Research methodlogy unit-iii-sampling

List of Topics
Unit-III
Sampling Introduction
Types of Probability Sampling
Types of Non-Probability Sampling
Sampling Frame
Determination of Sample Size

Sampling-Introduction
All items in any field of inquiry constitute a ‘Universe’ or ‘Population.’ A complete enumeration of all items in the
‘population’ is known as a census inquiry. It can be presumed that in such an inquiry, when all items are covered, no
element of chance is left and highest accuracy is obtained.
In practical life, considerations of time and cost almost invariably lead to a selection of respondents i.e., selection of only
a few items.The respondents selected should be as representative of the total population as possible in order to
produce a miniature cross-section.
The selected respondents constitute what is technically called a ‘sample’ and the selection process is called ‘sampling
technique.’The survey so conducted is known as ‘sample survey’.
A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the
procedure the researcher would adopt in selecting items for the sample.
T.MANOJ KUMAR RESEARCH METHODOLOGY
A S S T P R O F E S S O R
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SAMPLE DESIGN
Steps in Sample Design
(i) Population
(ii) Sampling Frame:
(iii) Sampling Unit
(iv) Size of sample
(v) SamplingTechnique
(vi) Draw or selecting a sample
(vi) Budgetary constraint
(vii) Sampling procedure
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STEPS IN SAMPLE DESIGN
1. Defining theTarget Population:
Defining the population of interest, for business research, is the first step in sampling process. In
general, target population is defined in terms of element, sampling unit, extent, and time frame.
The definition should be in line with the objectives of the research study.
A well defined population reduces the probability of including the respondents who do not fit
the research objective of the company. For ex, if the population is defined as all women above the
age of 20, the researcher may end up taking the opinions of a large number of women who
cannot afford to buy a micro oven
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2. Specifying the Sampling Frame:
• Once the definition of the population is clear a researcher should decide on the sampling frame.A
sampling frame is the list of elements from which the sample may be drawn. ex, an ideal sampling
frame would be a database that contains all the households that have a monthly income above
Rs.20,000.. In general, researchers use easily available sampling frames like telephone directories and
lists of credit card and mobile phone users.Whatever may be the case, an ideal sampling frame is
one that entire population and lists the names of its elements only once.
• A sampling frame error pops up when the sampling frame does not accurately represent the total
population or when some elements of the population are missing another drawback in the sampling
frame is over —representation
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3. Specifying the Sampling Unit:
A sampling unit is a basic unit that contains a single element or a group of elements of the
population to be sampled.
4. Selection of the Sampling Method
The sampling method outlines the way in which the sample units are to be selected.The choice
of the sampling method is influenced by the objectives of the business research, availability of
financial resources, time constraints, and the nature of the problem to be investigated.All
sampling methods can be grouped under two distinct heads, that is, probability and non-
probability sampling
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5. Determination of Sample Size:
The sample size plays a crucial role in the sampling process. In the case of probability sampling, however,
formulas are used to calculate the sample size after the levels of acceptable error and level of
confidence are specified. In non-probability sampling procedures, the allocation of budget, thumb rules
and number of sub groups to be analyzed, importance of the decision, number of variables, nature of
analysis, incidence rates, and completion rates play a major role in sample size determination
6. Specifying the Sampling Plan:
In this step, the specifications and decisions regarding the implementation of the research process are
outlined. Suppose, blocks in a city are the sampling units and the households are the sampling elements.
This step outlines the modus operandi of the sampling plan in identifying houses based on specified
characteristics. It includes issues like how is the interviewer going to take a systematic sample of the
houses.What should the interviewer do when a house is vacant? What is the recontact procedure for
respondents who were unavailable? All these and many other questions need to be answered for the
smooth functioning of the research process.These are guide lines that would help the researcher in
every step of the process.
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7. Selecting the Sample:
• This is the final step in the sampling process, where the actual selection of the sample elements
is carried out.At this stage, it is necessary that the interviewers stick to the rules outlined for
the smooth implementation of the business research.This step involves implementing the
sampling plan to select the sampling plan to select a sample required for the survey.
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TYPES OF SAMPLING
i) Probability sampling ii) Non Probability sampling
A probability sampling is one in which every unit in the population has a chance (greater
than zero) of being selected in the sample, and this probability can be accurately determined.The
combination of these traits makes it possible to produce unbiased estimates of population totals, by
weighting sampled units according to their probability of selection.
Non-probability sampling is defined as a sampling technique in which the researcher
selects samples based on the subjective judgment of the researcher rather than random selection
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PROBABILITY SAMPLING-TYPES
A probability sampling is one in which every unit in the population has a chance (greater than zero) of
being selected in the sample, and this probability can be accurately determined.The combination of these
traits makes it possible to produce unbiased estimates of population totals, by weighting sampled units
according to their probability of selection.
(i) Systematic Sampling
(ii) Stratified Sampling
(iii) Cluster Sampling
(iv) Area Sampling
(v) Multi-Stage Sampling
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PROBABILITY SAMPLING
1. Simple random sampling
In a simple random sample, every member of the population has an equal chance of being selected.Your
sampling frame should include the whole population.
Example
You want to select a simple random sample of 100 employees of Company X.You assign a number to
every employee in the company database from 1 to 1000, and use a random number generator to select
100 numbers.
2. Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct.
Every member of the population is listed with a number, but instead of randomly generating numbers,
individuals are chosen at regular intervals.
Example
All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly
select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6,
16, 26, 36, and so on), and you end up with a sample of 100 people.

