sampling techniques used in research
DESCRIPTION
different sampling techniques used in research : simple random sampling, random samplingTRANSCRIPT
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