Sampling Strategies for Educational Research

Dr. V.K. Maheshwari, Former Principal

K.L.D.A.V(P.G) College, Roorkee, India

Traditionally, the sample is always better than the stock you deliver to the store. ~Calvin Klein

Research is is a careful or diligent search, studious inquiry or examination especially investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts or practical application of such new or revised theories or laws, it can also be the collection of information about a particular subject.

In research it would be ideal to include the entire population when conducting a study; this enables a generalization to be made about the results to the population as a whole. In some cases this has been possible, but not always.

Cox and West describe a population as a well-defined group of people or objects that share common characteristics. A population in a research study is a   group of individual’s persons, objects, or items from which samples are taken for measurement .A population is   group about which some information is sought

Sometimes, the entire population will be sufficiently small, and the researcher can include the entire population in the study. This type of research is called a census study because data is gathered on every member of the population.

Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but carefully chosen sample can be used to represent the population. The sample reflects the characteristics of the population from which it is drawn.

Since it will not be practical to recruit every human for the study, it is necessary to define an accessible population. The accessible population is a subset of the target population that reflects specific characteristics  of the entire Most people intuitively understand the idea of sampling. The  basic idea of sampling is that by selecting some of the elements in a population, we may draw conclusions about the entire population. A sample is a finite part of a statistical population whose properties are studied to gain information about the whole .

The cost of studying an entire population to answer a specific question is usually prohibitive in terms of time, money and resources. Therefore, a subset of subjects representative of a given population must be selected; this is called sampling. The concepts involved in selecting subjects to represent the larger population are presented

The Purpose of Sampling

There are some distinct advantages and disadvantages in using samples. Advantages include that sampling involves a smaller number of subjects and is more time efficient, less costly and potentially more accurate (since it is more feasible to maintain control over a smaller number of subjects). Disadvantages include potential bias in the selection of subjects, which may lead to error in interpretation of results and decrease in ability to generalize the results beyond the subjects actually studied.

There are six main reasons for sampling instead of doing a census. These are; -Economy -Timeliness -The large size of many populations -Inaccessibility of some of the population -Destructiveness of the observation –accuracy

The Economic Advantage-Obviously, taking a sample requires fewer resources than a census. For the type of information desired, a small wisely selected sample can serve the purpose. . Rarely does a circumstance require a census of the population, and even more rarely does one justify the expense.

The Time Factor-A sample may provide you with needed information quickly.

The Very Large Populations-Many populations about which inferences must be made are quite large. In such a case, selecting a representative sample may be the only way to get the information required

The Partly Accessible Populations-There are some populations that are so difficult to get access to that only a sample can be used. . The inaccessibility may be economic or time related. Like a particular study population may be so costly to reach like the population of planets that only a sample can be used. In other cases, a population of some events may be taking too long to occur that only sample information can be relied on.

The Destructive Nature of the Observation -Sometimes the very act of observing the desired characteristic of a unit of the population destroys it for the intended use. Good examples of this occur in quality control.

Accuracy and Sampling-A sample may be more accurate than a census. A sloppily conducted census can provide less reliable information than a carefully obtained sample.

Concerns in Statistical Sampling

Representativeness This is the primary concern in statistical sampling. The sample obtained from the population must be representative of the same population.

The reason behind representativeness being the primary concern in statistical sampling is that it allows the researcher to draw conclusions for the entire population. If the sample is not representative of the population, conclusions cannot be drawn since the results that the researcher obtained from the sample will be different from the results if the entire population is to be tested.

Practicability -Practicability of statistical sampling techniques allows the researchers to estimate the possible number of subjects that can be included in the sample, the type of sampling technique, the duration of the study, the number of materials, ethical concerns, availability of the subjects/samples, the need for the study and the amount of workforce that the study demands.

Sampling Error-A sample is expected to mirror the population from which it comes, however, there is no guarantee that any sample will be precisely representative of the population from which it comes.

Sampling error comprises the differences between the sample and the population that are due solely to the particular units that happen to have been selected.

