This document discusses research sampling methods. It defines key terms like population, sample, and sampling frame. It explains that sampling involves selecting a subset of a population to make inferences about the entire population. There are two main types of sampling: probability and non-probability. Probability sampling gives all population elements an equal chance of selection and includes simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling does not give equal selection chances and includes convenience sampling and purposive sampling like judgment and quota sampling. The document outlines features of a good sample and steps in sampling design to accurately represent a population with minimal error.

1. ResearchMethod
SAMPLING
CS. Dr. Akhil Ramteke

2. • Sampling: It is a process of selecting some of the element
(Individuals, objects or event) in a population which enables to
draw conclusion about entire population.
• Population: All Individuals of interest to the researcher in
other words it is the total collection of element about which
the researcher wish to make inference.
• Population Element/Unit of Analysis: The individual
participants or object on which the measurement is taken.
• Sampling Frame: it is a process of listing of all population
element from which sample will be drawn.
• Sample: A sample is a subset of Population which comprise
only some element of Population.
• Census: It is a count of All element in population.
Introduction

3. Need of Sample/ Reason for sampling
• Lower Cost
• Greater accuracy of result
• Greater speed of data collection
• Availability of population element

4. Features of Good Sample
• Representativeness
• Sample shall be representative of the population from which it
was selected in order to draw valid inferences about the
population
• Validity:
• Accuracy (An accurate sample or unbiased sample)
• Precision of estimates which is measure by standard error of
estimates

5. Steps in Sampling Design
• Determine and define the target population (Individuals,
Households, families etc)
• Develop the parameter of Interest/ Variable of interest in the
population (Population parameter and Sample statistics)
• Develop the sampling frame (Complete and correct list of
population members only)
• Choose the appropriate sampling method (Probability and
Non probability)
• Determine the sample size needed for the study

6. Sampling Vs. Non-Sampling Error
• Sampling Error: The sampling error occurs when the sample is
not representative or there is a difference between sample
mean and population mean.
• Techniques of reducing sampling error: Increase the sample size
• Non-Sampling Error: There are various reasons for Non-
sampling error such as:
• Wrong answer given by respondent
• Mistakes in converting data from questionnaire to spreadsheet
• Error at the time of Coding, tabulation and computation
• Population not properly defined
• Sampling frame error

7. Sampling Design
• It is a process of Selecting sample from Population
• Types: Probability and Non-Probability
• Probability Sampling Design:
• In a sample each and every element of population has an equal
chance of being selected.
• It is used in conclusive research
• Non-Probability Sampling Design:
• In a sample the element of population do not have equal chance
of being selected.
• it is used in exploratory research

8. TypesofProbabilitySamplingDesign
• Simple Random sampling
• Systematic sampling
• Stratified Random Sampling
• Cluster Sampling
TypesofProbabilitySamplingDesign
• Convenience sampling
• Purposive sampling: Judgment & Quota
• Snowball sampling

9. Simple Random sampling
• In a simple random sample each population element has an
equal chance of being selected into the sample.
• There is two sub-methods of Simple random sampling i.e.
Drawing sample with Replacement and without replacement

10. Systematic Sampling
• In this method the researcher select an element of the
population at the beginning and follows the skip interval.
• It is also called as mixed sampling as it involves Probability as
well as non probability sampling design.

11. Stratified Random Sampling
• The Population of Interest is divided into various strata and
simple random sample can be taken from within each stratum
(element from within subgroups)
• The criteria of grouping is homogeneity within subgroups and
heterogeneity between groups.
• Each stratum is homogeneous internally and heterogeneous
with other strata.
• Ex. College student can be divided into various class level that
is 1st Year, 2Nd Year, Final Year etc.
• Proportionate and Disproportionate sampling Techniques used
to allocate a total sample among various strata

12. Cluster Sampling
• The Population of Interest is divided into various subgroups
and randomly select several subgroups for in depth study.
• The criteria of grouping is Heterogeneity within subgroups and
homogeneity between groups.
• Ex. College student can be divided into various class level that
is 1st Year, 2Nd Year, Final Year etc.

13. Convenience Sampling
• In this type of sampling the researcher used to obtain
information as per the convenience of researcher.
• This techniques generally used in pre-testing purpose for
which the sampling unit is self - selected or because of easily
availability

14. Purposive Sampling
• The sample that conforms to certain criteria is a purposive
sampling
• Types
• A: Judgment- this method used when the required information
possessed by limited number of respondents
• B: Quota: it is used to select sample that conforms to certain
criteria or parameter of the study.

15. Snow Ball Sampling
• This type of sampling design used when respondents are
difficult to identify
• The respondents are best located through referral networks

16. Determination of Sample Size
• The size of sample depends upon basic characteristics such as
POPULATION, the type of INFORMATION required from
survey, COST involve etc.
• Steps in determining sample size:
• Population Size
• Margin of Error (Confidence Interval)
• Confidence level
• Standard Deviation

17. Thank You