The Concept Of Sampling

The concept of sampling

Let us take a very simple example to explain the concept of sampling. Suppose you want to estimate the average age of the students in your class. There are two ways of doing this. The first method is to contact all students in the class, find out their ages, add them up and then divide this by the number of students (the definition of an average). The second method is to select a few students from the class, ask them their ages, add them up and then divide by the number of students you have asked. From this you can make an estimate of the average age of the class. Take another example: suppose you want to find out the average income of families living in a city. You could follow the procedures described above, but imagine the amount of effort and resources required to go to each family in the city to find out their income! You could follow the second method by selecting a few families to become the basis of your inquiry and then, from what you have found out from the few families, make an estimate of the average income of families in the city. A further example would be the outcome of an election: the result is decided after voting on Election Day, but predictions about the outcome are usually made on the basis of opinion polls. These polls are based upon a very small group of people who are questioned about their voting preferences. On the basis of these results, a prediction is made about the outcome.

Sampling, therefore, is the process of selecting a few (a sample) from a bigger group (the sampling population) to become the basis for estimating or predicting the prevalence of an unknown piece of information, situation or outcome regarding the bigger group. A sample is a subgroup of the population you are interested in. See Figure 12.1.

Figure 12.1 the concept of sampling

This process of selecting a sample from the total population has advantages and disadvantages. The advantages are that it saves time as well as financial and human resources. However, the disadvantage is that you do not find out the information about the population’s characteristics of interest to you but only estimate or predict them. Hence, the possibility of an error in your estimation exists.

Sampling is thus a trade-off between certain gains and losses. While on the one hand you save time and resources, on the other hand you may compromise the level of accuracy in your findings. Through sampling you only make an estimate about the actual situation prevalent in the total population from which the sample is drawn. If you ascertain a piece of information from the total sampling population, and if your method of inquiry is correct, your findings should be reasonably accurate. However, if you select a sample and use this as the basis from which to estimate the situation in the total population, an error is possible. Tolerance of this possibility of error is an important consideration in selecting a sample.

The concept of sampling in qualitative research:-

In qualitative research the issue of sampling has little significance as the main aim of most qualitative inquiries is either to explore or describe the diversity in a situation, phenomenon or issue. Qualitative research does not make an attempt to either quantify or determine the extent of this diversity. You can select even one individual as your sample and describe whatever the aim of your inquiry is. A study based upon the information obtained from one individual, or undertaken to describe one event or situation is perfectly valid. In qualitative research, to explore the diversity, you need to reach what is known as saturation point in terms of your findings; for example, you go on interviewing or collecting information as long as you keep discovering new information. When you find that you are not obtaining any new data or the new information is negligible, you are assumed to have reached saturation point. Some researchers prefer to select a sample using non-probability designs and to collect data till they have reached saturation point. Keep in mind that saturation point is a subjective judgment which you, as a researcher, decide.

Sampling terminology:-

Let us, again, consider the examples used above. Our main aims are to find out the average age of the class, the average income of the families living in the city, and the likely election outcome for a particular state or country. Let us assume that we adopt the second methodвЂ"that is, we select a few students, families or electorates to achieve these aims. In this process there are a number of aspects:

• The class, families living the city or electorates from which you select a few students, families, electors to question in order to find answers to your research questions are called the population or study population, and are usually denoted by the letter (N).

• The small group of students, families or electors from whom you collect the required information to estimate the average age of the class, average income or the election outcome is called the sample.

• The number of students, families or electors from whom you obtain the required information is called the sample size and is usually denoted by the letter (n).

• The way you select students, families or electors is called the sampling design or strategy.

• Each student, family or elector that becomes the basis for selecting your sample is called the sampling unit or sampling element.

• A list identifying each student, family or elector in the study population is called the sampling frame. If all elements in a sampling population cannot be individually identified, you cannot have a sampling frame for that study population.

• Your findings based on the information obtained from your respondents (sample) are called sample statistics. Examples of this information are the average age of students (calculated from the information obtained from those students who responded to your question on age); the average income of a family (calculated from the relevant information obtained from those families who participated in your study); and the expected outcome (predicted on the basis of the information obtained from those who expressed their intention in voting). Your sample statistics become the basis of estimating the prevalence of the above characteristics in the study population.

• Your main aim is