Unfortunately, all samples deviate from the true nature of the overall population by a certain amount due to chance variations in drawing the sample’s few cases from the population’s many possible members. This is called sampling error and is distinguished from non-chance variations due to determining factors. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error

The more dangerous error is the less obvious sampling error against which nature offers very little protection. There are two basic causes for sampling error. One is chance: That is the error that occurs just because of bad luck. This may result in untypical choices. Unusual units in a population do exist and there is always a possibility that an abnormally large number of them will be chosen. The second cause of sampling is sampling bias. Sampling bias is a tendency to favor the selection of units that have particular characteristics. Sampling bias is usually the result of a poor sampling plan. The most notable is the bias of non response when for some reason some units have no chance of appearing in the sample.

Non-Sampling Error (Measurement Error)-The other main cause of unrepresentative samples is non sampling error. This type of error can occur whether a census or a sample is being used. Like sampling error, non sampling error may either be produced by participants in the statistical study or be an innocent by product of the sampling plans and procedures.

A non sampling error is an error that results solely from the manner in which the observations are made.

The Interviewers Effect-No two interviewers are alike and the same person may provide different answers to different interviewers. The manner in which a question is formulated can also result in inaccurate responses. Individuals tend to provide false answers to particular questions.

The Respondent Effect-Respondents might also give incorrect answers to impress the interviewer. This type of error is the most difficult to prevent because it results from out right deceit on the part of the responder. It is important to acknowledge that certain psychological factors induce incorrect responses and great care must be taken to design a study that minimizes their effect.

Knowing the Study Purpose-Knowing why a study is being conducted may create incorrect responses. A classic example is the question: What is your income? If a government agency is asking, a different figure may be provided than the respondent would give on an application for a home mortgage.

Induced Bias-Finally, it should be noted that the personal prejudices of either the designer of the study or the data collector may tend to induce bias. In designing a questionnaire, questions may be slanted in such a way that a particular response will be obtained even though it is inaccurate

Types of Sampling

A basic principle of sampling is that every member of the population must have an equal chance of being included in the sample

The two types of sampling methods, probability and nonprobability, are defined and presented with their respective types

Non-Probability Sampling, members are selected from the population in some nonrandom manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling Non-probability sampling includes convenience sampling, consecutive sampling, judgmental sampling, quota sampling and snowball sampling In non-probability sampling, the degree to which the sample differs from the population remains unknown

Probability Samples, each member of the population has a known non-zero probability of being selected. Probability methods include random sampling, systematic sampling, and stratified sampling. Probability sampling includes simple random sampling, systematic sampling, stratified sampling, cluster sampling and disproportional sampling The advantage of probability sampling is that sampling error can be calculated.

The Convenient Sample-Convenience sampling is probably the most commonly used technique in  research today . With convenience sampling, subjects are selected because of their convenient accessibility to the researcher. These subjects are chosen simply because they are the easiest to obtain for the study. This technique is easy, fast and usually the least expensive and troublesome convenience sample results when the more convenient elementary units are chosen from a population for observation. Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient. This Non-probability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.

Consecutive Sampling -Is a strict version of convenience sampling where every available subject is selected, i.e., the complete accessible population is studied. This is the best choice of the Non-probability sampling techniques since by studying everybody available, a good representation of the overall population is possible in a reasonable period of time ..

The Judgment Sample-Judgmental sampling, also called Purposive Sampling, is another form of convenience sampling where subjects are handpicked from the accessible population Subjects usually are selected using judgmental sampling because the researcher believes that certain subjects are likely to benefit or be more compliant A judgement sample is obtained according to the discretion of someone who is familiar with the relevant characteristics of the population. It is a common non-probability method. The researcher selects the sample based on judgment.

Disproportional Sampling-Disproportional sampling is a method that facilitates the difficulty encountered with stratified samples of unequal size .

Random sampling-The random sample is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased.

This may be the most important type of sample. A random sample allows a known probability that each elementary unit will be chosen. This is the type of sampling that is used in lotteries and raffles.

Types of random Samples

A Simple Random Sample-A simple random sample is obtained by choosing elementary units in search a way that each unit in the population has an equal chance of being selected. A simple random sample is free from sampling bias. However, using a random number table to choose the elementary units can be cumbersome. If the sample is to be collected by a person untrained in statistics, then instructions may be misinterpreted and selections may be made improperly.

A systematic random sample-Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file

A systematic random sample is obtained by selecting one unit on a random basis and choosing additional elementary units at evenly spaced intervals until the desired number of units is obtained.

A Stratified Sample-Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share at least one common characteristic. . The researcher first identifies the relevant stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. “Sufficient” refers to a sample size large enough for us to be reasonably confident that the stratum represents the population. Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums.

A stratified sample is obtained by independently selecting a separate simple random sample from each population stratum. A population can be divided into different groups may be based on some characteristic or variable .

Quota Sampling-Quota sampling is the Non-probability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling Quota sampling is a non-probability technique used to ensure equal representation of subjects in each layer of a stratified sample grouping

A Cluster Sample-A cluster sample is obtained by selecting clusters from the population on the basis of simple random sampling. The sample comprises a census of each random cluster selected. Cluster sampling is a method used to enable random sampling to occur while limiting the time and costs that would otherwise be required to sample from either a very large population or one that is geographically diverse. Using this method, a one- or two-level randomization process is used the important element in this process is that each one of the criteria have an equal opportunity to be chosen, with no researcher or facility bias

Purposeful Sampling-Purposeful sampling selects information rich cases for in-depth study. Size and specific cases depend on the study purpose.

Intensity Sampling- This is information rich cases that manifest the phenomenon intensely, but not extremely, such as good students, poor students, above average/below average. Maximum variation sampling this involves purposefully picking a wide range of variation on dimensions of interest. This documents unique or diverse variations that have emerged in adapting to different conditions. It also identifies important common patterns that cut across variations.

Typical Case Sampling -It involves taking a sample of what one would call typical, normal or average for a particular phenomenon,

Stratified Purposeful Sampling- This illustrates characteristics of particular subgroups of interest and facilitates comparisons between the different groups.

Critical Case Sampling -This permits logical generalization and maximum application of information to other cases like “If it is true for this one case, it is likely to be true of all other cases.

Snowball or Chain Sampling -This particular one identifies, cases of interest from people who know people who know what cases are information rich that is good examples for study, good interview subjects. This is commonly used in studies that may be looking at issues like the homeless households. What you do is to get hold of one and he/she will tell you where the others are or can be found. When you find those others they will tell you where you can get more others and the chain continues. Snowball sampling is a special Non-probability method used when the desired sample characteristic is rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population.

Criterion Sampling -Here, you set a criteria and pick all cases that meet that criteria  This method of sampling is very strong in quality assurance.

Theory Based or Operational Construct Sampling -Finding manifestations of a theoretical construct of interest so as to elaborate and examine the construct. Confirming and dis-confirming cases elaborating and deepening initial analysis like if you had already started some study, you are seeking further information or confirming some emerging issues which are not clear, seeking exceptions and testing variation.

Opportunistic Sampling- This involves following new leads during field work, taking advantage of the unexpected flexibility.

Random Purposeful Sampling -This adds credibility when the purposeful sample is larger than one can handle. Reduces judgment within a purposeful category. But it is not for generalizations or representativeness.

Convenience Sampling -It is useful in getting general ideas about the phenomenon of interest. . It saves time, money and effort. It is the poorest way of getting samples, has the lowest credibility and yields information-poor cases.

Combination or Mixed Purposeful Sampling- This combines various sampling strategies to achieve the desired sample. This helps in triangulation, allows for flexibility, and meets multiple interests and needs. When selecting a sampling strategy it is necessary that it fits the purpose of the study, the resources available, the question being asked and the constraints being faced. This holds true for sampling strategy as well as sample size.

Sampling Risks

There are two types of sampling risks, first is the risk of acceptance of the research hypothesis and the second is the risk for incorrect rejection. These risks pertain to the possibility that when a test is conducted to a sample, the results and conclusions may be different from the results and conclusions when the test is conducted to the entire population.

The risk of incorrect acceptance pertains to the risk that the sample can yield a conclusion that supports a theory about the population when it is actually not existent in the population. On the other hand, the risk of incorrect rejection pertains to the risk that the sample can yield a conclusion that rejects a theory about the population when in fact, the theory holds true in the population.

Comparing the two types of risks, researchers fear the risk of incorrect rejection more than the risk of incorrect acceptance. Consider this example; an experimental drug was tested for its debilitating side effects. With the risk of incorrect acceptance, the researcher will conclude that the drug indeed has negative side effects but the truth is that it doesn’t. The entire population will then abstain from taking the drug. But with the risk of incorrect rejection, the researcher will conclude that the drug has no negative side effects. The entire population will then take the drug knowing that it has no side effects but all of them will then suffer the consequences of the mistake of the researcher.

Language also may present a potential difficulty with recruitment. Therefore, a brochure in the appropriate foreign language or a staff or volunteer who can translate or interpret the foreign language may be required.

Exclusion Criteria

Exclusion criteria are applied to subjects who generally meet the inclusion criteria but must be excluded because they cannot complete the study or possess unique characteristics that may confound the results. Subjects who may have unreliable sources of transportation or noncompliant parents also may need to be excluded. An important ethical consideration is the willingness of the subject to participate.

Sample Size

Before deciding how large a sample should be, you have to define your study population. The question of how large a sample should be is a difficult one. Sample size can be determined by various constraints. For example, the available funding may prespecify the sample size. When research costs are fixed, a useful rule of thumb is to spent about one half of the total amount for data collection and the other half for data analysis. This constraint influences the sample size as well as sample design and data collection procedures.

In general, sample size depends on the nature of the analysis to be performed, the desired precision of the estimates one wishes to achieve, the kind and number of comparisons that will be made, the number of variables that have to be examined simultaneously and how heterogeneous a universe is sampled. In non-experimental research, most often, relevant variables have to be controlled statistically because groups differ by factors other than chance.

Deciding on a sample size for qualitative inquiry can be even more difficult than quantitative because there are no definite rules to be followed. It will depend on what you want to know, the purpose of the inquiry, what is at stake, what will be useful, what will have credibility and what can be done with available time and resources. With fixed resources which are always the case, you can choose to study one specific phenomenon in depth with a smaller sample size or a bigger sample size when seeking breadth. In purposeful sampling, the sample should be judged on the basis of the purpose and rationale for each study and the sampling strategy used to achieve the studies purpose. The validity, meaningfulness, and insights generated from qualitative inquiry have more to do with the information-richness of the cases selected and the observational/analytical capabilities of the researcher than with sample size.


The goals and concepts related to recruitment are reviewed with application to survey and experimental research. Three steps are suggested for obtaining an appropriate research sample: clearly define the target population, the accessible population and define the steps and effort that will be employed to recruit subjects for study.

The goals of sampling are to decrease time and money costs, to increase the amount of data and detail that can be obtained, and to increase accuracy of data collection by preventing errors.

To accomplish these goals it is necessary to follow these steps:

  • Clearly define the target population to which the results will be generalized. .
  • An accessible population representative of the target must be defined by additional inclusion criteria with specific characteristics regarding the geographic, social and time frames required for this subpopulation.

The sampling process must be defined well ahead of subject selection whether it be a random (probability) or nonrandom (non-probability) approach, and the researchers must adhere to a specific technique for recruitment appropriate for that approach. The recruitment effort must be vigorous enough to assure a large enough sample to enable statistical validity and must minimize probability of error and bias of selection.

In conclusion, it can be said that using a sample in research saves mainly on money and time, if a suitable sampling strategy is used, appropriate sample size selected and necessary precautions taken to reduce on sampling and measurement errors, then a sample should yield valid and reliable information

Sampling is the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population The process of defining a representative subpopulation to study is called sampling.


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