1. Sampling Design

2. Steps in Sampling Process
1. Define the population
2. Identify the sampling frame
3. Select a sampling design
4. Determine the sample size
5. Draw the sample

3. Sampling Design Process Determine Appropriate
Define Population
Determine Sampling Frame
Determine Sampling Procedure
Non-Probability Sampling
Type of Procedure
Convenience
Judgmental
Quota
Probability Sampling
Type of Procedure
Simple Random Sampling
Stratified Sampling
Cluster Sampling
Determine Appropriate
Sample Size
Execute Sampling
Design

4. Terminology Population Element (sampling unit)
The entire body of units of interest to decision makers in a situation.
Element (sampling unit)
one unit from a population
Sampling
The selection of a subset of the population
Sampling Frame
Listing of population from which a sample is chosen
Census
A polling of the entire population
Survey
A polling of the sample

5. SAMPLING Census -- the entire population
most useful is the population ("n") is small
or the cost of making an error are high
Sample -- contacting a portion of the population (e.g., 10% or 25%)
best with a very large population (n)
easiest with a homogeneous population

6. Terminology Parameter Statistic Goal Critical Assumption
The variable of interest
Statistic
The information obtained from the sample about the parameter
Goal
To be able to make inferences about the population parameter from knowledge of the relevant statistic - to draw general conclusions about the entire body of units
Critical Assumption
The sample chosen is representative of the population

7. Population Vs. Sample
Population of Interest
Population Sample
Parameter Statistic
Sample
We measure the sample using statistics in order to draw
inferences about the population and its parameters.

8. Characteristics of Good Samples
Representative
Accessible
Low cost

9. …this (bad)…
Population
Sample

10. …or this (VERY bad)…
Sample
Population

11. Define the Population
It addresses the question “Ideally, who do you want to survey?”
It involves
defining population units
setting population boundaries

12. Steps in the Sampling Process
1. Define the “population”
The Element individuals
families
seminar groups
sampling Unit… individuals over 20
families with 2 kids
seminar groups at ”new” uni
Extent individuals who have bought “one”
families who eat fast food
seminar groups doing MR
Timing bought over the last seven days

13. The Target population for a toy store can be defined as all households with children living in Calgary.
What’s wrong with this definition?

14. Sampling Frame Obtaining a “list” of population (how to reach sample)
Students eat at Maconalds?
young people at random in the street?
‘phone book
students union listing
Uni. mailing list
Problems with lists
omissions
ineligibles
duplications
Random digit dialing (RDD)

15. Select “sample units” Individuals Household Streets Telephone numbers
Companies

16. Selecting a Sampling Design
Probability sampling
simple random sampling
systematic sampling
stratified sampling
cluster sampling
Non-probability sampling
convenience sampling
judgement sampling
snowball sampling
quota sampling

17. Probability Sampling
An objective procedure in which the probability of selection is nonzero and is known in advance for each population unit.
It is also called random sampling.

18. Simple Random Sampling (SRS)
Population members are selected directly from the sampling frame
Equal probability of selection for every member
Use random number table or random number generator

19. Simple Random Sampling
N = the number of cases in the sampling frame
n = the number of cases in the sample
NCn = the number of combinations (subsets) of n from N
f = n/N = the sampling fraction

20. Objective: To select n units out of N such that each NCn has an equal chance of being selected
Procedure: Use a table of random numbers, a computer random number generator, or a mechanical device to select the sample

21. Systematic Sampling
Order all units in the sampling frame based on some variable and number them from 1 to N
Choose a random starting place from 1 to N and then sample every k units after that

22. systematic random sample
number the units in the population from 1 to N
decide on the n (sample size) that you want or need
k = N/n = the interval size
randomly select an integer between 1 to k
then take every kth unit

23. Stratified Sampling (I)
The chosen sample is forced to contain units from each of the segments, or strata, of the population
Steps:
Population is divided into strata based on an appropriate population characteristic.
Simple random samples are then drawn from each stratum.

24. Stratified Random Sampling

25. Stratified Sampling (II)
Direct Proportional Stratified Sampling
The sample size in each segment is proportional to the segment size in the population
Disproportional Stratified Sampling
The sample size in each segment is NOT proportional to the segment size in the population
Used if
1) some segments are too small
2) some segments are more important than others
3) some segments are more diversified than others

26. Cluster Sampling
Clusters of population units are selected at random and then all or some randomly chosen units in the selected clusters are studied.
Steps:
Population is divided into subgroups, or clusters. Ideally, each cluster adequately represents the population.
A simple random sample of a few clusters is selected.
All or some randomly chosen units in the selected clusters are studied.

27. cluster or area random sampling
divide population into clusters (usually along geographic boundaries)
randomly sample clusters
measure all units within sampled clusters

28. When to use stratified sampling
If primary research objective is to compare groups
Using stratified sampling may reduce sampling errors
When to use cluster sampling
If there are substantial fixed costs associated with each data collection location
When there is a list of clusters but not of individual population members

29. Non-Probability Sampling
Subjective procedure in which the probability of selection for some population units are zero or unknown before drawing the sample.

30. Types of Non-Probability Sampling (I)
Convenience Sampling
A researcher's convenience forms the basis for selecting a sample.
Judgement Sampling
A researcher exerts some effort in selecting a sample that seems to be most appropriate for the study.

