Sampling

When a sociologist is doing a piece of research it is often impractical for them to survey everyone in the group they are looking at. A portion of elements taken from the larger portion or population of them is called a SAMPLE. The process of   drawing these elements from the larger population is called sampling. The sampling plan specifies how elements will be drawn from the larger or parent population and how many elements will   be drawn

Type of samples

There are two basic types of sampling techniques -- probability sampling and non-probability sampling. In a probability sample each unit in the general population has an equal or known chance of being included in the sample. By contrast, it is not possible to determine the likelihood that an element of the population might be included in the sample if one employs a nonprobability sampling technique. Therefore, most social scientists prefer probability samples, and point out the short-comings of nonprobability approaches to sampling. However, many research situations make it impossible to employ probability sampling techniques, making nonprobability sampling the method of choice -- even if not the method of preference.

The major advantage of probability sampling is that it insures greater generalizability of findings. Nonprobability sampling has the advantages of convenience, decreased costs, and may be the only method possible.

Types of Probability Samples

1.         Simple Random Sample: All elements in the population are listed and assigned a unique number. Then a table of random numbers is used to generate truly random digits, and selection is made on this basis. Sometimes the "Monte Carlo" method is substituted for the use of the random number table. With this method all of the elements are "put in a hat" and drawn at random until the desired number of elements have been selected.

2.         Stratified Random Sample. In this sampling technique the population is divided into two or more subgroups or strata. These strata represent those characteristics on which the researcher wishes to insure adequate representation. For example, If one were concerned that male and female perspectives were adequately represented, the population listing (or sampling frame) would be divided into two subgroups, and a simple random sample would be taken from each subgroup using a table of random numbers or the "Monte Carlo" method.

3.         Cluster Sampling. As mentioned earlier, there can be enormous problems and costs associated with probability sampling when it is mandatory that particular individuals be interviewed. Both the simple random and stratified random samples would be quite costly to obtain if the population were very large or the distribution of the population was widely scattered.

Probability samples have the following drawbacks:

1.         Probability samples are much more expensive.

2.         Non-response is a special problem.

3.         If one cannot list the elements in the population, they are impossible.

4.         Probability samples are much more time consuming.

Nonprobability Samples

While usually mush less costly to construct and implement, the nonprobability sample is subject to many criticisms related to its validity.

1.         Accidental Samples. As the name implies, the accidental sample consists of units which are obtained because cases are readily available. In constructing the accidental sample (also referred to as an availability sample), the researcher determines the desired size of the sample and then simply collects data on that number of individuals. 

2.         Quota Samples. The quota sample is an attempt to approximate the stratified random sampling technique but in a non-random manner. The researcher first identifies those categories which  are important to insure the representativeness of the population, then establishes a sample size for each category, and finally selects individuals on an availability basis. For example, if one wished to interview equal numbers of women and men concerning their opinions toward municipal laws governing wages for jobs with comparable worth, employing a quota sample one would interview willing and available individuals until the desired number of individuals in each subgroup had been interviewed.

3.         Purposive Samples. Purposive samples are sometimes called judgment samples, and are employed by the researcher in order to approximate the cluster sample using a nonprobability sample. In this sampling method the researcher selects a "typical group" of individuals who might represent the larger population and then collects data on this group. For example, if a researcher wished to survey the attitudes of freshman college students at particular university, he or she might survey the students in one or more freshman English classes -- the assumption is that since all students must take freshman English, the students in any class are representative of the entire freshmen class.

4.         Snowball Sampling. Our final form of non-probability sampling is snowball sampling. In this approach the researcher selects available respondents to be included in the sample. After the subject has been surveyed, the researcher asks for a referral of other individuals, who would represent the population of concern. For example, if you were studying wealthy persons in Chicago, chances are that you do not have a total list of all millionaire Chicago residents, but you might know one or more wealthy persons. You might begin with the wealthy persons you do know, interview them, and ask if they could each refer you to more Chicago millionaires. Since "birds of a feather flock together," they could probably supply you with such names. Through this snowball referral method, you could eventually obtain a sample of the desired size. It should be noted that it is very unlikely that this would be a unbiased and representative sample!

Factor affecting the size of the sample

An optimum sample in   a survey is the one which fulfills the requirement of efficiency representative ness, reliability and flexibility, The sample   should be small enough to avoid unnecessary expenses and large   enough to avoid intolerable sampling error”

1.         Homogeneity or Heterogeneity of the universe

2.         Number of classes proposed- If a large number of classes are   to be formed sample must be large enough so that every class may   be of proper size suitable for the statistical treatment.

3.         Nature of the study - if an intensive study for large time   than the large size is unfit.

4.         Practical considerations- Availability of finance, time, Number of trained field workers etc.

5.         Standard of accuracy -It is generally considered that larger   the size of the sample greater is the standard of accuracy or   representative ness although it is not true in all the cases as   more largeness of the size is no guarantee for representative    ness.

6.         Size of Questionnaire or Schedule - Larger the size smaller   should be the sample to reduce complexity.

7.         Nature of the cases to be contacted - If the cases are   geographically scattered the samples should be small.

8.         Type of sampling used - Absolute random sampling requires much   larger samples and for stratified sampling much smaller will do.

Procedures of selecting a sample

A valid sample must be representative of the universe or population.

The sample duly selected must be adequate in size. Although the size of sample is no necessary insurance of its representative ness.

Small samples properly selected may also be much more reliable than large samples poorly selected.

The most important consideration in selecting a sample is to see that it is closely representative of the universe.

The actual selection of sample should be so arranged that every item in the universe under consideration must have the same chance for inclusion in the sample.

A sample that is not representative is known as a biased sample.

There are four basic procedures in selecting the items for statistical samples They are: (1) Simple random sampling, (2) stratified random sampling (3) sampling by regular intervals (4) area sampling. All these four types of sampling procedures overlap to a greater extent. Actually speaking sampling design may include two or more of these procedures.

No sampling technique is completely automatic, all involve subjects matter decisions. Hence first hand practical experience with the concrete subject matter contribute quite as much to fulfillment of a sampling project as dexterity in the mechanical routine of applied statistics.