This document discusses sampling fundamentals and different sampling methods. It begins by defining key terms like population, sample, census, and parameter/statistic. It then explains that the goal of sampling is to select a sample that is as representative of the population as possible in order to make accurate inferences. The document discusses probability and non-probability sampling methods. It provides details on specific probability methods like simple random sampling, stratified sampling, cluster sampling, and systematic sampling. It also briefly outlines non-probability sampling techniques and their uses and benefits.

1. Sampling Fundamentals 1Sampling Fundamentals 1

2. Sampling Fundamentals
• Population
• Sample
• Census
• Parameter
• Statistic

3. The One and Only Goal in Sampling!!
Select a sample
that is as
representative
as possible.
So that an accurate inference about the population
can be made – goal of marketing research

4. Sampling Fundamentals
• When Is Census Appropriate?
• When Is Sample Appropriate?

5. Error in Sampling
• Total Error
• Sampling Error
• Non-sampling Error (dealt with in chapter 4)

6. Sampling Process: Identify Population
• Question: For a toy store in RH
• Question: For a small bookstore in RH
specializing in romance novels

7. Sampling Process: Determine sampling
frame
• List and contact information of population
members used to obtain the sample from
• Example – to address a population of all
advertising agencies in the US, the sampling
frame would be the Standard Directory of
Advertising Agencies
• Availability of lists is limited, lists may be obsolete
and incomplete

8. Problems with sampling frames
• Subset problem
– The sampling frame is smaller than the population
• Superset problem
– Sampling frame is larger than the population
• Intersection problem
– A combination of the subset and superset problem

9. Problems with sampling frames

10. Sampling Process: Sampling Procedure
Probability Sampling
Nonprobability Sampling

11. Sampling Procedure
Sampling Procedures
Non-Probability
Sampling
Probability Sampling
-Simple Random Sampling
-Systematic Sampling
-Stratified Sampling
-Cluster Sampling
-Convenience Sampling
-Judgmental Sampling
-Snowball Sampling
-Quota Sampling
Here’s the
difference!
Probability Sampling: Each subject has the same non-zero
probability of getting into the sample!

12. Probability Sampling Techniques
Simple Random Sampling
• Each population member has equal, non-zero
probability of being selected
• Equivalent to choosing with replacement

13. Probability Sampling Techniques
• Accuracy – cost trade off
• Sampling Efficiency = Accuracy/Cost
– Sampling efficiency can be increased by
either reducing the cost, increasing the
accuracy or doing both
– This has led to modifying simple random
sampling procedures

14. Probability Sampling Techniques
Stratified Sampling
• The chosen sample is forced to contain units from each of
the segments or strata of the population
• Sometimes groups (strata) are naturally present in the
population
• Between-group differences on the variable of interest are
high and within-group differences are low
• Then it makes better sense to do simple random sampling
within each group and vary within-group sample size
according to
– Variation on variable of interest
– Cost of generating the sample
– Size of group in population
• Increases accuracy at a faster rate than cost

15. Stratified Sampling – what strata are naturally
present

16. Consumer type Group size 10 Percent
directly
proportional
stratified sample
size
Brand-loyal 400 40
Variety-seeking 200 20
Total 600 60
Directly Proportionate Stratified Sampling

17. • 600 consumers in the population:
• 200 are heavy drinkers
• 400 are light drinkers.
• If heavy drinkers opinions are valued more and a sample
size of 60 is desired, a 10 percent inversely proportional
stratified sampling is employed. Selection probabilities are computed as
follows:
Denominator
Heavy Drinkers
proportion and
sample size
Light drinkers
proportion and
sample size
600/200 + 600/400 = 3 + 1.5 = 4.5
3/ 4.5 = 0.667; 0.667 * 60 = 40
1.5 / 4.5 = 0.333; 0.333 * 60 = 20
Inversely Proportional Stratified Sampling

18. Probability Sampling Techniques
Cluster Sampling
• Involves dividing population into clusters
• Random sample of clusters is selected and all
members of a cluster are interviewed
• Advantages
– Decreases cost at a faster rate than accuracy
– Effective when sub-groups representative of the
population can be identified

19. Cluster Sampling
• Math knowledge of all middle school children in
the US
• Attitudes to cell phones amongst all college
students in the US
• Knowledge of credit amongst all freshman
college students in the US

20. A Comparison of Stratified and Cluster Sampling
Stratified sampling
Homogeneity within group
Heterogeneity between groups
All groups are included
Random sampling in each group
Sampling efficiency improved by
increasing accuracy at a faster
rate than cost
Cluster sampling
Homogeneity between groups
Heterogeneity within groups
Random selection of groups
Census within the group
Sampling efficiency improved by
decreasing cost at a faster rate
than accuracy.

21. Probability Sampling Techniques
• Systematic Sampling
– Systematically spreads the sample through the entire list of
population members
– E.g. every tenth person in a phone book
– Bias can be introduced when the members in the list are
ordered according to some logic. E.g. listing women members
first in a list at a dance club.
– If the list is randomly ordered then systematic sampling
results closely approximate simple random sampling
– If the list is cyclically ordered then systematic sampling
efficiency is lower than that of simple random sampling

22. Non-Probability Sampling
• Benefits
– Driven by convenience
– Costs may be less
• Common Uses
– Exploratory research
– Pre-testing questionnaires
– Surveying homogeneous populations
– Operational ease required

23. Non-Probability Sampling Techniques
• Judgmental
• Snowball
• Convenience
• Quota