This document discusses various sampling methods used in marketing research. It defines key terms like population, sample, probability sampling, and non-probability sampling. Probability sampling methods ensure each unit has a known chance of selection, like simple random sampling, systematic random sampling, and stratified random sampling which divides the population into groups. Non-probability methods do not assign selection probabilities, such as convenience sampling. The document also covers determining appropriate sample sizes and developing a sampling plan.

1. Sampling: Theory and
Methods
Chapter 6

2. Sample
- is a small group of members selected from a population to represent the
population.
- Subset of Population.
Population
- group from which a sample is drawn
- exact population will depend on the scope of the study.

3. Value of Sampling in Marketing
Research
 Sampling
• Selection of a small number of elements from a large
defined target group of elements and expecting that the
information gathered from the small group will allow
judgments to be made about the larger group.
• Is a method that allows researchers to infer information
about a population based on the results from Sample.
 Census
• A research study that includes data about every member of
the defined population.

4. The Basics of Sampling Theory
 Population – The identifiable use of elements of
interest to the researcher and pertinent to the information
problem.
 Defined Target Population – The complete set of
elements identified for investigation.
 Sampling Units – The target population elements
available for selection during the sampling process.

5. Probability and Nonprobability
Sampling
 Probability Sampling – Each sampling unit in the
defined target population has known probability of being
selected for the sample.

6.  Nonprobability Sampling – Sampling designs in
which the probability of selection of each sampling unit is
not known. The selection of sampling units is based on the
judgment of the researcher and may or may not be
representative of the target population

7. Probability Sampling Designs
 Simple Random Sampling – A sampling procedure in which
every sampling unit has a known and equal chance of being
selected.

8. Probability Sampling Designs
 Systematic Random Sampling – Similar to simple random
sampling but the defined target population is ordered in some
way, usually in the form of a customer list, taxpayer roll, or
membership roster, and selected systematically.

9. Probability Sampling Designs
 Stratified Random Sampling – Separation of the target
population into different groups, called strata, and the selection of
samples from each stratum.

10. 3 Basic Steps Stratified Random Sampling:
1. Dividing the target population into homogeneous
subgroups or strata.
2. Drawing random samples from each stratum.
3. Combining the samples from each stratum into a single
sample of the target population.

11. Stratified Random Sampling
Two Common Methods are used to derive samples from
Strata:
 Proportionately Stratified Sampling – A stratified
sampling method in which each stratum is dependent on its
size relative to the population.
 Disproportionately Stratified Sampling – A stratified
sampling method in which the size if each stratum is
independent of its relative size in the population.

12. Probability Sampling Designs
 Cluster Sampling - A probability sampling method in which
the sampling units are divided into mutually exclusive sub-
populations called cluster.
*Area Sampling – A form of cluster sampling in which the clusters
are formed by geographic designations.

13. Non-probability Sampling Designs
 Convenience Sampling – A non probability sampling method
in which samples are drawn at the convenience of the researcher.

14. Non-probability Sampling Designs
 Judgment Sampling – A non probability sampling method in
which participants are selected according to an experienced
individual’s belief that they will meet the requirements of the
study.

15. Non-probability Sampling Designs
 Quota Sampling- A non-probability sampling method in which
participants are selected according to pre specified quotas
regarding demographics, attitudes, behaviors, or some other
criteria.

16. Non-probability Sampling Designs
 Snowball Sampling – a non-probability sampling method,
also called referral sampling in which a set of respondents is
chosen, and they help the researcher identify additional people to
be included in the study.

17. Factors to Consider in Selecting the Sampling Design
Selection Factors Questions
Research Objectives Do the research objectives call for the use of
qualitative or quantitative research designs?
Degree of Accuracy Does the research call for making predictions or
inferences about the defined target population or
only preliminary insights?
Resources Are there tight budget constraints with respect to
both dollars and human resources that can be
allocated to the research subject?
Timeframe How quickly does the research project have to be
completed?
Knowledge of the target population Are there complete lists of the defined target
population elements? How easy or difficult is it to
generate the required sampling frame of
prospective respondents?
Scope of the Research Is the research going be international, national,
regional, or local?
Statistical Analysis needs To what extent are accurate statistical projections
and/or testing of hypothesized differences in the
data required?

18. Determining Sample Sizes
Probability Sample Sizes
Three factors play an important role in determining sample
sizes with probability designs:
1. The population variance, which is measure of the dispersion of
the population, and it’s square root, referred to as the population
standard deviation. The greater the variability in the data being
estimated the larger the sample size needed.
2. The level of confidence desired in the estimate.
3. The degree of precision desired in estimating the population
characteristic.
Precision: The acceptable amount of error in the sample estimate.

19. Steps in Developing Sampling Plan
Sampling Plan – The blueprint or framework needed to ensure
that the data collected are representative of the defined target
population.
Step 1: Define the target population
Step 2: Select the Data Collection Method
Step 3: Identify the Sampling Frame(s) Needed
Step 4: Select the Appropriate Sampling Method
Step 5: Determine the Necessary Sample Sizes and Overall Contact
Rates
Step 6: Create an Operating Plan for Selecting Sampling Units
Step 7: Execute the Operational Plan

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