1. Sampling
Fundamentals
Lecture 7

2. Chapter 10
Sampling: Sample, design and sample
size
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3. Learning Objectives
• Distinguish between a census and a sample
• Describe the steps in the sampling process
• Define target population for a given research
problem
• Identify sampling frames
• Differentiate between probability & non-
probability sampling techniques
• Assess non-response problems

4. Review
• What should we ask? Relevance & accuracy
• Open: free response
• Closed: fixed, limited alternative / options
responses
• Which is better & why?
• Also depends on how you will ask eg mail,
telephone, personal
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5. Example
• How much do you depend on advertising
sources to get information about products
that you are likely to buy?
• Very little 1234567 A lot
• What does, Infrequently, occasionally,
frequently mean?
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6. The Marketing Research Process

7. Sampling terminology
• Sample: a subset, or some part, of a larger
population.
• Population: any complete group of entities
that share some common set of
characteristics.
• Population element: an individual member of
a population.
• Census: an investigation of all the individual
elements that make up a population.
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8. Why sample?
• Pragmatic reasons: budget and time
constraints
• Accurate and reliable results: most properly
selected samples give sufficiently accurate
results
– Relationship between sample size and
accuracy
• Destruction of test units.
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9. How sampling works
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10. Stages in the selection of a
sample
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11. Reasons for Using a Sample
• Time
• Cost
• Inability to study the whole population

12. Identify the Target Population
Define the Target Population
• Precise statement of who should and should not be included in the
sample
Sampling Elements
– Object about which or from which the information is desired
e.g., males, females, over 18, buys the groceries etc.
Sampling Units
– An element, or a unit containing the element that is available for
selection at some stage
e.g., households, small businesses, schools etc.
Extent
– Geographical boundaries
e.g., western metropolitan region of Melbourne, national, study of
two countries
Time period under consideration

13. Identify the Target Population
cont.
– Look to the research objectives
– Consider the alternatives
– Know your market
– Consider the appropriate sampling unit
– Specify clearly what is excluded
– Don’t over-define
– Should be reproducible i.e., consider
convenience

14. Defining the target population
• What is the relevant population?
– Survey of students’ choice of university
might target university students, but should
also include friends and parents who exert
an influence in the decision.
– Operational definition required
• Vital to carefully define the target population.
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15. The sampling frame
• Sampling frame: a list of elements from which
a sample may be drawn.
– Example: student email list, membership
list
• List brokers provide lists of specific
populations.
• Sampling frame error occurs when certain
sample elements are not listed or accurately
represented in a sampling frame.
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16. The use of sampling frames in
marketing research
• An accurate sampling frame will not always
exist.
– Potentially sacrifice accuracy by using
more practical methods like the shopping
mall intercept to sample consumers.
– Telephone directories may be incomplete.
• Using the wrong sample frame will result in
the inclusion of respondents who should not
be part of the population.
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17. Sampling units
• Does not have to be a person.
– An airline may wish to select individual
passengers as the sampling unit or select
certain flights as the sampling unit.
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18. Determine the Sampling Frame
• Representation of the target population
• List or set of directions for identifying the target
population e.g., telephone book, association
directory, mail list, map
• Sampling frame error
– The list may omit some elements of the population or
include other elements which do not belong
• Dealing with population sampling frame
differences
– Subset problem
– Superset problem
– Intersection problem

19. Selecting a Sampling Technique:
Non-Probability
• Personal judgement of the researcher is used
rather than chance to select elements
• Difficult to generalise result to the population
Examples of the methods
used with this techniques will be
covered shortly

20. Selecting a Sampling Technique:
Probability
• Sampling units are selected by chance
• Pre-specifying every potential sample of a given
size that could be drawn from the population
• Require precise definition of the target population
and sampling frame
• Able to make inferences about the target
population
Examples of the methods
used with this techniques will be
covered shortly

21. How do you go about selecting a random sample?
William Burlace, Director, Media Services
Roy Morgan Research

22. Determine the Sample Size:
Qualitative Factors
• Number of elements to be included in the study
• Qualitative factors to consider:
– Importance of the decision
– Number of variables
– Nature of the analysis
– Sample size used in similar studies
– Incidence rates Quantitative Factors will be
– Completion rates covered later in this lecture
– Resource constraints

23. Execute the Sampling Process
• Detail specification of how the sampling,
design decisions with respect to the
population, sampling frame, sampling units,
sampling techniques and sample size are to
be implemented.