31. Types of Non-Probability Sampling (II)
Snowball Sampling
Selection of additional respondents is based on referrals from the initial respondents.
Used to sample from low incidence or rare populations.
Quota Sampling
The population is divided into cells on the basis of relevant control characteristics.
A quota of sample units is established for each cell.
A convenience sample is drawn for each cell until the quota is met.
(similar to stratified sampling)

32. Quota Sampling
Let us assume you wanted to interview tourists coming to a community to study their activities and spending. Based on national research you know that 60% come for vacation/pleasure, 20% are VFR (visiting friends and relatives), 15% come for business and 5% for conventions and meetings. You also know that 80% come from within the province. 10% from other parts of Canada, and 10% are international. A total of 500 tourists are to be intercepted at major tourist spots (attractions, events, hotels, convention centre, etc.), as you would in a convenience sample . The number of interviews could therefore be determined based on the proportion a given characteristic represents in the population. For instance, once 300 pleasure travellers have been interviewed, this category would no longer be pursued, and only those who state that one of the other purposes was their reason for coming would be interviewed until these quotas were filled.

33. Alberta
Canada
International
Totals
Pleasure
.48
.06
.60
Visiting
.16
.02
.20
Business
.12
.015
.15
Convention
.04
.005
.05
.80
.10
100

34. Probability Vs. Non-Probability Sampling
Non-probability sampling is less time consuming and less expensive.
Theoretically speaking, quantitative generalizations about population can only be done under probability sampling.
However, in practice, marketing researchers also apply statistics to study non-probability samples.

35. The sampling process 1. Determine the target population
who are the people you want information on?
age, gender, product use
2. Determine the sampling frame
how will you get the names, phone numbers or addresses?
existing lists, phone book, random digit dialing

36. The sampling process 3. Select a sampling procedure:
A. Probability (random) sample
equal chance of being included in the sample
random number table, even-odd, etc.
B. Stratified
equalizing "important" variables
year in school, geographic area, product use, etc.

37. The sampling process C. Nonprobability sampling convenience sample
people in my classes
"snowball" sample
friends of friends
"quota" sampling
50 women, 50 men
mall intercepts
Market Mall

38. Generalization
You can only generalize to the population from which you sampled
U of L students not college students
geographic, different majors, different jobs, etc.
College students not Canadian population
younger, poorer, etc.
Canadians not people everywhere
less traditional, more affluent, etc.

39. Drawing inferences from samples
Population estimates
% who smoke, buy your product, etc
25% of sample
what % of population?
very dangerous with a non-representative sample or with low response rates

40. Drawing inferences from samples
Relationships
e.g., exposure to ads and liking for the product
relationships (qualitatively different)
less dangerous with a non-representative sample

41. Errors in Survey Sampling Error Non-sampling Error random error
the level of it is controlled by sample size
a larger sample size leads to a smaller sampling error.
Non-sampling Error
systematic Error
the level of it is NOT controlled by sample size.

42. Non-Sampling Errors (I)
The basic types of non-sampling error
Non-response error
Response or data error
A non-response error occurs when units selected as part of the sampling procedure do not respond in whole or in part
If non-respondents are not different from those that did respond, there is no non-response error

43. Non-Sampling Errors (II)
A response or data error is any systematic bias that occurs during data collection, analysis or interpretation
Respondent error (e.g., lying, forgetting, etc.)
Interviewer bias
Recording errors
Poorly designed questionnaires

44. Determine sample size Is Size Everything?
Typical Statistics estimate requires:
Population variance
Acceptable sample error
Level of confidence (coefficient of confidence)

45. Sample Confidence
“Probability” we can take results as “accurate representation” of universe
(i.e. that “sample statistics” are generalisable to the real “population parameters”)
Typically a 95% probability
(i.e. 19 times out of 20 we would expect results in this range)

46. Example: We can be 95% sure that, say, 65% of a target market
will name Martini’s “V2” vodka
in an unprompted recall test
plus or minus 4%

47. We can be 95% sure (level of confidence)
that, say, 65% (predicted result)
of a target market (of a given total population)
will name Martini’s “V2” vodka
in an unprompted recall test
plus or minus 4% (to a known margin of error)

48. 95% confidence
If we do the same test 20 times then it is statistically probable that the results will fall between %, at least 19 times
If we lower the probability then we lower the sample error
e.g.. at a 90% confidence level, result might be believe between 64% - 66%
(a tighter range but we are less sure the sample is representative of the real population)

49. Sample Size Issues Accuracy Resources / Budget Time Reachability
Statistical Analysis
Sub Group Size

50. Cost of research
sample
size
cost

51. Determining sample size
Balance between financial and statistical issues
1. What can I afford
2. Rule of thumb
past experience
historical precedence
gut feeling
some consideration of sample error
3. Make up of sub-groups (cells)
What statistical inferences do you hope make between sub groups (rare to fall between 20 for a sub group)
4. Statistical Methods

52. (Given reliability and validity hold)
Implications for sample size
(Given reliability and validity hold)
Above a certain size little extra information is gathered by increasing the sample size.
Generally, there is no relationship between the size of a population and the size of sample needed to estimate a particular population parameter, with a particular error range and level of confidence.

53. We can choose an error range (e.g. + 5%)
We can set a confidence level (e.g. 95%)
But
Without knowing the spread of results (i.e. the standard deviation for the population) we cannot work out the sample size required So
If you need a statistical assessment, estimate spread of responses (population variance /standard deviation) with:
pilot tests
guess
previous experience

54. Sample Size Determination
3 Statistical Determinants of Sample Size
DEGREE OF CONFIDENCE
Statistical Confidence
95% Confidence or .05 Level of Significance
DEGREE OF PRECISION
Accuracy in Estimating Population Proportion
+/- $5.00 versus +/- $1.00
+/- 10% versus +/- 5%
VARIABILITY IN THE POPULATION
To What Degree do the Sampling Units Differ

55. Size The tough question? Too big - it’s a waste of money
Too small - you cannot make a BIG decision

56. Sample Size Determination
TO BE
MORE CONFIDENT
MORE PRECISE
IF MORE VARIABLE
SAMPLE SIZE MUST INCREASE