Sampling Techniques with Brian Maregedze

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Using examples, explain each of the following sampling techniques

INTRODUCTION
A research is a systematic controlled empirical and critical investigation that is meant to find answers to different questions. To achieve the answers in a research there are sampling techniques that are employed in the bid to achieve the correct answers to the questions that one has resulted in the carrying out of a research. It is the essence of this essay to define the following sampling techniques simple random sampling, systematic sampling, cluster sampling, convenience sampling and snowball sampling.
(a)Simple random sampling
A sample is a part drawn from a larger whole. a sample is taken in order to learn something about the whole from which it is drawn. A simple random sample is a sample selected in such a way that every possible sample of the same size is equally likely to be chosen. Samples are chosen randomly meaning that there is no particular strategy to be employed when choosing the elements under research hence anything or anyone can be chosen as argued by Sudman (1966). In an opinion poll, for example, a number of persons or people are interviewed and their opinions on an issue or issues are solicited in order to discover the attitude of the community as a whole, of which the polled persons are usually a small part. Simple random sampling is applicable usually when one is carrying out a research on a population that is small, homogeneous and readily available. According to Moser (1953) all subsets of the frame are given an equal probability that is all elements under research have an opportunity of being included in the sample and the mathematical probability can be calculated. Each element of the frame thus has an equal probability of selection. It provides for greatest number of possible samples. This is done by assigning a number to each unit in the sampling frame. This type of sampling is very easy to apply as estimates are easy to calculate. An example of simple random sampling is when one draws three names from a hat containing all the names of the students in the class, any group of three names is as equally likely as picking any other group of three names. Sampling is a method of collecting information which, if properly carried out, can be convenient, fast, economical, and reliable. Unbiased and consistent estimates of the two population characteristics of greatest practical interest, the population mean and proportion, are the sample mean and the sample proportion respectively. These estimators are unbiased, whether the sample is with or without replacement. However, in random samples without replacement are more precise that is, they have lower variance than the same estimators based on samples with replacement of the same size. Sampling without replacement, therefore, should be the preferred sampling method.

(b)Systematic sampling

A systematic sampling method is obtained by separating the population into mutually exclusive sets, or strata, and then drawing simple random samples from each stratum or groups. Systematic sampling relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Unlike random sampling where targets are randomly picked and every target has an equal opportunity of being selected in systematic sampling targets are arranged in an orderly manner and are picked at regular intervals. Systematic sampling involves a random start and then proceeds with the selection of every target elements under study or research from then onwards. That is to say the starting point is not automatically the first in the list, but is instead randomly chosen by the one doing the research. A simple example of systematic sampling would be that of choosing or selecting every tenth name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10'). All elements or targets in systematic sampling have the same probability of selection (in the example given, one in ten). It is not 'simple random sampling' because different subsets of the same size have different selection probabilities and elements are now grouped and from the above example they are grouped into groups of ten. Systematic sampling method has got its benefits and limitations and the benefits includes that the sample is easy to select since the targeted element is controlled already, suitable sampling frame can be identified easily and samples are evenly spread over entire reference population. The limitations of using systematic sampling include the fact that the sample may be biased if hidden periodicity in population coincides with that of selection and difficult to assess precision of estimate from one survey.

©Cluster sampling

A cluster sample is a simple random sample of groups or clusters of elements (vs. a simple random sample of individual objects). Cluster sampling is an example of 'two-stage sampling are involved where the first stage a sample of areas is chosen and the second stage a sample of respondents within those areas is selected. Two types of cluster sampling methods. First-stage sampling is when all of the elements within selected clusters are included in the sample. Second-stage sampling all subset of elements within selected clusters are randomly selected for inclusion in the sample Population divided into clusters of homogeneous units, usually based on geographical contiguity. Sampling units are groups rather than individuals. A sample of such clusters is then selected. All units from the selected clusters are studied. This method is useful when it is difficult or costly to develop a complete list of the population members or when the population elements are widely dispersed geographically. Cluster sampling may increase sampling error due to similarities among cluster members. One can note that although strata and clusters are both non-overlapping subsets of the population, they differ in several ways. All strata are represented in the sample; but only a subset of clusters is in the sample. With stratified sampling, the best survey results occur when elements within strata are internally homogeneous. The reason to make this sampling is that sometimes it is too expensive to make a complete list of all the elements of the population that we want to study, or that when we finish making the list it may have no sense to make the study. The main disadvantage that we may have is that if the clusters are not homogeneous among them, the final sample may not be representative of the population. If we suppose that the clusters are as heterogeneous as the population, referring to the variable we are considering, and that the clusters are homogeneous among them, then to get a sample we only have to choose some clusters. We say that we make cluster sampling in one stage. This sampling method has the advantage that it simplifies the collecting of the sample information. However, one can note that with cluster sampling, the best results occur when elements within clusters are internally heterogeneous.

(d)Convenience sampling
Sometimes known as grab or opportunity sampling or accidental or haphazard sampling. A type of non-probability sampling which involves the sample being drawn from that part of the population which is close to hand, that is readily available and convenient. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer was to conduct a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing.

(e)Snowball sampling

Snowball sampling is a type of purpose sampling where existing participants recruit future subjects from among their acquaintances. Thus, the sample group appears to grow like a rolling snowball as stated by (Vogt: 1999). Purpose sampling is a non- random selection of participants on purpose. In snowball sampling, you begin by identifying someone who meets the criteria for inclusion in your study. You then ask them to recommend others who they may know who also meet the criteria. Although this method would hardly lead to representative samples, there are times when it may be the best method available. Snowball sampling is especially useful when you are trying to reach populations that are inaccessible or hard to find. For instance, if you are studying the homeless, you are not likely to be able to find good lists of homeless people within a specific geographical area. However, if you go to that area and identify one or two, you may find that they know very well who the other homeless people in their vicinity are and how you can find them. In Snowball sampling is not a stand-alone tool; the tool is a way of selecting participants and then using other tools, such as interviews or surveys. Having identified those with the skills and or knowledge or characteristics you require, you would then approach these people to invite them to participate in a community consultation process. This sampling technique has got its strengths and weaknesses and the strengths of this technique include that it is often used in hidden populations which are difficult for researchers to access for instance drug users or criminals, increases credibility of research, as participants are involved in the research process, cost efficient, helps to determine stakeholders, Increases the number of participants in process, builds on resources of existing networks and determines stakeholders unknown to you as postulated by Thomson, (1997) . According to (Van Meter, 1990; Kaplan et al, 1987 the weaknesses of snowball sampling technique include subjection for possible biases for example participants with many friends are more likely to be selected, researcher bias as it involves deliberate choices, difficulty of obtaining anonymity between participants, choice of initial contacts is most important and participation process should be drafted prior to the sampling to encourage participation from potential contacts which is much work for the researcher.