A group or class of
subjects, variables, concepts, or phenomena is called sample.
In some cases, an entire
class or group is investigated; The process of examining every member in a
population is called a census.
A sample is a subset of the
population that is representative of the entire population.
SAMPLE
SIZE
Determining an adequate
sample size is one of the most controversial aspects of sampling. The size of the sample required for a study depends
on at least one or more of the following seven factors:
(1)
project type,
(2)
project purpose,
(3) project complexity,
(4)
amount of error tolerated,
(5)
time constraints,
(6)
financial constraints, and
(7)
previous research in the area.
1. A primary consideration in determining sample size is the research
method used. Focus groups (see Chapter 5) use samples of 6-12 people,
but the results are not intended to be generalized to the population from which
the respondents are selected. Samples with 10-50 subjects are commonly used for
pretesting measurement instruments and pilot studies, and for conducting
studies that will be used for only heuristic value.
2.
Researchers often use samples of SO, 75, or 100
subjects per group, or cell More than likely, the client would accept SO
respondents in each of the eight cells, producing a sample of 400 (8 X SO).
3.
Cost and time considerations always control sample
size. Although researchers may wish to use a sample of 1,000 for a survey, the
economics of such a sample are usually prohibitive.
4.
Most research is conducted using a sample size that
conforms to the project's budget. Researchers may be wise to consider using
smaller samples for most projects.
5. Multivariate studies
require larger samples than do univariate studies because they involve
analyzing multiple response data.
6.
For panel studies, central
location testing, focus groups, and other pre recruit projects, researchers
should always select a larger sample than is actually required. If a survey is planned and
similar research indicates that a representative sample of 400 has been used
regularly with reliable results, then a sample larger than 400 may be
unnecessary.
7. Generally
speaking, the larger the sample, the better. However, a large unrepresentative
sample (The Law of Large Numbers) is as meaningless as a small unrepresentative
sample.
TYPES OF
SAMPLING PROCEDURES
There are a variety of
sampling methods available for researchers. We first need to discuss the two
broad categories of sampling: probability and nonprobability.
Probability
and 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.
There are four issues to consider when deciding whether to use
probability or non-probability sampling:
·
Purpose of the study.
·
Cost versus value.
·
Time constraints.
· Amount of acceptable error.
Types
of Non probability Sampling
Mass media researchers
frequently use non probability sampling, particularly in the form of available
samples.
An available sample (also known as a convenience
sample) is a collection of readily accessible subjects, elements, or
events for study.
In
most situations, available samples should be avoided because of the bias introduced
by the respondents' proximity to the research situation, but available samples
can be useful in pretesting questionnaires or other preliminary (pilot study)
The purposive sample,
Purposive samples are used frequently in mass media studies when researchers
select respondents who use a specific medium and are asked specific questions
about that medium. A purposive sample is chosen with the knowledge that it is
not representative of the general population.
For
example, a researcher interested in finding out how other internet providing
agents differ from geo accessibility.
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.
Types of Probability Samples
The
most basic type of probability sampling is the
simple random sample, where each subject, element, event, or unit in
the population has an equal chance of being selected.
Sampling with replacement is often used in
more complicated research studies such as nationwide surveys. For
example, a researcher who wants to analyze 10 prime-time television programs
out of a population of 100 programs to determine how the medium portrays
elderly people can take a random sample from the 100 programs by numbering each
show from 00 to 99 and then selecting 10 numbers from a table of random
numbers, such as the brief listing in Table 4.1
SIMPLE RANDOM SAMPLING
Advantages
1. Detailed
knowledge of the population is not required.
2. External validity may be
statistically inferred.
3. A representative group is
easily obtainable.
4. The possibility of
classification error is eliminated.
Disadvantages
1.
A list of the population must be compiled.
2.
A representative sample may not result in all cases.
3. The
procedure can be more expensive than other methods.
SYSTEMATIC SAMPLING
Advantages
1. Selection
is easy.
2. Selection
can be more accurate than in a simple random sample.
3. The procedure is generally
inexpensive.
Disadvantages
1. A
complete list of the population must be obtained.
2. Periodicity (arrangement
or order of list) may bias the process.
ADDRESS-BASED SAMPLING (ABS): A third
type of random selection method called address-based sampling (ABS) to
recruit sample households. The method uses randomly selected addresses. There
are several methods to develop random numbers or households, but two rules
always apply:
(1)
each unit or subject in the population must have an equal
chance of being selected,
(2)
and the
selection procedure must be free from subjective intervention by the
researcher.
A STRATIFIED SAMPLE is the approach used
to get adequate representation of a subsample. The characteristics of the
sub-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. The strata may be defined by an almost
unlimited number of characteristics; however, each additional variable or
characteristic makes the subsample more difficult to find and costs to find the
sample increase substantially.
Stratified sampling ensures that a sample is drawn from a homogeneous
subset of the population—that is, from a population that has similar
characteristics. Homogeneity helps researchers to reduce sampling error.
Stratified Sampling
Advantages
1.
Representativeness of relevant variables is ensured.
2. Comparisons
can be made to other populations.
3.
Selection is made from a homogeneous group.
4.
Sampling error is reduced.
Disadvantages
1. Knowledge
of the population prior to selection is required.
2. The procedure can be
costly and time-consuming.
3. It can be difficult to
find a sample if incidence is low.
4. Variables that define
strata may not be relevant.
CLUSTER SAMPLING : Select the sample in groups or categories; this
procedure is known as cluster sampling. For example, analyzing magazine
readership habits of people in Tamilnadu
would be time-consuming and complicated if individual subjects were randomly
selected. With cluster sampling, the state can be divided into districts, and
groups of people can be selected from each area.
Cluster Sampling
Advantages
1. Only
part of the population need be enumerated.
2. Costs are reduced if
clusters are well defined.
3. Estimates of cluster
parameters are made and compared to the population.
Disadvantages
1. Sampling
errors are likely.
2. Clusters
may not be representative of the population.
3. Each subject or unit must
be assigned to a specific cluster.
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