1. Sample Designs and Sampling Procedures
Research Methodology
Sample Designs and Sampling Procedures

2. Sampling Terminology Sample Population or universe Population element
Census

3. Sample
Subset of a larger population

4. Population Any complete group
A population is the total collection of elements about which we wish to make some inferences.
Any complete group
People
Sales territories
Stores

5. Census
Investigation of all individual elements that make up a population
A census is a count of
all the elements in a
population.

6. Population Frame
A list, map, directory, or other source used to represent the population
Overregistration -- the frame contains all members of the target population and some additional elements
Example: using the chamber of commerce membership directory as the frame for a target population of member businesses owned by women.
Underregistration -- the frame does not contain all members of the target population.
Example: using the chamber of commerce membership directory as the frame for a target population of all businesses. 5

7. Reasons for Taking a Census
Eliminate the possibility that a random sample is not representative of the population.
The person authorizing the study is uncomfortable with sample information. 4

8. Sampling
The process of using a small number of items or parts of larger population to make a conclusions about the whole population

9. Reasons for Sampling Sampling can save money. Sampling can save time.
For given resources, sampling can broaden the scope of the data set.
Because the research process is sometimes destructive, the sample can save product.
If accessing the population is impossible; sampling is the only option.
© 2002 Thomson / South-Western 3

10. Population, sample and individual cases
Selecting samples
Population, sample and individual cases
Source: Saunders et al. (2009)
Figure 7.1 Population, sample and individual cases

11. Sampling- a valid alternative to a census when
The need to sample
Sampling- a valid alternative to a census when
A survey of the entire population is impracticable
Budget constraints restrict data collection
Time constraints restrict data collection
Results from data collection are needed quickly

12. Overview of sampling techniques
Source: Saunders et al. (2009)
Figure 7.2 Sampling techniques

13. Stages in the Selection of a Sample Define the target population
Select a sampling frame
Determine if a probability or nonprobability
sampling method will be chosen
Plan procedure
for selecting sampling units
Determine sample size
Select actual sampling units
Conduct fieldwork

14. Target Population
The specific , complete group to research project

15. Sampling Frame
A sample frame is the listing of all population elements from which the sample will be drawn.

16. Sampling Units Group selected for the sample
Primary Sampling Units (PSU)
Secondary Sampling Units
Tertiary Sampling Units

17. Random vs Nonrandom Sampling
Every unit of the population has the same probability of being included in the sample.
A chance mechanism is used in the selection process.
Eliminates bias in the selection process
Also known as probability sampling
Nonrandom Sampling
Every unit of the population does not have the same probability of being included in the sample.
Open the selection bias
Not appropriate data collection methods for most statistical methods
Also known as nonprobability sampling 6

18. Random Sampling Error
The difference between the sample results and the result of a census conducted using identical procedures
Statistical fluctuation due to chance variations

19. Systematic Errors Nonsampling errors Unrepresentative sample results
Not due to chance
Due to study design or imperfections in execution

20. Bias
Bias is a systematic error that can prejudice your evaluation findings in some way.
Sampling bias is consistent error that arises due to the sample selection.
For survey researchers, sampling biases for averages derive from three sources: (1) imperfect sampling frames, (2) nonresponse bias, and (3) measurement error.
For example, distributing a questionnaire at the end of a 3-day conference is likely to include more people who are committed to the conference so their views would be overrepresented.

21. Errors Associated with Sampling
Sampling frame error
Random sampling error
Nonresponse error

22. Two Major Categories of Sampling
Probability sampling
Known, nonzero probability for every element
Nonprobability sampling
Probability of selecting any particular member is unknown

23. Sampling Non Probability Samples
A non probability sample relies on the researcher selecting the respondents.
They are considered to be:
Interpretive
Subjective
Not scientific
Qualitative
Unrepresentative

24. Nonprobability Sampling
Convenience
Judgment
Quota
Snowball

25. Sampling Probability Samples
Probability samples offer each respondent an equal probability or chance at being included in the sample.
They are considered to be:
Objective
Empirical
Scientific
Quantitative
Representative

26. Probability Sampling Simple random sample Systematic sample
Stratified sample
Cluster sample
Multistage area sample

27. Convenience Sampling
Convenience samples are nonprobability samples where the element selection is based on ease of accessibility. They are the least reliable but cheapest and easiest to conduct.
Examples include informal pools of friends and neighbors, people responding to an advertised invitation, and “on the street” interviews.