24. Random sampling and non–sampling
errors
• Random sampling error: the difference
between sample result and the result of a
census conducted using identical procedures.
– Statistical fluctuation due to chance
variations in elements selected for a
sample.
– Example, student population has a true
mean income of $20 000 but a sample
shows a mean income of $10 000.
• Conceivably occurred by accident: random
error.
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25. Random sampling and
non–sampling errors
• Beyond researcher’s ability to control random
error.
• But, as sample size increases, random
sampling error decreases.
• The more people we ask, the more likely the
sample result is going to reflect the true
result.
– With statistical inference, we are able to
estimate the probability of random error.
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26. Random sampling and non–sampling
errors
• Non–sampling (systematic) error results from
some imperfect aspect of the research design
such as mistakes in sample selection,
sampling frame error, or non–responses.
– Errors not due to chance fluctuations, but
due to errors resulting from the researcher.
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27. Less than perfectly representative
samples
• Non–response error: statistical differences
between a survey including only those who
responded and a perfect survey that would
also include those who failed to respond.
– Sample is less likely to be perfectly
representative.
– Example: consumers who are busier are
less likely to respond to a survey than
those with more spare time.
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28. Errors associated with sampling
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29. Probability versus non–probability
sampling
• Probability sampling: a sampling technique in
which every member of the population has a
known, non–zero probability of selection.
• Non–probability sampling: a sampling
technique which units of the sample are
selected on the basis of personal judgement
or convenience.
– The probability of any particular member of
the population being chosen is unknown.
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30. Probability sampling
• Since this process is random, error related to
researcher judgement is eliminated.
• There are various probability sampling
methods:
– Simple random sampling
– Systematic sampling
– Stratified sampling.
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31. Probability Sampling: Simple
Random Sampling (SRS)
• Each element in the population has a known and
equal chance of selection
• A sample is drawn by a random procedure from a
sampling frame
• Easily understood
• Generalise to the population
• Difficult to construct a sampling frame
• May or may not result in representative sample
• Accuracy-cost trade-off

32. Simple random sampling
• A sampling procedure that assures each
element in the population of an equal chance
of being included in the sample.
– Example: drawing names from a hat
• Random number generator.
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33. Systematic sampling
• A sampling procedure in which a starting
point is selected by a random process and
then every nth number on the list is selected.
• Requires sampling frame
• Results appear to be random if there is no
other systematic pattern to the list.
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34. Stratified sampling
• A sampling procedure in which simple
random sub–samples that are more or less
equal on some characteristic are drawn from
within each stratum of the population.
• Involves dividing sampling frame into strata
then randomly sampling within each strata.
– Example: random sampling within the male
and female strata.
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35. Probability Sampling: Stratified
Sampling
• Population split into sub-populations
• Strata are mutually exclusive and collectively
exhaustive
• Then SRS from each stratum to select the
elements
– Within stratum – homogeneous
– Each(between) stratum - heterogeneous
– Proportional stratified sampling
– Disproportional324-325 – Proportional stratified sampling
Refer to pp.
stratified sampling

