SAMPLING PROCEDURES
MKTG 3342
Fall 2008
Professor Edward Fox
SAMPLING PROCEDURES
Outline
1. INTRODUCTION: Sampling vs. Census
2. PROCEDURE FOR DRAWING SAMPLE
3. TYPES OF SAMPLING PLANS
4. NONPROBABILITY SAMPLES
5. PROBABILITY SAMPLES
1. SAMPLING VS. CENSUS
ADVANTAGES OF SAMPLING
 Time
 Cost
DISADVANTAGES
 Because only a sample has been
drawn, there is associated uncertainty
(error)
1. SAMPLING VS. CENSUS
Target
Population
Sample
2. PROCEDURE FOR DRAWING SAMPLE
(slightly different from procedure in your book)
A. DEFINE THE TARGET POPULATION
 the
specification of people or cases on whom
the research is to be conducted.
B. IDENTIFY THE SAMPLING FRAME
 Listing
of population elements from which
sample is drawn.
C. SELECT THE SAMPLING PLAN
D. DETERMINE SAMPLE SIZE
E. SELECT THE SAMPLING UNITS
3. TYPES OF SAMPLING PROCEDURES
SAMPLE DESIGN
NONPROBABILITY SAMPLES
- CONVENIENCE
- JUDGMENTAL
- QUOTA
- SNOWBALL
PROBABILITY SAMPLES
- SIMPLE RANDOM
- STRATIFIED
PROPORTIONATE
DISPROPORTIONATE
- CLUSTER
- SYSTEMATIC
PROBABILITY VS. NONPROBABILITY

PROBABILITY SAMPLING
 Every member of the population has a known,
non-zero probability of being selected

NON-PROBABILITY SAMPLING
 The probability of any particular member being
chosen for the sample is unknown
NONPROBABILITY SAMPLING METHODS
CONVENIENCE SAMPLES
 Nonprobability samples used primarily because
they are easy to collect
JUDGMENT SAMPLES
 Nonprobability samples in which the selection
criteria are based on personal judgment that the
element is representative of the population
under study
NONPROBABILITY SAMPLING METHODS
QUOTA SAMPLES
 Nonprobability samples in which population
subgroups are classified on the basis of
researcher judgment
SNOWBALL SAMPLES
 Nonprobability samples in which selection of
additional respondents is based on referrals
from the initial respondents
PROBABILITY SAMPLING METHODS
SIMPLE RANDOM SAMPLING
 A probability sample in which every element of
the population has a known and equal
probability of being selected into the sample
Probability of Selection =
Sample Size
Population Size
PROBABILITY SAMPLING METHODS
 STRATIFIED RANDOM SAMPLING
INVOLVES THE FOLLOWING TWO-STEP
PROCEDURE:
I. The parent population is divided into mutually
exclusive and collectively exhaustive subsets
(strata)
II. A simple random sample is chosen from each
subset
REASONS FOR STRATIFIED SAMPLING
-- Investigate characteristics of interest by
subgroup; stratification allows for
adequate representation of different subgroups
-- Increase precision (reduce sampling error)
EXAMPLE

Suppose I want to investigate if low-income users default
more on credit card than high-income users. I want to
ensure adequate representation of people with both high
and low incomes, so I divide the population on the basis of
income and take a random sample from the high-income
group and the low-income group.
PROPORTIONATE VS. DISPROPORTIONATE
STRATIFIED SAMPLING


PROPORTIONATE STRATIFIED SAMPLING: Take
sample size in (same) proportion to size of the population
in each subgroup or stratum; e.g., suppose there are 3,000
high-income users and 10,000 low-income users; then take
maybe 30 (1%) high-income and l00 (1%) low-income
users
DISPROPORTIONATE STRATIFIED SAMPLING:
Sample size not necessarily in proportion to population
subgroup size; e.g. take 60 (2%) high-income consumers
and 100 (1%) low-income users because I think there is
substantial variation among high-income consumers
 CLUSTER SAMPLING

TWO-STEP PROCEDURE:
-- Population is divided into mutually
exclusive and collectively exhaustive subsets
-- A random sample of the subsets is selected
-- In one-stage cluster sampling, all elements in
the randomly selected subsets are included
-- In two-stage cluster sampling, a sample is
selected probabilistically from each randomly
selected subset
MOTIVATION FOR CLUSTER SAMPLING
GENERALLY LOWER COST (but less accurate)
For example,
In the income / credit default case, suppose you divide people
based on where they live (say, by zip code), then randomly select
zip codes (say 75248 and 75212) and investigate either everyone
in both zip codes or a random sample of people from both zip
codes
DIFFERENCE BETWEEN STRATIFICATION
AND CLUSTERING
 The variable used for stratification must be related to research
focus (income, in our example)
 The variable used for clustering must not be related to research
focus (zip code, in our example)
PROBABILITY SAMPLING METHODS
SYSTEMATIC SAMPLING
Probability sampling in which the entire
population is numbered. The first number is
drawn randomly. Subsequent elements are
drawn using a skip interval.
Skip Interval =
Population Size
Sample Size
PROBABILITY SAMPLING METHODS
 Example
of systematic sampling
 Suppose
I want to pick 100 phone numbers to
call from a telephone directory with 1000
pages. Use
Population Size (1000)
Skip Interval =
Sample Size (100)
= 10
 First, draw a random number between 1 and 10
(say you get 7); then pick pages 7, 17, 27, …997
 From each page you can pick a phone number
(say on top right corner)
SUMMARY OF KEY POINTS
(1 of 2)
 The
population is the total group of people
in whose opinions one is interested
 A census involves collecting desired
information from all the members of the
population
 A sample is simply a subset of a population
SUMMARY OF KEY POINTS
(2 of 2)
Probability sampling methods are selected in such
a way that every element of the population has a
known, nonzero probability of selection
 Nonprobability sampling methods include all
methods that select specific elements from the
population in a nonrandom manner
 Stratified probability sampling is generally the
best method for selecting a sample, if time and
budget permit
