Types and Methods of Sampling – Probability and Non-Probability Sampling Techniques

In this article we are discussing about various types of sampling and methods of sampling – Probability and Non-Probability sampling techniques in Research.

Types of Sampling:

The technique of selecting a sample is of fundamental importance in sampling theory and it depends upon the nature of investigation. The sampling procedures which are commonly used may be classified as types methods of sampling 

1. Probability sampling techniques
2. Non-probability sampling techniques

Probability and Non-Probability Sampling Techniques

Types and Methods / techniques of Probability Sampling

In statistics, probability sampling refers to the sampling method in which all the members of the population has a pre-specified and an equal chance to be a part of the sample. This technique is based on the randomization principle, wherein the procedure is so designed, which guarantees that each and every individual of the population has an equal selection opportunity. This helps to reduce the possibility of bias.

Statistical inferences can be made by the researchers using this technique, i.e. the result obtained can be generalised from the surveyed sample to the target population. The methods and Types of probability sampling are provided below:

• Simple Random Sampling
• Stratified Sampling
• Cluster Sampling
• Systematic Sampling

Methods / techniques of Non-Probability Sampling

When in a sampling method / techniques, all the individuals of the universe are not given an equal opportunity of becoming a part of the sample, the method is said to be Non-probability sampling. Under this technique as such, there is no probability attached to the unit of the population and the selection relies on the subjective judgment of the researcher. Therefore, the conclusions drawn by the sampler cannot be inferred from the sample to the whole population. The methods and types of non-probability sampling are listed below:

• Convenience Sampling
• Quota Sampling
• Judgment or Purposive Sampling
• Snowball Sampling

Basis for Comparison	Probability Sampling	Non-Probability Sampling
Meaning	Probability sampling is a sampling technique, in which the subjects of the population get an equal opportunity to be selected as a representative sample.	Nonprobability sampling is a method of sampling wherein, it is not known that which individual from the population will be selected as a sample.
Alternately known as	Random sampling	Non-random sampling
Basis of selection	Randomly	Arbitrarily
Opportunity of selection	Fixed and known	Not specified and unknown
Research	Conclusive	Exploratory
Result	Unbiased	Biased
Method	Objective	Subjective
Inferences	Statistical	Analytical
Hypothesis	Tested	Generated

 Types and Methods of Probability and Non-Probability Sampling – Research Methodology

TYPES AND METHODS OF NON-PROBABILITY SAMPLING:

In non-probability sampling methods, types, techniques, designs, the elements in the population do not have any probabilities attached to their being chosen as sample subjects. This means that the findings from the study of the sample cannot be confidently generalized to the population. However the researchers may at times be less concerned about generalizability than obtaining some preliminary information in a quick and inexpensive way. Sometimes non-probability could be thee only way to collect the data.

1. Convenience Sampling

Convenience sampling techniques (also called haphazard or accidental sampling) refers to sampling by obtaining units or people who are most conveniently available.  For example, it may be convenient and economical to sample employees in companies in a nearby area, sample from a pool of friends and neighbors. The person-on-the street interview conducted by TV programs is another example. TV interviewers go on the street with camera and microphone to talk to few people who are convenient to interview. The people walking past a TV studio in thee middle of the day do not represent everyone (homemakers, people in the rural areas). Likewise, TV interviewers select people who look “normal” to them and avoid people who are unattractive, poor, very old, or inarticulate.

Another example of haphazard sample is that of a newspaper that asks the readers to clip a questionnaire from the paper and mail it in. Not everyone reads thee newspaper, has an interest in the topic, or will take the time to cut out the questionnaire, and mail it. Some will , and the number who do so may seem large, but the sample cannot be used to generalize accurately to the population. Convenience samples are least reliable but normally the cheapest and easiest to conduct. Convenience sampling is most often used during the exploratory phase of a research project and is perhaps the best way of getting some basic information quickly and efficiently. Often such sample is taken to test ideas or even to gain ideas about a subject of interest.

2. Purposive Sampling

Depending upon the type of topic, the researcher lays down the criteria for the subjects to be included in the sample. Whoever meets that criteria could be selected in the sample. The researcher might select such cases or might provide the criteria to somebody else and leave it to his/her judgment for the actual selection of the subjects. That is why such a sample is also called as judgmental or expert opinion sample. For example a researcher is interested in studying students who are enrolled in a course on research methods, are highly regular, are frequent participants in the class discussions, and often come with new ideas. The criteria has been laid down, the researcher may do this job himself/herself, or may ask the teacher of this class to select the students by using the said criteria. In the latter situation we are leaving it to the judgment of the teacher to select the subjects. Similarly we can give some criteria to the fieldworkers and leave it to their judgment to select the subjects accordingly. In a study of working women the researcher may lay down the criteria like: the lady is married, has two children, one of her child is school going age, and is living in nuclear family.

