Chapter 15 Data Preparation andDescription McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.

15-2 Learning Objectives Understand... The importance of editing the collected raw data to detect errors and omissions. How coding is used to assign number and other symbols to answers and to categorize responses. The use of content analysis to interpret and summarize open questions.

15-3 Learning Objectives Understand... Problems with and solutions for “don’t know” responses and handling missing data. The options for data entry and manipulation.

15-4 Goal of Data Decription “ The goal is to transform data into information, and information into insight. Carly Fiorina former president and chairwoman, Hewlett-Packard Co

15-5 PulsePoint: Research Revelation 55 The percent of white-collar workers who answer work-related calls or e- mail after work hours.

15-6 Data Preparation in the Research Process

15-7 Monitoring Online Survey Data Online surveys need special editing attention. CfMC provides software and support to research suppliers to prevent interruptions from damaging data.

15-8 Editing Criteria Consistent Uniformly entered Uniformly entered Arranged for simplification Arranged for simplification Complete Accurate

15-9 Field Editing Speed without accuracy won’t help the manager choose the right direction. Field editing review Entry gaps identified Callbacks made Validate results

15-10 Central Editing Be familiar with instructions given to interviewers and coders Do not destroy the original entry Make all editing entries identifiable and in standardized form Initial all answers changed or supplied Place initials and date of editing on each instrument completed

15-11 Sample Codebook

15-12 Precoding

15-13 Coding Open-Ended Questions 6. What prompted you to purchase your most recent life insurance policy? _______________________________

15-14 Coding Rules Categories should be Categories should be Appropriate to the research problem Exhaustive Mutually exclusive Derived from one classification principle

15-15 Content Analysis QSR’s XSight software for content analysis.

15-16 Content Analysis

15-17 Types of Content Analysis Syntactical Propositional Referential Thematic

15-18 Open-Question Coding Locus of Responsibility Mentioned Not Mentioned A. Company _____________ ___________ B. Customer _____________ ___________ C. Joint Company- Customer _____________ ___________ F. Other _____________ ___________ Locus of Responsibility Frequency (n = 100) A. Management 1. Sales manager 2. Sales process 3. Other 4. No action area identified B. Management 1. Training C. Customer 1. Buying processes 2. Other 3. No action area identified D. Environmental conditions E. Technology F. Other

15-19 Handling “Don’t Know” Responses Question: Do you have a productive relationship with your present salesperson? Years of Purchasing YesNoDon’t Know Less than 1 year10%40%38% 1 – 3 years years or more6030 Total 100% n = % n = % n = 200

15-20 Data Entry Database Programs Database Programs Optical Recognition Optical Recognition Digital/ Barcodes Digital/ Barcodes Voice recognition Voice recognition Keyboarding

15-21 Missing Data Listwise Deletion Pairwise Deletion Replacement

15-22 Key Terms Bar code Codebook Coding Content analysis Data entry Data field Data file Data preparation Data record Database Don’t know response Editing Missing data Optical character recognition Optical mark recognition Precoding Spreadsheet Voice recognition

Appendix 15a Describing Data Statistically McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.

15-24 Research Adjusts for Imperfect Data “In the future, we’ll stop moaning about the lack of perfect data and start using the good data with much more advanced analytics and data-matching techniques.” Kate Lynch research director Leo Burnett’s Starcom Media Unit

15-25 Frequencies Unit Sales Increase (%)FrequencyPercentage Cumulative Percentage Total Unit Sales Increase (%)FrequencyPercentage Cumulative Percentage Origin, foreign (1) Origin, foreign (2) Total A B

15-26 Distributions

15-27 Characteristics of Distributions

15-28 Measures of Central Tendency MeanModeMedian

15-29 Measures of Variability Interquartile range Quartile deviation Quartile deviation Range Standard deviation Variance

15-30 Summarizing Distribution Shape

15-31 _ _ _ Symbols

15-32 Key Terms Central tendency Descriptive statistics Deviation scores Frequency distribution Interquartile range (IQR) Kurtosis Median Mode Normal distribution Quartile deviation (Q) Skewness Standard deviation Standard normal distribution Standard score (Z score) Variability Variance