16-1

Chapter 16Chapter 16

Data Data PreparationPreparation

andand

DescriptionDescription

16-2

Learning Objectives

Understand . . .

• 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

• use of content analysis to interpret and summarize open questions

16-3

Learning Objectives

Understand . . .

• problems and solutions for “don’t know” responses and handling missing data

• options for data entry and manipulation

16-4

Exhibit 16-1 Data Preparation in the Research Process

16-5

Editing

CriteriaCriteria

Consistent with the intent of Ques

Consistent with the intent of Ques

Uniformly entered

Uniformly entered

Arranged forsimplification

Arranged forsimplification

CompleteComplete

AccurateAccurate

16-6

Field Editing

• Field editing review• Abbreviations to be

clarified• Re interview some• Entry gaps identified• Callbacks made• Validate results

16-7

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

16-8

Central Editing

• Incorrect entries are corrected

• In appropriate or missing replies

• Armchair Interviewing can be detected

16-9

Coding

• Coding involves assigning numbers or other symbols to answers so that the response can be grouped into a limited number of categories

• Categorisation is the process of using rules to partition a body of data

16-10

Codebook Construction

• Code book or coding scheme contains each variable in the study and specifies the application of coding rules to the variable

• Used to promote more accurate and more efficient data entry

16-11

Coding Closed Questions

• Easier to code, record and analyse

• Precoding

16-12

Coding Free Response

• Open ended responses are used for measuring sensitive or disapproved behaviour, encourage natural modes of expression

• Slows the analysis process and increases the opportunity for error

16-13

16-14

Exhibit 16-2 Sample Codebook

16-15

Exhibit 16-3 Precoding

16-16

Exhibit 16-3 Coding Open-Ended Questions

16-17

Coding Rules

Categories should be

Categories should be

Appropriate to the research problem

Exhaustive

Mutually exclusiveDerived from one

classification principle

16-18

Coding Rules

• Appropriateness– Best partitioning of the data for testing

hypotheses and showing relationships– Availability of comparison data

• Exhaustiveness– Does the response cover all the possibilities– Others may be classified further

16-19

Coding Rules

• Mutual Exclusivity– Example of profession– Add more field of second profession

• Derived from one classification principle– Manager and Unemployed

16-20

Content Analysis

QSR’s XSight software for

content analysis.

16-21

Types of Content Analysis

Syntactical

Propositional

Referential

Thematic

16-22

Content Analysis

• Syntactical– Words, phrases, sentences, or paragraphs– What is the meaning they reveal

• Referential– Units described by words or phrases, they

may be objects, events, persons

16-23

Content Analysis

• Propositional units– Are assertions about an object, event, person

and so on

• Thematic – Past, present or the future in responses

16-24

Example

• How can company – customer relations be improved ?

16-25

Exhibit 16-4 & 16-5Open-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

10

20

7

3

15

12

8

5

20

16-26

Don’t Know Responses

• Design better questions in the beginning

• Good rapport will ensure less DK responses– Who developed the Managerial grid concept– Do you believe the current budget is good– Do you like your present job– How often each year you go to the movies

16-27

Exhbit 16-7 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

16-28

Data Entry

Database Programs

Database Programs

Optical Recognition

Optical Recognition

Digital/Barcodes

Digital/Barcodes

Voicerecognition

Voicerecognition

KeyboardingKeyboarding

16-29

Missing Data

Listwise Deletion

Pairwise Deletion

Replacement

16-30

Key Terms

• Bar code• Codebook• Coding• Content analysis• Data entry• Data field• Data file• Data preparation• Database• Don’t know response

• Editing• Missing data• Optical character

recognition• Optical mark recognition• Precoding• Record• Spreadsheet• Voice recognition

16-31

Appendix 16aAppendix 16a

Describing Data Describing Data StatisticallyStatistically

16-32

Frequencies

Unit Sales Increase (%) Frequency Percentage

Cumulative Percentage

5

6

7

8

9

Total

1

2

3

2

1

9

11.1

22.2

33.3

22.2

11.1

100.0

11.1

33.3

66.7

88.9

100

Unit Sales Increase (%) Frequency Percentage

Cumulative Percentage

Origin, foreign (1) 6

7

8

1

2

2

11.1

22.2

22.2

11.1

33.3

55.5

Origin, foreign (2) 5

6

7

9

Total

1

1

1

1

9

11.1

11.1

11.1

11.1

100.0

66.6

77.7

88.8

100.0

A

B

16-33

Distributions

16-34

Characteristics of Distributions

16-35

Measures of Central Tendency

Mean ModeMedian

16-36

Measures of Variability

Interquartile range

Interquartile range

Quartile deviationQuartile deviation

DispersionDispersion

Range

Standard deviationStandard deviation

VarianceVariance

16-37

Summarizing Distributions with Shape

16-38

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

16-39

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