1. Data Collection Methods: Questionnaires
Chapter 9
Data Collection Methods: Questionnaires 1

2. Questionnaire Design Definition Steps
A questionnaire is a pre-formulated, written set of questions to which the respondent records his answers
Steps
Determine the content of the questionnaire
Determine the form of response
Determine the wording of the questions
Determine the question sequence
Write cover letter

3. 1. Questionnaire content
Framework
Need information for all constructs in framework
Measurement: Operationalizing
Objective construct:
1 element/items
=> 1 question
Subjective construct:
multiple elements/items
=> multiple questions

4. 2. Response format Closed vs. Open-ended questions
Closed questions
Helps respondents to make quick decisions
Helps researchers to code
Open-ended question
First: unbiased point of view
Final: additional insights
Complementary to closed question: for interpretation purpose
Cfr. Measurement: Response scales

5. 3. Question wording Avoid double-barreled questions
Avoid ambiguous questions and words
Use of ordinary words
Avoid leading or biasing questions
Social desirability
Avoid recall depended questions

6. Question wording Use positive and negative statements
Dresdner delivers high quality banking service
Dresdner has poor customer operational support
Avoid double negatives
Limit the length of the questions
Rules of thumb:
< 20 words
< one full line in print

7. 4. Question sequence
Personal and sensitive data at the end

8. 5. Cover letter
The cover letter is the introductory page of the questionnaire
It includes:
Identification of the researcher
Motivation for respondents to fill it in
Confidentiality
Thanking of the respondent

9. Data Collection Methods: Introduction and Interviews
Chapter 7
Data Collection Methods: Introduction and Interviews 9
© 2012 John Wiley & Sons Ltd.

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Sources of Data
Primary data: information obtained firsthand by the researcher on the variables of interest for the specific purpose of the study.
Examples: individuals, focus groups, panels
Secondary data: information gathered from sources already existing.
Examples: company records or archives, government publications, industry analyses offered by the media, web sites, the Internet, and so on.
© 2012 John Wiley & Sons Ltd.

11. Interviews Unstructured interviews: Structured interviews:
the interviewer does not enter the interview setting with a planned sequence of questions to be asked of the respondent.
Structured interviews:
Conducted when it is known at the outset what information is needed.
The interviewer has a list of predetermined questions to be asked of the respondents either personally, through the telephone, or via the computer.

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Personal interview
Advantages
Can clarify doubts about questionnaire
Can pick up non-verbal cues
Relatively high response/cooperation
Special visual aids and scoring devises can be used
Disadvantages
High costs and time intensive
Geographical limitations
Response bias / Confidentiality difficult to be assured
Some respondents are unwilling to talk to strangers
Trained interviewers
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Telephone interview
Advantages
Discomfort of face to face is avoided
Faster / Number of calls per day could be high
Lower cost
Disadvantages
Interview length must be limited
Low response rate
No facial expressions
© 2012 John Wiley & Sons Ltd.

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Self-administered
Advantages
Lowest cost option
Expanded geographical coverage
Requires minimal staff
Perceived as more anonymous
Disadvantages
Low response rate in some modes
No interviewer intervention possible for clarification
Cannot be too long or complex
Incomplete surveys
© 2012 John Wiley & Sons Ltd.

15. Projective Methods Word association techniques:
Asking the respondent to quickly associate a word with the first thing that comes to mind.
Often used to get at true attitudes and feelings.
Thematic apperception tests (TAT):
Call for respondent to weave a story around a picture that is shown.
To trace patterns and personality characteristics of respondents.
Inkblot tests:
Form of motivational research, uses colored inkblots that are interpreted by respondents.

16. Data Collection Methods: Observation
Chapter 8
Data Collection Methods: Observation 16

17. Observation
Observation involves going into ‘the field’, - the factory, the supermarket, the waiting room, the office, or the trading room - watching what workers, consumers, or day traders do, and describing, analyzing, and interpreting what one has seen.

18. Examples Shadowing a Wall Street broker engaged in his daily routine.
Observing in-store shopping behavior of consumers via a camera.
Sitting in the corner of an office to observe how a merchant bank trader operates.
Working in a plant to study factory life.
Studying the approach skills of sales people disguised as a shopper.

19. Key dimensions characterizing type of observation
Controlled versus Uncontrolled Observational Studies
Participant versus Non-Participant Observation
Structured versus Unstructured Observational Studies
Concealed versus Unconcealed observation

20. Participant Observation
The participatory aspect:
Complete participation
Moderate participation
Active participation
To what extent should I participate?

21. Participant Observation
The observation aspect
Obtaining permission
Finding a ‘sponsor’
Establishing rapport

22. What to observe? Descriptive observation stage: Space Objects Actors
Feelings
Events
Spradly, 1980

23. What to observe? Focused and selective observation stage:
Look for a story line
Sort out regular from irregular activities
Look for variation in the storyline
Look for negative cases or exceptions
Develop a plan for systematic observation if needed
DeWalt and DeWalt, 2002

24. Structured observation
Looks selectively at predetermined phenomena
Different levels of structure

25. Coding schemes Focus Objective Ease of use
Mutually exclusive and collectively exhaustive

26. Standard Coding Schemes
Simple checklist
Sequence record
Sequence record on time scale

27. Measurement of Variables: Scaling, Reliability, Validity
Chapter 12
Measurement of Variables: Scaling, Reliability, Validity 27

28. Scale
Scale: tool or mechanism by which individuals are distinguished as to how they differ from one another on the variables of interest to our study.

