Friday, 24 March 2017

Sampling Method in Research: Random and Non-Random


Sampling Method in Research Random and Non-Random
Sampling Method in Research: Random Samples
Researchers often try to make inferences about the population on the basis of results from a survey sample. To draw samples from populations, researchers must first decide the population. Suppose that you were interested in studying the degree to which housewives in India rely on television commercials in preference to traditional methods. All the housewives in the country would constitutes the population.
Take another example. You are interested to study the degree to which the news items in national dailies are slanted on a particular topic or party. In this case all the news items of all the national daily newspapers will be the population. Researchers define population as having certain characteristics.
Sampling is the process of selecting units from a population. The total punctuation under study is called the ’universe' of the study. This practice is required as an alternative to census where you have to survey the entire population i.e. data is collected from each unit. This is chosen in cases where the size of the population is very large.
However, the conclusion of the study can be good only when the collection of data is done through logical reasoning behind choices. So we need to understand and study closely the population under study and how samples are drawn from that population in order to study the whole characteristics of that population.
A population is the universe of events from which the sample is drawn. In other words it is all the units about which the information is sought. The researcher, on the basis of certain characteristics, defines populations. Though population may be quite broad as all people or all news items in all newspapers, they may be defined quite narrowly, such as television commercials that appear during network prime time, i.e. between 7p.m -11p.m
Sample is a part of the population selected for a particular research study. Researchers rarely sample all the events or units, but rely on a portion of all data to draw conclusions. Sampling may have the following dimension in a study of communication research:
  • In sampling events are selected from the population to be included in the study.
  • The results of the study are interpreted to test hypothesis and in order to estimate parameters of the population from sample data.

The individual elements or events in the population are assigned numbers. These numbers from population are called units. A sample is simply a selection of units from the population. The collection of selected individuals or events is called the sample. A statistic is a number computed from a sample. The statistics that reflect the features of the sample are called sample characteristics Communication researchers gather statistics from the sample to determine the properties of the population.


The process of selecting sample units from the population has to be objective and without bias. While sampling there is a tendency for the researcher to err. This is referred to as bias in sampling. Certainly, researchers work to eliminate and minimize bias. Opinion poll predictions and television rating services are supposed to be accurate, for this elimination of bias in sampling is a precondition. Controlling bias is critical as accuracy does not occur by accident.
Representative sample: The goal of effective sampling should be that a good sample must be representative of the population and big enough to permit reasonable analysis of data. A representative sample is one that accurately reflects characteristics of the population from which it is drawn,


"How big should a sample be?" This question is crucial for the research students. Generally researchers collect the sample large enough to make reasonable interpretations. Yet, large sample size is not enough to prove that a sample is representative of the population. The students of communication research should try to gather a reasonably sized sample in order to make a good and representative study.
The size of the sample should be determined keeping in view the following factors:
  • Degree of accuracy required
  • Time available for completion of the study
  • Manpower available
  • Finances available
  • Subject matter of research

There cannot be an ideal proportion between the size of the size of the universe and the size of the sample. ln most cases sample size is governed by the above-mentioned factors. However, it may also be noted that very small sample may give distorted results. At the same time, very large sample may also be wastage of resources.


AS we study a definite sized sample and not the entire population some error is bound to occur in telling the characteristics of the population exactly.
Sampling error may be defined as the degree to which sample attributes differ from population characteristics on certain measures. If we study the entire population then there is no sampling error at all. But we also know that it is impossible to study the entire population over a fixed period of time and we are bound to resort to sampling. Larger the size of the sample lesser will be the sampling error.
Margin of error is the amount of sampling error associated with the sample. If we collect a big sample this margin of error can be reduced and our research reports may be very accurate. In good sample studies, sample error is generally indicated. For example, if in a population 40 percent of the households own television sets, but a sample study may reveal that 39 percent households have television sets in their homes. This is sampling error, but from research point of view 39 percent and 40 percent is not a big difference. In research study it may be indicated as + 1%.


Broadly, sampling can be done in two ways:
  • Probability sampling (Random Sampling) and
  • Non-probability sampling (Non-Random Sampling)

Probability sampling is more commonly known as Random Sampling. Non-probability sampling is called non-random sampling.

