One-Way Frequency Table Illustrating Missing Data 10 -18

Descriptive Statistics • Used to summarize and describe the data obtained from a sample of respondents • Measures used to describe data: – Central tendency – Dispersion 10 -19

Graphical Illustration of Data • Next step following development of frequency tables is to translate them into graphical illustrations 10 -20

Marketing Research in Action: Deli Depot • Run a frequency count on variable X 3– Competent Employees. – Do the customers perceive employees to be competent? 10 -21