Sunday, 18 September 2016


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.

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.

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
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.
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.

1.     Selection is easy.
2.     Selection can be more accurate than in a simple random sample.
3.     The procedure is generally inexpensive.

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
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.
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
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.
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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