SESSION 7DATA ANALYSIS

Chapter 15: Data Preparation and Description Chapter 16: Exploring, Displaying, and

Examining Data

RESEARCH METHODOLOGY

CHAPTER 16 Data Preparation and Description

Learning Objectives:

• 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.

• Problems with and solutions for “don’t know” responses and handling missing data.

• The options for data entry and manipulation.

15-3

Data Preparation in the Research Process

15-4

Monitoring Online Survey Data

Online surveys need special editing attention. CfMC provides software and support to research suppliers to prevent interruptions from damaging data .

15-5

Editing

Criteria

Consistent

Uniformly entered

Arranged forsimplification

Complete

Accurate

15-6

Field Editing

Speed without accuracy won’t help the manager choose the right direction.

•Field editing review•Entry gaps identified•Callbacks made•Validate results

15-7

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

15-8

Sample Codebook

15-9

Precoding

15-10

Coding Open-Ended Questions

6. What prompted you to purchase your most recent life insurance policy?

_______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________

15-11

Coding Rules

Categories should be

Appropriate to the research problemExhaustive

Mutually exclusive Derived from one classification principle

15-12

Content Analysis

QSR’s XSight software for content

analysis.

15-13

Content Analysis

15-14

Types of Content Analysis

Syntactical

Propositional

Referential

Thematic

15-15

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

identifiedB. Management 1. Training C. Customer

1. Buying processes 2. Other

3. No action area identified

D. Environmental conditions

E. TechnologyF. Other

102073

15

1285

20

15-16

Handling “Don’t Know” Responses

Question: Do you have a productive relationship with your present salesperson?

Years of Purchasing Yes No Don’t Know

Less than 1 year 10% 40% 38%

1 – 3 years 30 30 32

4 years or more 60 30 30

Total100%n = 650

100%n = 150

100%n = 200

15-17

Data Entry

Database Programs

Optical Recognition

Digital/Barcodes

Voicerecognition

Keyboarding

15-18

Missing Data

Listwise Deletion

Pairwise Deletion

Replacement

15-19

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 15aDescribing Data Statistically

McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved. 

15-21

Frequencies

Unit Sales Increase

(%) Frequency PercentageCumulative Percentage

56789

Total

123219

11.122.233.322.211.1

100.0

11.133.366.788.9100Unit Sales

Increase (%) Frequency Percentage

Cumulative Percentage

Origin, foreign (1)

678

122

11.122.222.2

11.133.355.5

Origin, foreign (2)

5679

Total

11119

11.111.111.111.1

100.0

66.677.788.8

100.0

A

B

15-22

Distributions

15-23

Characteristics of Distributions

15-24

Measures of Central Tendency

Mean ModeMedian

15-25

Measures of Variability

Interquartile range

Quartile deviation

Range

Standard deviation

Variance

15-26

Summarizing Distribution Shape

15-27

Variable Population Sample Mean

µ

X

Proportion

p

Variance

2

s2

Standard deviation

s

Size

N

n

Standard error of the mean

x

Sx

Standard error of the proportion

p

Sp

__

_

Symbols

15-28

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

CHAPTER 16 Exploring, Displaying, and Examining Data

Learning Objectives:

• That exploratory data analysis techniques provide insights and data diagnostics by emphasizing visual representations of the data.

• How cross-tabulation is used to examine relationships involving categorical variables, serves as a framework for later statistical testing, and makes an efficient tool for data visualization and later decision-making

16-30

Exploratory Data Analysis

ConfirmatoryExploratory

16-31

Data Exploration, Examination, and Analysis in the Research Process

16-32

Frequency of Ad Recall

Value Label Value Frequency Percent Valid Cumulative Percent Percent

16-33

Bar Chart

16-34

Pie Chart

16-35

Frequency Table

16-36

Histogram

16-37

Stem-and-Leaf Display

455666788889124667990223567802268

240183106336

3

68

56789101112131415161718192021

16-38

Pareto Diagram

16-39

Boxplot Components

16-40

Diagnostics with Boxplots

16-41

Boxplot Comparison

16-42

Mapping

16-43

Geograph: Digital Camera Ownership

16-44

SPSS Cross-Tabulation

16-45

Percentages in Cross-Tabulation

16-46

Guidelines for Using Percentages

Averaging percentages

Use of too large percentages

Using too small a base

Percentage decreases can never exceed 100%

16-47

Cross-Tabulation with Control and Nested Variables

16-48

Automatic Interaction Detection (AID)

16-49

Exploratory Data Analysis

This Booth Research Services ad suggests that the researcher’s role is to make sense of data displays.

Great data exploration and analysis delivers insight from data.

16-50

Key Terms

• Automatic interaction detection (AID)

• Boxplot• Cell• Confirmatory data

analysis• Contingency table• Control variable• Cross-tabulation• Exploratory data analysis

(EDA)

• Five-number summary• Frequency table• Histogram• Interquartile range (IQR)• Marginals• Nonresistant statistics• Outliers• Pareto diagram• Resistant statistics• Stem-and-leaf display

Working with Data Tables

McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved. 

16-52

Original Data Table

Our grateful appreciation to eMarketer for the use of their table.

16-53

Arranged by Spending

16-54

Arranged by No. of Purchases

16-55

Arranged by Avg. Transaction, Highest

16-56

Arranged by Avg. Transaction, Lowest

REFERENCES: