sampling techniques used in research

Sampling techniques

in research

Population is defined as the entire mass of observation, which is the parent group from which a sample is to be formed.

Sample is defined as the aggregate of objects, person or elements, selected from the universe.

Sampling- The method of taking the sample is known as sampling.

Importance of Sampling

Economical

Accuracy

Save time and efforts

Easily approachable

Errors can be controlled

Practical

Sampling

Probability Non Probability

Probability sampling

• Every unit of the population has an equal chance of being selected for the sample.

Non probability sampling

• Sampling techniques one cannot estimate beforehand the chance of each element being included in the sample.

Simple random sampling

Stratified random sampling

Systematic sampling

Cluster sampling

Multi-stage sampling

Probability sampling

Random sampling is applied when the method of selection assures each individual element in the universe an equal chance of being chosen.

Advantages

Conjunction with other methods

unbiased

Easy to find errors

Equal chance

Consumes time and energy

More chances of misleading

sample

Difficult for comparison

study

Disadvantages

Lottery Method

Tippet’s Number

Grid method

METHODS OF RANDOM SAMPLING

Stratified sampling - When the population is divided into different strata then samples are selected from each stratum by simple random sampling or by regular interval method we call it as stratified random sampling method.

Advantages

•Comparing sub-categories

•Save time, money and energy

•Can represent various group

Requires more efforts

Needs a larger sample size

Strata are overlapping, chances of bias

Disadvantages

Stratified sampling

Disproportionate sampling

Proportionate sampling

Systemic sampling - This sampling is obtaining a collection of elements by drawing every nth person after that; n is a number termed as sampling interval.

Advantages •Easy to use

Disadvantages•Over representation of several groups is greater.

Cluster Sampling- The whole population is surveyed and such areas are located wherein elements are seen clustering themselves and sample is selected from such clusters and they reflect all characteristics of the Universe.

AdvantagesEasier to apply larger Geographical area

Save time of travelling

Disadvantages

Not good representative of the population

Sampling error

Same individual can belong to two clusters and studied twice

Multi stage sampling sample is selected in various stages but only last sample is studied.

Advantages •Good representative of population•Improvement of other sampling methods

Disadvantages•Difficult and complex method

Non probability Sampling- One cannot estimate beforehand the probability of each element being included in the sample. It also does not assure that every element has a chance of being included.

Non probability sampling

Incidental/ Accidental sampling

Convenience sampling

Purposive sampling

Quota sampling

Incidental or Accidental sampling means selecting the units on basis of easy approaches.

Advantages• Easy and quick results• Saves time, money and

energy

Disadvantages• Not representative of

population• Cannot produce reliable

results

In Convenience method, the investigator selects certain items are to his convenience. No pre planning is necessary for the selection of items.

disadvantages•Biased data•Not representative

population

Advantages• Easy method• Economical

Purposive sampling- The selection of elements is based upon the judgement of the researcher, the purposive sampling is called judgement sample

Advantages•Control on variable

Disadvantages•Reliability of criterion

is questionable

Quota sampling:-In the quote sampling the interviewers are instructed to interview a specified number of persons from each category.

• Practical• Economical

Advantages

• Not true representative• Not free from error

disadvantages

Errors

Sampling Errors

Biased errors

Unbiased errors

Non Sampling Errors

Technique Strength Weakness

Probability

Simple Random Sampling Easily understood, results projectable

Expensive, assurance of representative

Stratified sampling Include all important sub populations

Expensive, Difficult to select relevant stratification variables

Systemic sampling Increase representativeness

Can decrease representative

Cluster sampling Easy to implement, cost effective

Difficult to interpret results

Non probability

Convenience sampling Least expensive, least time consuming.

Quota sampling Sample can be controlled for certain characteristics

Bias, no assurance of representative

Choosing non Probability vs. Probability sampling

Conditions favouring the use of

Factors Non probability sampling

Probability sampling

Nature of research Exploratory Conclusive

Relative magnitude of sampling and non sampling errors

Non sampling errors are larger

Sampling errors are larger

Variability in the population

Homogeneous Heterogeneous

Statistical consideration Unfavourable Favourable

Operational considerations

Favourable Unfavourable