3. Quota Sampling

A sampling procedure that ensures that certain characteristics of a population sample will be represented to the exact extent that the researcher desires. In this case the researcher first identifies relevant categories of people (e.g. male and female; or under age 30, ages 30 to 60, over 60, etc) then decides how many to get in each category. Thus the number of people in various categories of sample is fixed. For example the researcher decides to select 5 males and 5 females under age 30, 10 males and 10 females aged 30 to 60, and 5 males and 5 females over age 60 for a 40 person sample. This is quota sampling.

Once the quota has been fixed then the researcher may use convenience sampling. The convenience sampling may introduce bias. For example, the field worker might select the individual according to his/her liking, who can easily be contacted, willing to be interviewed, and belong to middle class. Quota sampling can be considered as a form of proportionate stratified sampling, in which a predetermined proportion of people are sampled from different groups, but on a convenience basis. Speed of data collection, lower costs, and convenience are the major advantages of quota sampling compared to probability sampling. Quota sampling becomes necessary when a subset of a population is underrepresented, and may not get any representation if equal opportunity is provided to each. Although there are many problems with quota sampling, careful supervision of the data collection may provide a representative sample of the various subgroups within the population.

4. Snowball Sampling

Snowball sampling methods (also called network, chain referral, or reputational sampling) is a method for identifying and sampling (or selecting) cases in the network. It is based on an analogy to a snowball, which begins small but becomes larger as it is rolled on wet snow and picks up additional snow. It begins with one or a few people or cases and spreads out on the basis of links to the initial cases. This design has been found quite useful where respondents are difficult to identify and are best located through referral networks. In the initial stage of snowball sampling, individuals are discovered and may or may not be selected through probability methods. This group is then used to locate others who possess similar characteristics and who, in turn, identify others. The “snowball” gather subjects as it rolls along.

For example, a researcher examines friendship networks among teenagers in a community. He or she begins with three teenagers who do not know each other. Each teen names four close friends. The researcher then goes to the four friends and asks each to name four close friends, then goes to those four and does the same thing again, and so forth. Before long, a large number of people are involved. Each person in the sample is directly or indirectly tied to the original teenagers, and several people may have named the same person. The researcher eventually stops, either because no new names are given, indicating a closed network, or because the network is so large that it is at the limit of what he or she can study.

5. Sequential Sampling

Sequential sampling types is similar to purposive sampling with one difference. In purposive sampling, the researcher tries to find as many relevant cases as possible, until time, financial resources, or his or he energy is exhausted. The principle is to get every possible case. In sequential sampling, a researcher continues to gather cases until the amount of new information or diversity is filled. The principle is to gather cases until a saturation point is reached. In economic terms, information is gathered, or the incremental benefit for additional cases, levels off or drops significantly. It requires that the researcher continuously evaluates all the collected cases. For example, a researcher locates and plans in-depth interviews with 60 widows over 70 years old who have been living without a spouse for 10 or more years. Depending on the researcher’s purposes, getting an additional 20 widows whose life experiences, social background, and worldview differ little from the first 60 may be unnecessary.

6. Theoretical Sampling

In theoretical sampling techniques, what the researcher is sampling (e.g. people, situation, events, time periods, etc.) is carefully selected, as the researcher develops grounded theory. A growing theoretical interest guides the selection of sample cases. The researcher selects cases based on new insights they may provide. For example, a field researcher may be observing a site and a group of people during week days. Theoretically, the researcher may question whether the people at the same at other times or when other aspects of site change. He or she could then sample other time periods (e.g. nights and weekends) to get more full picture and learn whether important conditions are the same.

TYPES AND METHODS OF PROBABILITY SAMPLING

1. Simple random sampling is a completely random method of selecting subjects. These can include assigning numbers to all subjects and then using a random number generator to choose random numbers. Classic ball and urn experiments are another example of this process (assuming the balls are sufficiently mixed). The members whose numbers are chosen are included in the sample.

2. Stratified Random Sampling involves splitting subjects into mutually exclusive groups and then using simple random sampling to choose members from groups.

3. Systematic Sampling means that you choose every “nth” participant from a complete list. For example, you could choose every 10th person listed.

4. Cluster Random Sampling is a way to randomly select participants from a list that is too large for simple random sampling. For example, if you wanted to choose 1000 participants from the entire population of the U.S., it is likely impossible to get a complete list of everyone. Instead, the researcher randomly selects areas (i.e. cities or counties) and randomly selects from within those boundaries.

5. Multi-Stage Random sampling uses a combination of techniques.

Advantages and Disadvantages

Each probability sampling method has its own unique advantages and disadvantages.

Advantages

• Cluster sampling: convenience and ease of use.
• Simple random sampling: creates samples that are highly representative of the population.
• Stratified random sampling: creates strata or layers that are highly representative of strata or layers in the population.
• Systematic sampling: creates samples that are highly representative of the population, without the need for a random number generator.

Disadvantages

• Cluster sampling: might not work well if unit members are not homogeneous (i.e. if they are different from each other).
• Simple random sampling: tedious and time consuming, especially when creating larger samples.
• Stratified random sampling: tedious and time consuming, especially when creating larger samples.
• Systematic sampling: not as random as simple random sampling,

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