28. Judgment Sampling Also called purposive sampling
An experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member

29. Quota Sampling
Ensures that the various subgroups in a population are represented on pertinent sample characteristics
To the exact extent that the investigators desire
It should not be confused with stratified sampling.

30. Snowball Sampling A variety of procedures
Initial respondents are selected by probability methods
Additional respondents are obtained from information provided by the initial respondents

31. Simple Random Sampling
A sampling procedure that ensures that each element in the population will have an equal chance of being included in the sample

32. Simple Random Sampling: Random Number Table9 4 3 7 8 6 1 5 2
N = 30
n = 6 10

33. Simple Random Advantages Disadvantages
Easy to implement with random dialing
Disadvantages
Requires list of population elements
Time consuming
Larger sample needed
Produces larger errors
High cost
In drawing a sample with simple random sampling, each population element has an equal chance of being selected into the samples. The sample is drawn using a random number table or generator. This slide shows the advantages and disadvantages of using this method.
The probability of selection is equal to the sample size divided by the population size.
Exhibit 14-6 covers how to choose a random sample. The steps are as follows:
Assign each element within the sampling frame a unique number.
Identify a random start from the random number table.
Determine how the digits in the random number table will be assigned to the sampling frame.
Select the sample elements from the sampling frame.

34. Systematic Sampling A simple process
Every nth name from the list will be drawn

35. Systematic Sampling Convenient and relatively easy to administer
Population elements are an ordered sequence (at least, conceptually).
The first sample element is selected randomly from the first k population elements.
Thereafter, sample elements are selected at a constant interval, k, from the ordered sequence frame. k = N n ,
where :
sample size
population size
size of selection interval 14

36. Systematic Advantages Disadvantages Simple to design
Easier than simple random
Easy to determine sampling distribution of mean or proportion
Disadvantages
Periodicity within population may skew sample and results
Trends in list may bias results
Moderate cost
In drawing a sample with systematic sampling, an element of the population is selected at the beginning with a random start and then every Kth element is selected until the appropriate size is selected.
The kth element is the skip interval, the interval between sample elements drawn from a sample frame in systematic sampling. It is determined by dividing the population size by the sample size.
To draw a systematic sample, the steps are as follows:
Identify, list, and number the elements in the population
Identify the skip interval
Identify the random start
Draw a sample by choosing every kth entry.
To protect against subtle biases, the research can
Randomize the population before sampling,
Change the random start several times in the process, and
Replicate a selection of different samples.

37. Stratified Sampling Probability sample
Subsamples are drawn within different strata
Each stratum is more or less equal on some characteristic
Do not confuse with quota sample

38. Stratified Random Sample: Population of FM Radio Listeners
years old
(homogeneous within)
(alike)
years old
years old
Hetergeneous
(different)
between
Stratified by Age 13

39. Stratified Advantages Disadvantages Control of sample size in strata
Increased statistical efficiency
Provides data to represent and analyze subgroups
Enables use of different methods in strata
Disadvantages
Increased error if subgroups are selected at different rates
Especially expensive if strata on population must be created
High cost
In drawing a sample with stratified sampling, the population is divided into subpopulations or strata and uses simple random on each strata. Results may be weighted or combined. The cost is high.
Stratified sampling may be proportion or disproportionate. In proportionate stratified sampling, each stratum’s size is proportionate to the stratum’s share of the population.
Any stratification that departs from the proportionate relationship is disproportionate.

40. Cluster Sampling
Population is divided in to into nonoverlapping clusters or areas
Each cluster is a miniature, or microcosm, of the population.
A subset of the clusters is selected randomly for the sample. 16

41. Cluster Sampling Advantages
More convenient for geographically dispersed populations
Reduced travel costs to contact sample elements
Simplified administration of the survey
Unavailability of sampling frame prohibits using other random sampling methods
Disadvantages
Statistically less efficient when the cluster elements are similar
Costs and problems of statistical analysis are greater than for simple random sampling 17

42. Cluster Sampling: Some Test Market Cities
San Jose
Boise
Phoenix
Denver
Cedar Rapids
Buffalo
Louisville
Atlanta
Portland
Milwaukee
Kansas City
San
Diego
Tucson
Grand Forks
Fargo
Sherman-
Dension
Odessa-
Midland
Cincinnati
Pittsfield 18

