Download Sample

COM317 800
Taejin Jung, Ph.D.
Week 8: Sampling Messages
and People
Sampling
Definition
-
The science of systematically
drawing a valid group of objects
from a population reliably
Critical Issues in Sampling

Who/what should be questioned/
observed?

What demographic, psychographic, or
behavioral traits should be used to
identify population membership?

How many population elements must
be selected to ensure
representativeness?

How reliable does the information have
to be for the decision maker? What
are the data quality factors and
acceptable levels of sampling error?

What sampling techniques should be
used?

What are the time and cost
constraints?
A major difference between
formal and informal methodology
-
Ability to generalize to a larger
population
Types of sampling
-
Census
Nonprobability sampling
Probability sampling
Sampling Terminology

Population
- Any complete group of interest in
which members share some
common characteristic
- The complete set of subjects,
variables, or concepts under
consideration
- e.g., registered voters, press
release for a particular product,
households with satellite dishes

Sample
-A subset of the population

Sampling
- Using a subset of the
population to make
conclusions about the entire
population

Census
- Investigating every item or
person in the population
Sampling


If it's not representative,
it's not generalizable to
the larger population
In other words, to be
generalizable, a sample
should "look" like the
population as a whole.

Why Sample?
- Cost and time to census is
great
- Sampling can give accurate
results
- Some tests destroy the
sampled units (or sensitizes
them)
Sampling Process
1. Determine the population
- Who is the target of interest
- Must be operationally defined
2. Select sampling frame
- A listing of the all available sampling units
- Used to draw the sample
- Sampling frame error
: when the frame is not identical to the population
3. Select sampling design
- Probability sample: every element in the population has a
equal chance of selection
- Non-probability sample
Sampling Elements and Definitions

Universe
- The general concept of who or whom will be sampled
(e.g., all college & university students)

Population
- The message types or the people to be sampled, as defined and
described (e.g., Those that are private)

Sampling frame
- A list of all the messages or people to be surveyed
(e.g., most current list of private college students)

Sample
- The actual messages or people chosen for inclusion in the research
(e.g., available list of private college students)

Completed sample
- Messages selected and analyzed and the people who actually responded
to the survey (e.g., sample college students who responded to the survey)
Sampling Elements and Definitions

Coverage error
- Error produced in not having an up-to-date sampling framing from
which to sample

Sampling error
- Error produced when you do not sample from all the members of
the sampling frame

Measurement error
- Error found when people misunderstand or incorrectly respond to
questions (found primarily when sampling people)
Types of Probability Samples

Simple Random Sample
- Each element has an equal chance of selection
- You must identify all population members
- e.g., bucket selection

Stratified Random Sample
-Population is divided into two or more groups (strata)
and a random sample is selected from each group (strata)
- Proportional sample - The size of each group in the sample is
proportional to the size of the group in the population
- Disproportional - Sample size for each group is not
proportional to the population.
Types of Probability Samples

Systematic
- A random starting point is selected
- Every Nth item is selected
(skip interval: population size/sample size)

Cluster
- When do not have a complete sampling frame but know that population
consists of relatively easily identifiable subgroups
- Primary sampling unit is not the individual element in the population but a
large cluster of elements

Multistage area sampling
- Uses a combination of two or more probability sampling techniques
Nonprobability Sampling

The chance that any particular item being
selected is unknown

No methods for determining random
sampling error

Results can not be generalized to the
population.
Types of Nonprobability Samples

Judgment/Purposive sample
-Researcher selects subjects based upon opinion of some characteristic

Quota sample
- Subgroups identified
- Items/subjects selected non-randomly to match subgroups

Snowball sample
- Initial subjects selected randomly
- Those subjects identify next subjects

Convenience sample
- Subjects who are convenient are selected
Distributions


Normal Distribution
 A symmetrical bell shaped
distribution
 Almost all (99%) of its values
are within ± 3 standard
deviations from the mean

Standardized Normal Distribution.
 A special normal distribution
 Mean = 0, Std. Dev. = 1
 Z table
 Used in inferential statistics

Population distribution
: a frequency distribution of the
elements of a population

Sample distribution
: a frequency distribution of a
sample
Sampling distribution
: a theoretical probability
distribution of sample means for
all possible samples of a certain
size drawn from a particular
population
Sample Size and The Normal Curve

Sample =1

Sample = 100

Sample =10

Sample = 500
Central Limit Theorem

As a sample size increases, the distribution of
sample means of size n approaches a normal
distribution.

The central limit theorem works regardless of the
shape of the original population distribution.
Sample Size for Probability Samples
(How large a sample depends on…)

Sampling Error
(Confidence)
- Sampling error is typically
set at 5%, or 95%
confidence interval
- When we set a 95%
confidence interval, no
more than 5 units (people
or messages) will be
missampled)

Measurement Error (Accuracy)
-
The amount of random error found
in any measure
-
How much accuracy at a minimum
we are willing to accept or tolerate
-
The nominal standard is 95%
confidence in measurement
Factors to Specify Sample Size




Population size (Q)
Expected Outcome (p)
Measurement Error (E)
Sampling Confidence (S)
N = (Q)(p)(1-p)/
(Q-1)(E/C2) + (p)(1-p)
Confidence
Error
95%
99%
1%
2%
3%
4%
5%
6%
7%
9,604
2,401
1,067
600
348
267
196
16,587
4,147
1,843
1,037
663
461
339
Determining Factors for Sample Size

Budgetary consideration
- The larger the sample, the more it will cost

Time constraints
- In PR, you do not have the luxury of time

Resource constraints
- Unlike marketing and advertising, you may have smaller necessary staffs

Prior research
- May provide clues as to how large the sample size needed

Precision or accuracy required for the research