29. Nominal Scale
A nominal scale is one that allows the researcher to assign subjects to certain categories or groups.
What is your department?
O Marketing O Maintenance O Finance
O Production O Servicing O Personnel
O Sales O Public Relations O Accounting
What is your gender?
O Male
O Female

30. Nominal Scale

31. Ordinal Scale
Ordinal scale: not only categorizes variables in such a way as to denote differences among various categories, it also rank-orders categories in some meaningful way.
What is the highest level of education you have completed?
O Less than High School
O High School/GED Equivalent
O College Degree
O Masters Degree
O Doctoral Degree

32. Ordinal Scale

33. Interval Scale
Interval scale: whereas the nominal scale allows us only to qualitatively distinguish groups by categorizing them into mutually exclusive and collectively exhaustive sets, and the ordinal scale to rank-order the preferences, the interval scale lets us measure the distance between any two points on the scale.

34. Interval scale
Circle the number that represents your feelings at this particular moment best. There are no right or wrong answers. Please answer every question.
1. I invest more in my work than I get out of it
I disagree completely I agree completely
2. I exert myself too much considering what I get back in return
3. For the efforts I put into the organization, I get much in return

35. Interval scale

36. Ratio Scale
Ratio scale: overcomes the disadvantage of the arbitrary origin point of the interval scale, in that it has an absolute (in contrast to an arbitrary) zero point, which is a meaningful measurement point.
What is your age?

37. Ratio Scale

38. Properties of the Four Scales
Insert Table 12.1 here

39. Goodness of Measures

40. Validity

41. Reliability
Reliability of measure indicates extent to which it is without bias and hence ensures consistent measurement across time (stability) and across the various items in the instrument (internal consistency).

42. Stability
Stability: ability of a measure to remain the same over time, despite uncontrollable testing conditions or the state of the respondents themselves.
Test–Retest Reliability: The reliability coefficient obtained with a repetition of the same measure on a second occasion.
Parallel-Form Reliability: Responses on two comparable sets of measures tapping the same construct are highly correlated.

43. Internal Consistency
Internal Consistency of Measures is indicative of the homogeneity of the items in the measure that tap the construct.
Interitem Consistency Reliability: This is a test of the consistency of respondents’ answers to all the items in a measure. The most popular test of interitem consistency reliability is the Cronbach’s coefficient alpha.
Split-Half Reliability: Split-half reliability reflects the correlations between two halves of an instrument.

44. Chapter 13
Sampling 44

45. Sampling
Sampling: the process of selecting a sufficient number of elements from the population, so that results from analyzing the sample are generalizable to the population.

46. Relevant Terms - 1
Population refers to the entire group of people, events, or things of interest that the researcher wishes to investigate.
An element is a single member of the population.
A sample is a subset of the population. It comprises some members selected from it.

47. Relevant Terms - 2
Sampling unit: the element or set of elements that is available for selection in some stage of the sampling process.
A subject is a single member of the sample, just as an element is a single member of the population.

48. Relevant Terms - 3
The characteristics of the population such as µ (the population mean), σ (the population standard deviation), and σ2 (the population variance) are referred to as its parameters. The central tendencies, the dispersions, and other statistics in the sample of interest to the research are treated as approximations of the central tendencies, dispersions, and other parameters of the population.

49. Statistics versus Parameters

50. Advantages of Sampling
Less costs
Less errors due to less fatigue
Less time
Destruction of elements avoided

51. The Sampling Process Major steps in sampling: Define the population.
Determine the sample frame
Determine the sampling design
Determine the appropriate sample size
Execute the sampling process

52. Sampling Techniques Probability versus nonprobability sampling
Probability sampling: elements in the population have a known and non-zero chance of being chosen

53. Sampling Techniques Probability Sampling Nonprobability Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Nonprobability Sampling
Convenience Sampling
Judgment Sampling
Quota Sampling

54. Simple Random Sampling
Procedure
Each element has a known and equal chance of being selected
Characteristics
Highly generalizable
Easily understood
Reliable population frame necessary

55. Systematic sampling Procedure Characteristics
Each nth element, starting with random choice of an element between 1 and n
Characteristics
Idem simple random sampling
Easier than simple random sampling
Systematic biases when elements are not randomly listed

56. Cluster sampling Procedure Characteristics
Divide of population in clusters
Random selection of clusters
Include all elements from selected clusters
Characteristics
Intercluster homogeneity
Intracluster heterogeneity
Easy and cost efficient
Low correspondence with reality

57. Stratified sampling Procedure Characteristics
Divide of population in strata
Include all strata
Random selection of elements from strata
Proportionate
Disproportionate
Characteristics
Interstrata heterogeneity
Intrastratum homogeneity
Includes all relevant subpopulations

58. (Dis)proportionate stratified sampling
Number of subjects in total sample is allocated among the strata (dis)proportional to the relative number of elements in each stratum in the population
Disproportionate case:
strata exhibiting more variability are sampled more than proportional to their relative size
requires more knowledge of the population, not just relative sizes of strata

59. Example

60. Overview

61. Choice Points in Sampling Design

62. Tradeoff between precision and confidence
We can increase both confidence and precision by
increasing the sample size

63. Sample size: guidelines
In general: 30 < n < 500
Categories: 30 per subcategory
Multivariate: 10 x number of var’s
Experiments: 15 to 20 per condition

64. Sample Size for a Given Population Size