This document discusses different sampling techniques used in research studies. It begins by defining key concepts like population, sample, and sampling frame. It then distinguishes between census and sampling. Some characteristics of a good sample are representativeness, accuracy, precision and size. Factors to consider when choosing between census and sampling include population size, time, funds and facilities available. Advantages of sampling are reduced time, cost and better quality results. Limitations include potential for incorrect results and difficulty ensuring representativeness. Common sampling techniques discussed are probability/random sampling and non-probability sampling, with simple random sampling, stratified sampling, systematic sampling and cluster sampling provided as examples within probability sampling.

1. SAMPLING DESIGN
PRESENTED BY
DR.M.MUTHULAKSHMI
ASSISTANT PROFESSOR OF COMMERCE
A.P.C MAHALAXMI COLLEGE FOR WOMEN
THOOTHUKUDI

2. CONCEPT OF SAMPLING
• Empirical field studies collection of first hand information or data pertaining to the units of
the study from the field.The units of study may includes geographical area like district taluk
cities or villages which are covered by the study or institutions for households about which
information is required for persons from whom information is available.
• Aggregate of all the units pertaining to a study is called as population or the universe.
• A part of the population is known as a sample.
• The process of drawing a sample from the larger population is called sampling
• the list of sampling unit from which sample is taken is called the sampling frame example a
map a telephone directory the list of industrial undertaking list of car licensees etc.,

3. CENSUSVS SAMPLING
• The researcher must decide whether he should cover all the units are sample of units.
• When all the units of study such a complete coverage is called as census survey.
• Only a sample of the universe is studied study is called sample survey.

4. CHARACTERISTICS OF A GOOD SAMPLE
1. Representativeness
2. Accuracy
3. Precision
4. Size

5. FACTORS CONSIDERED TO CHOOSE CENSUS OR
SAMPLING
1. The size of the population
2. Amount of funds budgeted for the study
3. Facilities
4. Time

6. ADVANTAGES OF SAMPLING
1. Sampling reduces the time and cost of the research studies
2. Sampling saves labour
3. The quality of our research study is often better with sampling than is complete
coverage
4. Sampling provides much Quicker results than census
5. Sampling is the only procedure possible if the population of infinite example throws of
dice consumer behaviour survey etc

7. LIMITATIONS OF SAMPLING
• Sampling demands a through knowledge of sampling methods and procedures and an
exercise of greater care. Otherwise the results obtained may be incorrect or misleading.
• Banda characteristics to be measured only rarely in the population a very large sample is
required to secure units that will give reliable information about it. The lord the sample has all
the drawbacks of a census survey.
• A complicated sampling plan may require more labour than a complete coverage
• It may not be possible to ensure the representativeness of a sample even by the most perfect
sampling procedures. Therefore sampling results in a certain degree of sampling errors.i.e,
there will be some difference between the sampe value and the population value

8. SAMPLING TECHNIQUES OR METHODS
• Sampling techniques or methods may be classified into two generic types:
• a) probability or random sampling(used when population is known)
• b) non probability or non random sampling( when population is unknown)

9. PROBABILITY SAMPLING
• Sampling is based on theory of probability. It is also known as random sampling.
• Characteristics of random sampling:
• in probability sampling every element of the population has a chance of being selected
• Such chance is known as probability.
• For example if a sample frame is cal list of 100 students of specific course of study in a simple random sample
each student has 1/100 th chances of being selected.
• Probability sampling yields a representative sample and hence the findings of the sample survey are generalizable
to the population.
• The closeness of a sample the population can be determined by estimating sample bias or errors. Through
randomization the danger of unknown sampling bias can be minimised. Hence, probability sampling is preferable
to non probability sampling

10. TYPES OF PROBABILITY OR RANDOM SAMPLING
1. Simple random sampling
2. Stratified random sampling
3. Systematic sampling
4. Cluster sampling
5. Area family
6. Multistage and sub sampling
7. Random sampling with probability proportional to size
8. Double sampling and multiphase sampling
9. Replicated or interpenetrating sampling

11. 1. SIMPLE RANDOM SAMPLING
• This sampling technique gives each element an equal and independent chance of being selected.
• An equal chance means equal probability of selection e.g., in a population of 300 each element theoretically has 1/300th
chance of being selected.
• An independent chance means that the draw of one element will not affect the chances of other elements be selected
• Basic methods: a) lottery method b) table of random numbers c) a computer
• Suitability
• When the population is a homogeneous group
• Where the population is relatively small
• Complete list of all elements is available or can be prepared
• It is not suitable for drawing a sample from a larger heterogeneous population