36. Probability sampling
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37. Proportional versus disproportional
sampling
• Proportional stratified sample: a stratified
sample in which the number of sampling units
drawn from each stratum is in proportion to
the population size of that stratum.
• Disproportional stratified sample: a stratified
sample in which the sample size for each
stratum is allocated according to analytical
considerations.
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38. Cluster sampling
• An economically efficient sampling technique
in which the primary sampling unit is not the
individual element in the population but a
large cluster of elements.
• Clusters are selected randomly.
– Example: geographic cluster or area
sample
• Unlike a strata, a cluster should be as
heterogeneous as the population itself.
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39. Probability Sampling:
Cluster Sampling
• Target population split into mutually exclusive and
collectively exhaustive sub-populations
• Than random sample of clusters is selected based
on SRS
• A sample from each cluster
– Within cluster – heterogeneous
– Each(Between) cluster - homogeneous
• Cost effective e.g., area sampling

40. Non–probability sampling
• Sometimes bias resulting from judgement in
selection is necessary because marketers do
not always have an accurate list available
from which to select respondents.
• There are various non–probability sampling
methods:
– convenience sampling
– judgement sampling
– quota sampling
– snowball sampling.
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41. Convenience sampling
• The sampling procedure of obtaining those
people or units that are most conveniently
available.
• Convenient but may be unwilling or
unrepresentative
– Example: lecturer who uses students
• Best use for exploratory research but
inappropriate for projecting results.
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42. Non-probability Sampling:
Judgmental Sampling
• Selection based on researcher judgement
• Inexpensive, quick, can be used for exploratory
research, or pre-test questionnaire
• Selection bias present, not representative, can not
generalise to target population

43. Judgement sampling
• A non–probability sampling technique in
which an experienced individual selects the
sample based on personal judgement about
some appropriate characteristic of the sample
member.
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45. Non-probability Sampling:
Snowball Sampling
• Initial group of respondents is selected
• These respondents are asked to identify others
who belong to the target population of interest
• Referrals will have demographic and
psychographic characteristics that are more
similar to person referring than would be by
chance
e.g., minority groups, people involved in a specialist sport
or hobby

46. Non-probability Sampling:
Convenience Sampling
• Selection of sampling units is left to the interviewer
• Inexpensive, quick, can be used for exploratory
research
• Selection bias present, not representative, can not
generalise to the population
e.g., students at uni, shopping centres without qualifying
respondents, questionnaires in magazines or restaurants

47. Non-Probability Sampling: Quota
Sampling
• Develop quotas of population elements e.g., gender,
age
• Respondents are then selected based on
convenience or judgement
• Not representative but could be relevant
• Selection bias
• Lower cost and greater convenience

48. Non-Response in Sampling
• Refusals
• Unwillingness / inability of people included in the
sample to participate
• Reduce refusals by:
– Prior notification
– Motivating the respondents
– Incentives
– Good questionnaire design and administration

49. Non-response in Sampling cont.
• Not at homes or inaccessible
• Potential respondents are not at home when
contact is attempted
• Use ‘call backs’

50. Quota sampling
• A non–probability sampling procedure that
ensures various subgroups of a population
will be represented to the extent that the
investigator desires.
• Introduces bias because quota samples tend
to include people who are easily found and
willing to be interviewed.
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51. Snowball sampling
• A sampling procedure in which initial
respondents are selected by probability
methods and additional respondents are
obtained from information provided by the
initial respondents.
• Locate members of rare populations by
referrals.
– Example: stamp collectors and adult
croquet players.
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52. What is the appropriate sample
design?
• There are a number of sampling criteria to
evaluate each sample design:
– Degree of accuracy
• Probability methods are better for conclusive
projects that demand accuracy.
– Resources
• Non–probability methods are better for projects
with financial and human resource constraints.
– Time
• Simple sample design is better for projects with
time constraints.
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53. What is the appropriate sample
design?
• There are a number of sampling criteria to
evaluate each sample design:
– Advance knowledge of the population
• Lack of adequate lists rule out systematic and
stratified sampling.
– National versus local project
• Cluster sample better when population
elements are unequally distributed
geographically.
– Need for statistical analysis
• Non–probability methods do not allow statistical
analysis to project data beyond the sample.
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54. What is the appropriate sample
design?
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55. What is the appropriate sample
design?
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56. Random error and sample size
• Random sampling error varies with samples
of different sizes.
– Increasing sample size increases
accuracy.
• But since every project has a budget, we can
ask a certain number of people and be
confident of getting the same results had we
asked many more people.
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57. Factors in determining sample size
• Three factors affect sample size:
– The variance or heterogeneity of the
population.
• Only a small sample is required if the
population is homogenous.
– The magnitude of acceptable error.
• Higher precision requires a larger sample.
– The confidence level.
• Higher confidence requires a larger sample.
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58. Determining sample size on the
basis of judgement
• Sample size may also be determined on the
basis of managerial judgements.
– Rely on experience and sample size
similar to previous studies.
• Researcher is governed by more practical
restrictions such as budget and time
limitations.
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59. Appropriate Sample Design
• Degree of accuracy
• Resources
• Time
• Advance knowledge of the population
• National vs. local project
• Need for statistical analysis