43. Cluster Advantages Disadvantages
Provides an unbiased estimate of population parameters if properly done
Economically more efficient than simple random
Lowest cost per sample
Easy to do without list
Disadvantages
Often lower statistical efficiency due to subgroups being homogeneous rather than heterogeneous
Moderate cost
In drawing a sample with cluster sampling, the population is divided into internally heterogeneous subgroups. Some are randomly selected for further study.
Two conditions foster the use of cluster sampling:
the need for more economic efficiency than can be provided by simple random sampling, and
2) the frequent unavailability of a practical sampling frame for individual elements.
Exhibit 14-7 provides a comparison of stratified and cluster sampling and is highlighted on the next slide.
Several questions must be answered when designing cluster samples.
How homogeneous are the resulting clusters?
Shall we seek equal-sized or unequal-sized clusters?
How large a cluster shall we take?
Shall we use a single-stage or multistage cluster?
How large a sample is needed?

44. What is the Appropriate Sample Design?
Degree of accuracy
Resources
Time
Advanced knowledge of the population
National versus local
Need for statistical analysis

45. Internet Sampling is Unique
Internet surveys allow researchers to rapidly reach a large sample.
Speed is both an advantage and a disadvantage.
Sample size requirements can be met overnight or almost instantaneously.
Survey should be kept open long enough so all sample units can participate.

46. Internet Sampling Major disadvantage
lack of computer ownership and Internet access among certain segments of the population
Yet Internet samples may be representative of a target populations.
target population - visitors to a particular Web site.
Hard to reach subjects may participate

47. Web Site Visitors Unrestricted samples are clearly convenience samples
Randomly selecting visitors
Questionnaire request randomly "pops up"
Over- representing the more frequent visitors

48. Internet Samples
Recruited Ad Hoc Samples
Opt-in Lists

49. 4. Ratio Scale
Ratio scales are quantitative measures with fixed or true zero. Ratio scales has all four properties of scales that are described above. For example, a weighing scale is a ratio scale. Some other examples are height, price, sales, revenue, profit etc. In all these cases zero implies absence of that characteristic.

50. Strengths Of Multiple-Item Scales
Validity
Content validity
Construct validity
Predictive validity
Reliability
Test-retest reliability
Split-half reliability
Sensitivity
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51. Validity
The validity of a scale is the extent to which it is a true reflection of the underlying variable it is attempting to measure

52. VALIDITY Content”: related to objectives and their sampling.
“Construct”: referring to the theory underlying the target.
“Criterion”: related to concrete criteria in the real world. It can be concurrent or predictive.
“Concurrent”: correlating high with another measure already validated.
“Predictive”: Capable of anticipating some later measure.
“Face”: related to the test overall appearance.

53. Content Validity
Face validity or content validity is the extent to which the content of a measurement scale seems to tap all relevant facets of an issue that can influence respondents’ attitudes

54. Exhibit 9.5 Types of Equivalence
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55. Construct Validity
Construct Validity is the nature of the underlying variable or construct measured by the scale

56. Predictive Validity
Predictive Validity refers to how well the attitude measure provided by the scale predicts some other variable or characteristic

57. Reliability
Reliability measures how consistent or stable the ratings generated by the scale are likely to be

58. Test-Retest Reliability
Test-Retest Reliability measures the stability of ratings over time and involves administering the scale to the same group of respondents at two different times

59. Split-Half Reliability
Split-Half Reliability measures the degree of consistency across items within a scale and can only be assessed for multiple-item scales

60. Sensitivity
Sensitivity focuses specifically on its ability to detect subtle differences in the attitudes being measured

61. Attitudes
Attitudes are similar to beliefs, except that they also involve respondents’ evaluative judgments
For instance, do respondents feel print advertisements for cigarettes should be banned?