12. 2.STRATIFIED RANDOM SAMPLING
• In this method the population is subdivided into homogeneous group or strata and from each stratum random sample
is drawn.
• Example: university students may be divided on the basis of discipline and each discipline group maybe again divided
into juniors and seniors
• Need for stratification: 1) increasing examples statistical efficiency. 2) providing adequate data for analysing the various
sub populations
• Suitability: for large heterogeneous population
• Stratification process
1. Stratification base should be decided
2. The number of strata
3. Strata sample size

13. TYPES OF STRATIFIED
SAMPLING
• proportionate stratified random sampling.
• This sampling involves a sample from each stratum in proportion to the latter's
share in the total population
• Disproportionate stratified random sampling
• This method does not give proportionate representation to strata. It necessarily
involve giving over representation to some strata and underrepresentation to
others.

14. 3.SYSTEMATIC SAMPLING OR FIXED INTERVAL
METHOD
• This method of sampling is an alternative to random sampling.it consists of taking every Kate
item in the population after a random start with an item from 1 to k.
• Example suppose it is desired to select a sample of 20 students from a list of 300 students
divide the population total of 300 by 20 the quotient is 15. Select a number at random
between 1 to 15, using lottery method for the table of random numbers. Suppose the
selected number is 9 then the students number 9, 24 (9+15),39(24+15).......
• As the interval between sample units is fixed this method is also known as fixed interval
method.

15. APPLICATIONS OF SYSTEMATIC SAMPLING
• Systematic selection can be e applied to various population such as students in a class
house in a street, telephone directory customers of a bank assembly line output in a
factory members of an association and so on

16. CLUSTER SAMPLING
• Cluster sampling means random selection of sampling units consisting of population elements. Each such
sampling unit is a cluster of population elements.Then from each selected sampling unit a sample of population
elements is drone by either simple random selection or stratified random selection.
• Features
1. What makes a desirable cluster depends on the survey's situation and resources.The individual elements
are determined by the survey of objectives.
2. the cluster may be an institution or a geographical area or any other appropriate group depending on the
nature of survey.
3. The number of elements in a cluster is called the cluster size
4. The clusters in most population are are of unequal size.e.g., dwelling in blogs persons in household
employee in section farmhouse old SIM village etc
5. Cluster of equal size are often the result of plant condition such as manufacturing e.g., matches in Matchbox
soap cakes in cases.

17. CLUSTER SAMPLING PROCESS
1. Identify clusters
2. Examine the nature of clusters
3. Determine the number of stages
• Single stage sampling
• Two stage sampling
• Multistage sampling

18. AREA SAMPLING
• This is an important form of cluster sampling.in large Field service clusters consisting of
specific geographical areas like districts, talukas villages or blogs in a city are commonly
drawn.s r critical girias are selected as sampling units in such cases their sampling is called
area sampling.
• It is not a separate method of sampling that forms part of cluster sampling.

19. MULTISTAGE SAMPLING
• In this method sampling is carried out in two or more stages. The population is regarded as being composed
of a number of first stage sampling units. Each of them is made up of the number of 2nd stage units and so
forth.
• That is, at which stage,a sampling unit is a cluster of sampling units of the subsequent stage.
• First, a sample of the first stage sampling units is drawn, then from which of the selected first stage sampling
unit a sample of the second stage sampling unit is a drawn.
• the procedure continuous down to the final sampling units for population elements. Appropriate random
sampling method is adopted at each stage.
• Usage
• Multistage sampling is appropriate where the population is scattered over a wider geographic area and no
frame are lists available for sampling.it is also useful when a survey has to be made within a limited time and
cost budget.

20. SUB- SAMPLING
• Subsampling is a part of a multistage sampling process. In multistage sampling the sampling in
second and the subsequent stage frames is called sub sampling.
• Suppose that from a population of 40000 household in 800 streets of a city we want to select a
sample of about 400 households.We can select a sample of 400 individual household or a sample
of 8 streets.The sample of 400 elements would be scattered over the city but the class 10
sample would be confined to 8 streets.
• Clustering reduces survey cast but increases the sampling error. Subsampling balances these two
conflicting effects of clustering.

21. Thank you