60. Special Cases
• Web-based samples
– Yields a high response rate because panel
members have agreed to cooperate with the
research organisation and are compensated for
their time.
• Shopping centre sampling
– Shopping centre selection
– Sample locations within a centre
– Time sampling
– Sampling people versus shopping visits

61. Sampling in an International
Context
• Key Issues:
– Absence of information on sampling frames
– May need to consider regions rather than an
individual country
– Selection of respondents
– Generalisability of results across geographic
regions
– High cost of multi-country research
– Appropriateness of different sampling
techniques
– Use of the same sampling technique in different
countries or regions
– Sample size

62. Chapter 11
Editing and coding: Transforming raw
data into information
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63. Stages of data analysis
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64. Editing
• Editing is the process of checking the
completeness, consistency and legibility of
data and making the data ready for coding
and transfer to storage.
• Coding is the process of assigning a
numerical score or other character symbol to
previously edited data.
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65. Field editing
• Preliminary editing done by field supervisor
on the day of interview.
• Purpose of field editing is to catch technical
omissions, to check legibility of handwriting,
and clarify responses that are logically or
conceptually inconsistent.
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66. In–house editing
• In–house editing rigorously investigates the
results of data collection.
• In–house editor’s tasks:
– Adjust inconsistent or contradictory
responses so that the answers will not be a
problem for coders and keyboard
operators.
– Checks for adherence to the data
collection framework.
– Checks for logically consistent responses.
– Edit for completeness.
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67. Facilitating the coding process
• Editing and tabulating ‘don’t know’ answers
– An alternative is to record all ‘don’t knows’
as a separate category.
• Write a code book.
– A systematic procedure for assessing the
questionnaires should be developed.
– Clear and unambiguous instructions on
how to deal with each type of response.
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68. Coding
• Codes are rules for interpreting, classifying,
and recording data in the coding process.
• A field is a collection of characters that
represent a single type of data.
• A record is a collection of related fields.
• A file is a collection of related records.
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69. Pre– coding fixed–alternative
questions
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70. Pre–coding fixed–alternative
questions
• The code for each response will be used by
keyboard operator for data entry.
– Example: a question with three possible
answers are pre–coded 1, 2, 3.
• Pre–coding can be used if the researcher
knows what the answer categories will be
prior to data collection.
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71. Coding open–ended questions
• Purpose of pre–coding open–ended
questions is to reduce the large number of
individual responses to a few general
categories of answers that can be assigned
numerical codes.
– Similar answers should be placed in a
general category and assigned the same
code.
– Code building is based on thoughts, not
just words.
– Test tabulation.
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72. Coding open–ended questions
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73. Computerised data processing
• Production coding is the process of
transferring the data from the questionnaire to
the storage medium.
• Optical scanning system and Intelligent
Character Recognition are computerised
methods to read questionnaire responses
directly.
• Otherwise, data entry is used to transfer data
from the research project to computers.
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