Probability Sampling (RANDOM SAMPLING): 

Random sampling plays an important part in research. In this form of sampling the selection of sample is done in such a way that each event in the population gets equal chance of selection. Random sampling is taken for ail statistical tools, which are applicable to data.
The distributions of randomly occurring events can be used to figure the odds that a sample truly reflects the characteristics of the population. Random sampling is not haphazard, unsystematic or accidental. However, in research random means every unit gets equal chance of selection.
Random sampling is considered as a systematic and most scientific means of studying the population. Random sampling consumes a lot of time and most researchers want shortcuts. But the shortcuts throw off the whole sample and leads to faulty results.


  • Simple Random sampling
  • Stratified random sampling
  • Cluster sampling
  • Systematic sampling

Simple Random Sampling: In this type of random sampling the selection of data is done in such a way that each event (individuals) gets an equal chance for selection. It may be done by way of pulling names out of a container. Numbers are assigned to each individual events and a lot can be drawn randomly or using a random number table the numbers to be included in the final selection can be drawn. This increases the representativeness and sampling error can be easily computed.

Stratified random sampling: Population is divided into different strata based on the known proportions or properties and random sampling is completed within each group in the population. As in simple random sampling this method is also time consuming but allows analysis by sub division of strata and the disproportionate representation of the sub divisions of the population is also prevented.
Say in a study of the opinion of men and women on certain issues of a particular place. The member of men and women are identified. After deciding the number of men and women to be taken for study a simple random sample is drawn from each sub group of the population stratification (the population is stratified as men & women).
For a study on an industry strata can be divided into managers, superiors, skilled workers, and unskilled workers. For a study on rural youth the strata can be student youth, non-student youth, rich-, medium-, or poor youth.
But it should be noted that in a study based on stratified sampling, results should be drawn for each stratum separately and various strata should not be merged for the entire population. This will give you erroneous results.

Custer Sampling: In cluster sampling groups of events or areas (clusters) are taken as a unit (rather than taking single individual events as units) and an actual sample is drawn from them. This method is considered as a practical solution to the problems of gaining access to many settings and the cost of sampling is minimized in large-scale surveys.
However, this sampling is disadvantaged by the requirement of larger samples and weights for each strata or each individual event may be difficult to know in many settings. Results cannot be taken as representative for the entire population.

Systematic Sampling: This is a commonly used method in which cluster sampling and stratified sampling are combined. Every n-th event or cluster in the population is taken for study and a systematic sampling is done among the events or clusters thus selected.


We know that some errors are bound to occur whatever method we used for sampling. Errors due to sampling factors (Sampling error) usually get the most attention. However, there are a lot of factors, which affect a study. These factors (or the sources of error) are critical as there are so many of them. The non-sampling errors may include.
  • Refusal by respondents for interview
  • Intentional lying by respondent
  • Prevalent opinion
  • Poor recall
  • Subculture of respondents
  • Miscommunication
  • Coding errors
  • Image of the interviewers
  • Recording error
  • Misunderstanding of questions being asked


This form of sampling is applicable where random sampling is not possible.
Non-random sampling may be classified as:
  • Convenience sampling,
  • Quota sampling
  • Purposive or known group sampling, and
  • Snowball sampling

Convenience sampling: ln convenience sampling no attempt at randomization is made. Here selection of respondents/events depends upon the availability. Although economical in nature the computation of bias is not possible in this case and the generalisation to the population is out of question.
Quota sampling: This method of sampling attempts that important parts of the population are not omitted and samples are defined based on the known proportions within the population and non-random sampling is completed within each group.
Purposive or Known group sampling: This is a convenient and economical sampling method when key population characteristics are identified. Here the selection of respondents is from groups that are known to possess particular characteristics under investigation. However, in this case also the generalization to the population is also not possible.
SnowballI Sampling: Snowball sampling is highly useful in studies where population units are not well defined and thus cannot be listed. The selection of respondents is based on referrals from initial informants. In this case the respondent is requested to refer the researcher to other individuals in the group. Examples of not so well defined population are members of underworld organization, prostitutes’ criminals, AIDS patients, users of a particular brand, etc.
However, studies depending on snowball sampling provide broad features of the population and cannot be considered as actual research.


As we know that all the forms of sampling random or non-random have advantages and disadvantages depending upon the type, nature of the study, we require selecting a suitable method. We also know that the study of research is riddled with choices and compromises. This would in the best interest of the researcher that all the pros and cons are zoomed in and adopt so that they can produce good research results. Randomization is a pain in the neck so far as the credibility of the study is concerned.