This document provides an overview of sampling concepts, including definitions of population, sample characteristics, and methods of sampling, such as probability and non-probability techniques. It discusses the importance of having a good sampling frame, how to determine sample size, and ways to reduce sampling errors, along with practical examples. Additionally, it outlines different sampling methods, including simple random, systematic, stratified, area, cluster, judgment, convenience, purposive, quota, and snowball sampling.

1. UNIT IV- SAMPLING: BASIC
CONCEPTS
DR. PRACHI MURKUTE

2. CONTENTS OF THE UNIT
• Sampling: Basic Concepts: Defining the Universe, Concepts of Statistical Population, Sample,
Characteristics of a good sample. Sampling Frame, determining the sample frame, Sampling
errors, Non-Sampling errors, Methods to reduce the errors, Sample Size constraints, Non-
Response.
• Probability Sample: Simple Random Sample, Systematic Sample, Stratified Random Sample,
Area Sampling & Cluster Sampling.
• Non-Probability Sample: Judgment Sampling, Convenience Sampling, Purposive Sampling,
Quota Sampling & Snowballing Sampling methods.
• Determining size of the sample: Practical considerations in sampling and sample size,
(sample size determination formulae and numerical not expected)
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3. SAMPLING: BASIC CONCEPTS
Defining the Universe –
The population or universe represents the entire group of units which is the focus of the
study.
Thus, the population could consist of all the persons in
the country,
geographical location,
Special ethnic or economic group,
depending on the purpose and coverage of the study.
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4. CONCEPTS OF STATISTICAL POPULATION
• In statistics, a population is a representative sample of a larger group of
people (or even things) with one or more characteristics in common.
(https://theintactone.com/2019/03/04/brm-u4-topic-2-concept-of-statistical-
population/)
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5. SAMPLE
• Sampling means selecting the group
that you will collect data from in your
research.
• For example, if you are researching
the opinions of students in your
university, you could survey a sample
of 100 students. In statistics, sampling
allows you to test a hypothesis about
the characteristics of a population.
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6. CHARACTERISTICS OF A GOOD SAMPLE
• A sample should have a clear goal.
• A good sample should be an accurate representation of the entire universe or population.
• A good sample is free from bias.
• A sample should be chosen randomly.
• The adequacy of a sample is essential.
• A sample should be proportional.
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7. SAMPLING FRAME
• A sampling frame is a researcher's
list or device to specify the
population of interest. It's a group of
components that a researcher can use
to select a sample from the population.
Limited resources and accessibility
might prohibit researchers from
collecting data from all target
population segments.
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8. DETERMINING THE SAMPLE FRAME
• Five steps to finding your sample size
1.Define population size or number of people.
2.Designate your margin of error.
3.Determine your confidence level.
4.Predict expected variance.
5.Finalize your sample size.
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9. SAMPLING ERRORS,
• Sampling error is the difference between a
population parameter and a sample
statistic used to estimate it.
• For example, the difference between a
population mean and a sample mean is
sampling error. Sampling error occurs
because a portion, and not the entire
population, is surveyed
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10. NON-SAMPLING ERRORS,
• Non-sampling error refers to all sources of error that are unrelated to
sampling. Non-sampling errors are present in all types of survey, including
censuses and administrative data.
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11. METHODS TO REDUCE THE ERRORS,
• The prevalence of sampling errors can be reduced by increasing the sample
size. As the sample size increases, the sample gets closer to the actual
population, which decreases the potential for deviations from the actual
population
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12. SAMPLE SIZE
CONSTRAINTS,
• In the formula, the sample size is
directly proportional to Z-score and
inversely proportional to the margin of
error. Consequently, reducing the sample
size reduces the confidence level of the
study, which is related to the Z-score.
Decreasing the sample size also
increases the margin of error.
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Z = standard score
X = observed value
µ =
mean of the
sample
=
standard deviation
of the sample

13. NON-
RESPONSE
• Non-response is a form of
non-observation present in
most surveys, which
means failure to obtain a
measurement on one or
more study Variables for
one or more elements
selected for the survey.
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14. PROBABILITY
SAMPLE:
• Simple Random Sample :-
Simple random sampling is a type of
probability sampling in which the
researcher randomly selects a subset
of participants from a population. Each
member of the population has an
equal chance of being selected.
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15. SYSTEMATIC SAMPLE
• Systematic sampling is a type of
probability sampling method in
which sample members from a
larger population are selected
according to a random starting
point but with a fixed, periodic
interval.
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16. STRATIFIED RANDOM SAMPLE,
• Stratified random sampling is a method of sampling that involves the division of a
population into smaller subgroups known as strata. In stratified random sampling, or
stratification, the strata are formed based on members' shared attributes or
characteristics, such as income or educational attainment.
• For example, one might divide a sample of adults into subgroups by age, like 18–29,
30–39, 40–49, 50–59, and 60 and above.
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17. AREA SAMPLING & CLUSTER SAMPLING
• In cluster sampling, researchers divide a population into smaller groups
known as clusters. They then randomly select among these clusters to form
a sample.
• Cluster sampling is a method of probability sampling that is often used to study
large populations, particularly those that are widely geographically dispersed.
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18. NON-PROBABILITY SAMPLE: JUDGMENT
SAMPLING,
• Judgment Sampling- Judgment sampling, also referred to as judgmental
sampling or authoritative sampling, is a non-probability sampling technique
where the researcher selects units to be sampled based on his own existing
knowledge, or his professional judgment
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19. CONVENIENCE SAMPLING,
• Convenience sampling is a non-probability sampling method where units are
selected for inclusion in the sample because they are the easiest for the
researcher to access. This can be due to geographical proximity, availability at a
given time, or willingness to participate in the research.
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20. PURPOSIVE
SAMPLING,
• Purposive sampling refers to a group of non-probability sampling techniques in which
units are selected because they have characteristics that you need in your sample. In
other words, units are selected “on purpose” in purposive sampling.
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21. QUOTA SAMPLING
• Quota sampling is a non-probability sampling
method that relies on the non-random selection
of a predetermined number or proportion of
units. This is called a quota. You first divide the
population into mutually exclusive subgroups (called
strata) and then recruit sample units until you reach
your quota.
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22. SNOWBALLING SAMPLING METHODS.
• Snowball sampling is a non-probability
sampling method where new units are
recruited by other units to form part of
the sample. Snowball sampling can be a
useful way to conduct research about
people with specific traits who might
otherwise be difficult to identify
• For example, a researcher who is
seeking to study leadership patterns
could ask individuals to name others
in their community who are influential.
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23. DETERMINING SIZE OF THE SAMPLE:
In general, three or four factors must be known or estimated to calculate sample
size:
(1) the effect size (usually the difference between 2 groups);
(2) the population standard deviation (for continuous data);
(3) the desired power of the experiment to detect the postulated effect; and
(4) the significance level.
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24. FURTHER READING
• https://www.questionpro.com/blog/snowball-sampling/
• https://conjointly.com/kb/nonprobability-sampling/
• https://conjointly.com/kb/sampling-statistical-terms/
• https://conjointly.com/kb/probability-sampling/
• https://conjointly.com/kb/sampling-in-research/
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