CA3011
Communication
Arts Research
A. Parichart W.
A. Chulamani C.
Sampling
Objectives
 To
explain and differentiate the
population and sample
 To describe the types of sampling
procedures; nonprobability sampling, and
probability sampling
Population and Sample
Population
 One
goal of scientific research is to
describe the nature of a population- a
group or class of subjects, variables,
concepts, or phenomena.
 In many situations, however, an entire
population cannot be examined due to
time and resource constraints.
 The usual procedure in these instances is
to take a sample from the population.
Sample
A
sample is a subset of the population
that is representative of the entire
population.
 A sample that is not representative of the
population , regardless of its size, is
inadequate for testing purposes because
the results cannot be generalized to the
population from which the sample was
drawn.
A Venn Diagram Used in the
Process of Sample Selection
Population
Sample
Types of Sampling
Procedures
Probability VS Nonprobability
Sampling



Probability sampling uses mathematical
guidelines whereby each unit’s chance for
selection is known.
Nonprobability sampling does not follow the
guidelines of mathematical probability.
However, the most significant characteristics
distinguishing the two types of samples is that
probability sampling allows researchers to
calculate the amount of sampling error
present in a research study: nonprobability
sampling does not.
Types of Nonprobability
Sampling
1. Convenience Sampling



A convenience (available) sampling is a
collection or readily accessible subjects,
elements, or events for study, such as a group
of students enrolled in a subject.
The nonprobability sampling can be
problematic because they contain unknown
quantities of error. Researchers need to
consider the pros an cons of available
samples.
E.g. students in the class, shoppers in the mall
The available samples do not
represent the population .
Example
 Mall
intercept studies are criticized
because only the people who are at the
mall the time of the study have a chance
to participate. No one outside the mall
has such an opportunity.
Research using an unqualified
volunteer sample is bad science
because there is no way to know who
participated in the research study. The
results from any study using an
unqualified volunteer sample should be
consider highly questionable.
2. Purposive Sample
Purposive sample, which includes respondents,
subjects, or elements selected for specific
characteristics or qualities and eliminates those
who fail to meet these criteria.
• Purposive samples are used frequently in mass
media studies when researchers select
respondents who use a specific medium and
asked specific questions about that medium.
Ex. teenagers between the age of 18 to 23
Ex. The sample is those who consumer online media

A purposive sample is not
representative of the general
population.
3. Quota Sampling

Subjects are selected to meet a
predetermined or known percentage.
Ex. A researcher interested in finding out how
DVD owners differ from non DVD owners in their
use of television may know that 40% of a
particular population owns a DVD. The sample
the researcher selects, therefore, would be
composed of 40% DVD owners and 60% nonDVD owners (to reflect the population
characteristics)
Example: Search engine
market share
4. Snowball Sampling
A
researcher randomly contacts a few
qualified respondents and then asks these
people for the names of friends, relatives,
or acquaintances they know who may
also qualify for the research study.
 It is recommended for the academic
research for a reason that the sample
may be completely biased. The sample
may consist of respondents who are from
a particular club or group.
Types of Probability
Sampling
1. Simple Random Sampling
 Where
each subject, element, event, or
unit in the population has an equal
chance of being selected.
 Researchers often use a table of random
numbers to generate a simple random
sample.
A representative sample may
not result in all casess
2. Systematic Random
Sampling
 Every
nth subject, unit, or element is
selected from a population.
Ex. To obtain a sample of 20 from a
population of 100, or a sampling rate is 1/5,
a researcher randomly selects a starting
point and a sampling interval. If the number
11 is chosen as the starting point, the sample
will include the 20 subjects or items
numbered 11, 16, 21, 26, and so on.
Systematic Random Sampling
 The
accuracy of the systematic random
sampling depends on the adequacy of
the sampling frame, or the complete list
of members in the population.
- In some projects, researchers want
to guarantee that a specific
subsample of the population is
adequately represented, and no
such guarantee is possible using a
simple random sampling.
-The list can be biased.
3. Stratified Sample


is the approach used to get adequate
representation of sample (strata or segment)
may include almost any variable; age,
gender, religion, income level, or even
individuals who listen to specific radio stations
or read certain magazines.
Stratified sampling ensures that a sample is
drawn from a homogeneous subset of the
population-that is , from a population that has
similar characteristics.
This requires the researcher to
have a complete list of the
population.
Proportionate VS Disproportionate
Stratified Sampling
 Proportionate
stratified sampling includes
strata which sizes based on their
proportions in the population.
Ex. If 30% of the population is adults ages
18-24, then 30% of the total sample will be
subjects in this age group.
• The procedure is designed to give each
person in the population and equal
chance of being selected.
Proportionate VS Disproportionate
Stratified Sampling
 Disproportionate
stratified sampling is
used to oversample or over represent a
particular stratum.
 The approach is used because that
stratum is considered important for
marketing, advertising, or other similar
reason.
Example
 In
a telephone study of 400 respondents,
the station management may wish to
have the sample represented as follows:
70% in the 25-34 group, 20% in the 35-49
group, and 10% in the 50-54 group.
This distribution would allow
researchers to break the 25-34
group into smaller subgroups such
as males, females, fans of specific
stations, and still have reasonable
sample sizes.
4. Cluster Sampling
 With
cluster sampling, the state can be
divided into districts, countries, or zip code
areas, and groups of people can be
selected from each area.
 Cluster sampling may create the error.
Ex. A zip code area may contain mostly
residents of a low socioeconomic status who
are unrepresentative of the rest of the state.
This is suitable when there is
no way to obtain the list of
the population.
Multistage Sampling
In many national studies, researchers use a form of
cluster sampling called multistage sampling, in
which individual households or people are
selected.
Ex. First, a cluster of countries in the U.S. is selected.
Researchers then narrow this cluster by randomly
selecting a country, district. Next, individual blocks
are selected within each area. Finally, a convention
such as “ the third household from the northeast
corner’ is established.
• Applying the selection formula in the stages just
described can thus identify the individual
households in the sample.

Issues to Consider when
Using Probability and Nonprobability Sampling
Four Issues when deciding to
use Probability or Nonprobability Sampling


Purpose of the study. Some studies are not
designed to generalize the results to the
population but rather to investigate the variable
relationships or collect exploratory data to design
questionnaires or measurement instruments.
Nonprobability sampling is appropriate in these
situations.
Cost versus value. A sample should produce the
greatest value for the least investment. If the cost
of probability sampling is too high in relation to the
type and quality of info. Collected, then
nonprobability sampling is usually satisfactory.
Four Issues when deciding to use
Probability or Nonprobability Sampling


Time constraints. In many cases, researchers
collecting preliminary info. operate under
time constraints imposed by sponsoring
agencies, management directives, or
publication guidelines. Since probability
sampling is often time consuming, a non
probability sample may meet the need
temporarily.
Amount of acceptable error. In preliminary
studies or pilot studies, where error control is
not a prime concern, a non-probability
sample is usually adequate.
Example 1
 The
comparison of the perception
between the Communication Arts
students and Business students towards
Dove advertising campaign in 2013.
Example 2
 The
study of the impact of the celebrity
endorser on the purchase intention
Example 3
 The
study of the value derived from the
media of the people in Thailand
Reference
 Wimmer,
R. & Dominick, J. (2011). Mass
Media Research: An Introduction (9th ed.).
Belmont, CA: Thompson Wadsworth.
Thank you for your
attention
In-class Group Advising
 Develop
the research topic which is
relevant to the communication area
 Define the population and sample
 Define and describe the types of the
nonprobability and probability sampling
method used