PROBABILITY SAMPLING
3. Stratified sampling
Stratified sampling involves dividing the population into subpopulations that may differ in important ways.
It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in
the sample.
To use this sampling method, you divide the population into subgroups (called strata) based on the
relevant characteristic (e.g. gender, age range, income bracket, job role).
4. Cluster sampling
Cluster sampling also involves dividing the population into subgroups, but each subgroup should have
similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you
randomly select entire subgroups.
Example
The company has offices in 10 cities across the country (all with roughly the same number of employees
in similar roles).You don’t have the capacity to travel to every office to collect your data, so you use
random sampling to select 3 offices – these are your clusters.

TYPES OF NON-PROBABILITY SAMPLING
Non-probability sampling is defined as a sampling technique in which the researcher
selects samples based on the subjective judgment of the researcher rather than random selection
1. Convenience Sampling
2. Judgment Sampling or purposive
3. Quota Sampling
4. Snowball Sampling
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1. Convenience sampling
A convenience sample simply includes the individuals who happen to be most accessible to the
researcher.This is an easy and inexpensive way to gather initial data, but there is no way to tell if
the sample is representative of the population, so it can’t produce generalizable results.
Example
You are researching opinions about student support services in your university, so after each of
your classes, you ask your fellow students to complete a survey on the topic.This is a convenient
way to gather data, but as you only surveyed students taking the same classes as you at the same
level, the sample is not representative of all the students at your university.

2.Voluntary response sampling
Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher
choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online
survey).Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to
volunteer than others.
3. Purposive sampling or Judgmental Sampling
This type of sampling involves the researcher using their judgement to select a sample that is most useful to the purposes of
the research.
It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon
rather than make statistical inferences.An effective purposive sample must have clear criteria and rationale for inclusion.
Example
You want to know more about the opinions and experiences of disabled students at your university, so you purposefully select
a number of students with different support needs in order to gather a varied range of data on their experiences with student
services.

4. Snowball sampling
If the population is hard to access, snowball sampling can be used to recruit participants via other participants.The number of
people you have access to “snowballs” as you get in contact with more people.
Example
You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city,
probability sampling isn’t possible.You meet one person who agrees to participate in the research, and she puts you in
contact with other homeless people that she knows in the area.
5.Quota Sampling Definition
Quota sampling is an important sampling method which involves a non-probability sampling technique in which sampling is
not based upon the probability of appearance. In such a process, the researcher decides the selection of sampling based on
some quota. In quota sampling, the researcher makes sure that the final sample must meet his quota criteria.
“The sample obtained from a quota sampling method contains similar proportions of observations as the whole population
with some known traits or characteristics. In quota sampling, the researcher selects from his/her judgement or some fixed
quota. In other words, the sample observations are to be chosen based on some pre-specified virtues.Then the total sample
contains the same distribution of characteristics that were assumed to be found in the population of concern”.
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SAMPLING ERRORS
The errors which arise due to the use of sampling survey are known as sampling errors.These are random
variation in the sample estimate around the true population parameters.
Type of sampling errors
Biased errors:These errors are occurring due to the faulty selection of sampling method due to the
prejudice of the researchers.
Unbiased errors:This type of bias is occurring due to chance difference between the items
included in the sample
Bias may arise due to,
1. Faulty process selection.
2. Faulty work during the collection of information.
3. Faulty method of analysis.
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SAMPLING ERRORS
Non-Sampling Error
Non-sampling errors are other errors which can impact the final survey estimates, caused by
problems in data collection, processing, or sample design.They include:
1. Over coverage: Inclusion of data from outside of the population.
2. Under coverage: Sampling frame does not include elements in the population.
3. Measurement error: e.g. when respondents misunderstand a question, or find it difficult to
answer.
4. Processing error: Mistakes in data coding.
5. Non-response: Failure to obtain complete data from all selected individuals.
After sampling, a review should be held of the exact process followed in sampling, rather than
that intended, in order to study any effects that any divergences might have on subsequent analysis.
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CHARACTERISTICS OF GOOD SAMPLE DESIGN
a. Representative.
b.Viable.
c.The selected sample design should not cause more errors.
d.A good sample design able to control systematic bias efficiently.
e. If the sample is well design and selected, decision makers can use this info with
Confidence

SAMPLE SIZE
Sample Size
Sample size is the number of items to be selected from the universe. It should be optimum formulas, tables,
and power function charts are well known approaches to determine sample size.
Steps in calculating sample size
STEP 1: DEFINEYOUR OBJECTIVE
STEP 2: DETERMINEYOUR DEPENDENTVARIABLE
STEP 3: DECIDE ONYOUR MARGIN OF ERROR
STEP 4: DECIDE ONYOUR SIGNIFICANCE LEVEL
STEP 5: DECIDE ON THE NECESSARY LEVEL OF POWER
STEP 6: ESTIMATE THE CURRENT AND EXPECTED LEVEL OF KEY INDICATORS
STEP 7: ESTIMATEYOUR RESPONSE AND ATTRITION RATES
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Research methodlogy unit-iii-sampling

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