This document discusses different concepts related to sampling, including population, sample, sampling process, and probability sampling methods. It defines population as the total group being studied, while sample is a subset selected to represent the population. Probability sampling methods like simple random sampling, stratified random sampling, cluster sampling, and systematic random sampling are covered. These methods aim to select samples in a way that gives all population members an equal chance of being selected to avoid bias. The document provides details on how to implement each sampling technique.

1. Sample and Sampling
methods
Part I

2. Population
Definition:
Population is the aggregation of all the units in which the
researcher is interested.
The term population refers to the aggregate or totality of all the
objects, subjects or members who possess similar characteristics.

3. Types of population
Target population:
The entire group of people who meet the criteria of the researcher
Eg. If the researcher needs to study the problems faced by nursing students in
India. In this the population refers to all the Nsg students in India
Accessible population
It is the subset of target population in which the researcher has accessible. This
can be an institution, area, city, state etc
Eg. In the above example the accessible population is students of crescent CON.

4. Sample
Definition :
It is defined as a representative unit of a target population , which is to
be worked upon by researchers during their study
Sample consists of subsets of units which comprise the population
selected by investigators or researchers to participate in their research
project

6. Sampling process
It is the process of selecting observations to provide an
adequate description and inferences of the population.
Sampling frame:
Listing of population from which a sample is chosen

7. Selection of sample
Homogeneous
Heterogeneous

10. Probability sampling methods
Random selection of the elements/members of the population
In this every subject in a population has equal chance to be selected as study
sample
Features:
Equal chances of being selected
Equal opportunity for selection
Absence of both systemic and sampling bias
Complete elimination of sampling bias

11. Simple Random Sampling
All subsets of the frame are given an equal probability.
Methods of selection:
Lottery method:
Use of table of random numbers
Use of computer

12. Sampling process:
● Identify and define the population
● Determine the desired sample size
● List all members of the population
● Assign all members on the list a consecutive number
● Select an arbitrary starting point from a table of random numbers and
read the appropriate number of digits

13. Advantages
● Minimum knowledge of
population
● Easy to analyse data
Disadvantages
● Low frequency of use
● Does not require researcher’s
expertise
● Larger risk of random error

14. Stratified random sampling
The population is divided into two or more groups called strata according to some
criterion such as geographic,location,grade,age..etc…

15. Selection process
● Identify and define the population
● Determine the desired sample size
● Identify each strata
● Put the entire population in each strata
● Select equal representatives from each strata

16. Advantages
● Assures representation of all
group in sample population
● Characteristics of all stratum
can be estimated
● Comparisons can be made
between each stratum
Disadvantages
● Requires accurate information
on proportion of each stratum
● Stratified lists expensive to
prepare

17. Cluster sampling method
Process of randomly selecting intact groups, not defined within the defined
population sharing similar characteristics
It can be
● Neighbourhood
● School
● Districts
● Classroom

18. Cluster sampling method

20. Selection process
● Identify and define the population
● Determine the desired sample size
● Identify logical cluster
● Make the whole population into different cluster
● Estimate average number per cluster
● Divide the sample size with number of cluster
● Randomly select the needed sample from each cluster

21. Advantages
● Can estimate the
characteristics of both
cluster and population
Disadvantages
● Expensive
● Each stage in cluster will
induce sampling error

22. Systematic random
sampling
Order all units in sampling
frame
Every nth number on the
order list will be taken for
study.
Eg. Every 3rd person in the
list is selected for study.

23. Selection process
● Identify and define the population
● Make sampling frame
● Determine the desired sample
● Decide on the nth number, in which you are going to select sample
● Select according to the fraction(nth number)
● The first number can be picked randomly

24. Advantages
● Simple method
● Easy to select
● Evenly spread over entire
population
● Cost effective
Disadvantages
● Bias may happen
● Each element will not get equal
chance
● Ignorance of all element
between the nth number

25. Multistage sampling
This method is carried out in different stages
Population is divided into multiple clusters and then these clusters are further
divided and grouped into various sub groups (strata) based on similarity.
One or more clusters can be randomly selected from each stratum. This process
continues until the cluster can’t be divided anymore.
For example country can be divided into states, cities, urban and rural and all the
areas with similar characteristics can be merged together to form a strata.

27. Advantages
● More accurate
● More effective
Disadvantages
● Costly
● Each stage introduce
sampling error

28. Summary
● Population
● Sample
● Sampling process
● Sampling type
● Probability sampling
method
● Features of probability
sampling
● Types of probability
sampling

29. Thank you