62. Attitudes – Conceptually and Operationally
A conceptual definition of attitude may be “a predisposition to respond favorably or unfavorably to a stimulus object”
An operational definition of attitude refers to a person’s attitude towards a particular retail store that may be measured as the total of the person’s expressed degree of agreement, on a 5-point, “strongly agree” to “strongly disagree” scale, with each of a set of 20 evaluative statements about various aspects of the retail store

63. Attitude Scaling Attitudes Measures in which inferences are drawn from
Widely believed to be a key determinant of behavior
Can only be inferred and cannot be directly ascertained
Measures in which inferences are drawn from
Observed overt behavior
Individual's reaction
Performance on objective tasks
Physiological reactions

64. Observing Overt Behavior
Observation of overt behavior is useful when other attitude measurement methods are inconvenient or infeasible
An observation study can be used to ascertain the attitudes of very young children toward a variety of toys
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65. Analyzing Reactions to Partially Structured Stimuli
Projective Techniques
The approach of analyzing reactions to partially structured stimuli involves asking respondents to react to or describe in some fashion, an incomplete, vague stimulus

66. Evaluating Performance on Objective Tasks
To evaluate performance on objective tasks, respondents are asked to complete an ostensibly objective, well-defined task
The nature of their performance is then analyzed to infer their attitudes

67. Monitoring Physiological Responses
Monitoring physiological responses is based on the premise that a person's emotional reactions to a stimulus will be accompanied by corresponding involuntary physiological changes

68. Self-report Measurements of Attitudes
This method involves asking respondents relatively direct questions concerning attitudes toward whatever is of interest to the researcher
The questions are typically in the form of rating scales on which respondents check off appropriate positions that best reflect their feelings

69. Graphic Formats
A graphic rating scale presents a continuum, in the form of a straight line, along which a theoretically infinite number of ratings are possible
Example: Indicate your overall opinion about eBay by placing a  mark at an
appropriate position on the line below.
Very
Bad
Good

70. Itemized Formats
Itemized rating scales have a set of distinct response categories
Any suggestion of an attitude continuum underlying the categories is implicit
They essentially take the form of the multiple-category questions

71. Forced Response Choices
A forced-choice scale does not give respondents the option of expressing a neutral or middle-ground attitude

72. Forced Response Choices (Cont’d)
Indicate your overall opinion about effectiveness of HR by checking one of the following categories:
Very Neither Bad Very
Bad Bad nor Good Good Good
[ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ]
What is your overall rating of HR in comparison with previous organisation you worked?
Much worse Worse About the same Better Much better
( ) ( ) ( ) ( ) ( )

73. Non-forced Response Choices
A non-forced-choice scale give respondents the option to express a neutral attitude

74. Non-forced Response Choices (Cont’d)
Indicate your overall opinion about effectiveness of HR by placing a  mark in the category that best summarizes your feelings.
Very Very
Bad Good
[ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ]
What is your overall rating of HR in comparison with previous organisation you worked?
Much worse Worse Better Much better
( ) ( ) ( ) ( )

75. Unbalanced Response Choices
An unbalanced rating scale that can be used if respondents’ opinions about a subject are anticipated to be predominantly positive

76. Labeled Response Choices

77. Unlabeled Response Choices

78. Exhibit 9.2 Rating Scales with Picture Labels

79. Number of Scale Positions
A scale with a large number of positions will not be meaningful if respondents are unable to make fine mental distinctions with respect to whatever is being measured
More precise measurements should result as the number of scale positions increase

80. Commonly Used Multiple-item Scales
Likert Scale
Semantic-Differential Scale

81. In 1932, Renis Likert invented a measurement method, called the Likert Scales (often called a rating scale), used in questionnaires such as attitude surveys. They allow answers that range from such choices “strongly disagree” to “strongly agree.” It is the most widely used scale in survey research. When responding to a Likert questionnaire item, respondents specify their level of agreement to a statement.

82. for example:
Strongly ,agree ,Slightly Agree,Undecided ,Slightly Agree, Strongly Disagree ,Disagree, Agree
1. My job provides a lot of variety. _____
2. My job provides the opportunity for independent action. _____

83. Table 9.2 Likert Scale Items6. 5. 4. 3. 2. 1.
________
The auction site support
system is confusing
The auction site is not
careful with personal information
The auction site responds
to complaints quickly
Agree
Strongly
The auction site commission
is reasonable
User registration
is complex at this site
The online auction site
contains an abundance of exhibits
Neither Agree nor Disagree
Disagree

84. Semantic-Differential Scale The Semantic Differential (SD) measures people's reactions to stimulus words and concepts in terms of ratings on bipolar scales defined with contrasting adjectives at each end.

85. Exhibit 9.3 Semantic-Differential Scale Items

86. Exhibit 9.4 Pictorial Profiles Based on Semantic-Differential Ratings