SAMPLING SAMPLING IS THE PROCESS OF SELECTING A SMALL NUMBER OF ELEMNTS FROM A LARGER DEFINED TARGET GROUP OF ELEMNTS SUCH THAT THE INFORMATION GATHERDED FROM THE SMALL GROUP WILL ALLOW JUDEN=MENT TO BE MADE ABOUT THE LARGER GROUPS. IN SIMPLE WORDS A PROCEDURE BY WHICH SOME MEMBERS OF A GIVEN POPULATION ARE SELECTED AS REPRESENTATION OF THE ENTIRE POPULATION . PURPOSE OF SAMPLING To gather data about the population in order to make an inference that can be generalized to the populations. . PROBABILITY SAMPLING Probability sampling is a type of sampling where each member of the population has a known probability of being selected in the sample . In probability sampling some elements of randomness is involved in selection of units ,so that personal judgement or bias is not there. NON- PROBABILITY SAMPLING Non- Probability sampling is a type of sampling where each member of the population does not have known probability of being selected in the sample. In this each member of the population does not get equal chance of being selected in the sample. This sampling methods is adopted when each member of the population can not be selected or the researcher deliberately wants to choose member selectively

1. MS. POOJA SEN
MSC NURSING ( MHN)

3. SAMPLING
SAMPLING IS THE PROCESS OF SELECTING A SMALL NUMBER OF
ELEMNTS FROM A LARGER DEFINED TARGET GROUP OF ELEMNTS
SUCH THAT THE INFORMATION GATHERDED FROM THE SMALL GROUP
WILL ALLOW JUDEN=MENT TO BE MADE ABOUT THE LARGER GROUPS.
IN SIMPLE WORDS A PROCEDURE BY WHICH SOME MEMBERS OF A
GIVEN POPULATION ARE SELECTED AS REPRESENTATION OF THE
ENTIRE POPULATION .

4. PURPOSE OF SAMPLING
To gather data about the population in order to make an inference that can be
generalized to the populations. .

7. PROBABILITY SAMPLING
Probability sampling is a type of sampling where each member of the
population has a known probability of being selected in the sample .
In probability sampling some elements of randomness is involved in selection
of units ,so that personal judgement or bias is not there.

8. PROBABILITY
SAMPLING
Simple
Sampling
Stratified
Sampling
Cluster &
Multistage Sampling
Systematic random
Sampling
Sequential Sampling
Starting Se Sim ka System Close Hai

9. NON- PROBABILITY SAMPLING
Non- Probability sampling is a type of sampling where each member of the
population does not have known probability of being selected in the sample.
In this each member of the population does not get equal chance of being
selected in the sample.
This sampling methods is adopted when each member of the population can
not be selected or the researcher deliberately wants to choose member
selectively.

10. NON PROBABILITY
SAMPLING
Convenient
Sampling
Purposive
Sampling
Consecutive Sampling
Quota
Sampling
Snow Ball Sampling
Par Suno Convence Q or Con Kare

12. 1. SIMPLE RANDOM SAMPLING
It is the simplest form of Probability sampling. In this method each
individual/subject/unit is chosen randomly. In this type of sampling design
every population of member has a similar chance of being picked as the
subject.
There is need of 2 homogeneous & researcher must have a list of the
elements/members of the accessible population.
The sample drawn from sampling frame by using following methods.

14. 1. SIMPLE RANDOM SAMPLING
 Lottery method – it is the oldest method of simple random sampling . In
this all items in the sampling frame are numbered on separate slips of
paper of identical shape and size, these slip are folded & mixed up in
bowl. A blindfold selection is then made of the number of slips required to
constitute the desired sample size.
Example – If we want to take a sample of 10 person out of population 100 persons on separate slip of
paper, fold this slips, mix them thoroughly and then make a blindfold selection of 10 slips .

16. 1. SIMPLE RANDOM SAMPLING
 Random Number Table method – This is most commonly and accurately
used method in simple random sampling , researcher initially prepare a
number list of elements and with the blind fold choose the number of the
random table .the same procedure is continued until desire number of
sample not achieved.
Example – The names of 25 employees being chosen out of a hat from a company of 250 employees.

18. 1. SIMPLE RANDOM SAMPLING
 Use of Computer– Now days random tables may be generated from the
computer and subjects may be selected as described in the use of
random tables .
Example – A researcher intends to collect a systematic sample of 5000 people in a population of
50000.

20. 1. SIMPLE RANDOM SAMPLING
 MERITS -
• Easiest way to doing sampling .
• Fair way of selecting a sample from a given population since every
member is given equal opportunity to being selected.
• Easy to understand and implement.
• Required less knowledge regarding population.
• It is most Unbiased method for sample selection.

21. 1. SIMPLE RANDOM SAMPLING
 DEMERITS -
• Do not consider heterogeneous population.
• Difficult to achieve a good sample.
• Not suitable for all type of researches.
• Prone to sampling error because the selection is random.
• Expensive and Time consuming methods .

23. 2. Stratified RANDOM SAMPLING
Stratified sampling is a type of sampling method in which the total population is
divided into homogeneous smaller groups or strata to complete the sampling
process. The strata is formed based on some common characteristics in the
population data like age ,gender, religion, socioeconomic status, diagnosis,
education, type of Care, type of Nursing area ,type of nurse .

24. 2. Stratified RANDOM SAMPLING
 Proportional Stratified random sampling– The size Of the sample selected
from each subgroup is proportional to the size of that subgroup in the
entire population.
Example – Researchers has 3 strata containing 100,200 and 300 population sizes ,the researcher
select the sampling fraction is ½ then the researcher should randomly sample 50,100& 150 subject
from each stratum.

26. 2. Stratified RANDOM SAMPLING
 Disproportional Stratified random sampling– The size of the same
selected from each subgroup is disproportional to the size if that subgroup
in the population.
Example – Researchers wants to study biophysical profile of nursing students in a college of
nursing, which contains 100 students from the Himachal,200 students from Haryana and 300 students
from Panjab .the researcher choose different sampling fraction and then randomly select of 50 subject
from each stratum.

28. Stratified Sampling: Formula
we use this formula to find the sample size of each
subgroup involving one or more strata:

29. Stratified sampling example: You’re interviewing a school to understand the type of
food that the students like. This school has both boys and girls, and you want to take
their thoughts into account with a sample size of 100 students.
Here are the numbers:
•Total Students: 2,000
•No. of boys: 800
•No. of girls: 1,200
So, using the stratified sampling formula;
•Total girls in the final sample: (1,200 / 2,000) * 100 = 60
•Total boys in the final sample: (800 / 2,000) * 100 = 40
Hence, using this formula, you get the size of each sub-group in proportion to the size
of each sub-group (girls and boys) where the stratum is gender.

30. 2. Stratified RANDOM SAMPLING
 MARITS
• It provides more accuracy than simple random sampling
• It ensures representation of all groups in a population.
• Efficiency in survey execution.
• Enable to use a different or other method in strata.
• Comparison possible in two groups.

31. 2. Stratified RANDOM SAMPLING
 DEMARITS
• Need to have information of whole of the population.
• Time consuming and costly method as it has several steps in drawing the
sample.
• Needs proper focus.
• Proper knowledge about population required.
• Large population is required.

33. 3. Systematic RANDOM SAMPLING
• It is a method of choosing a random sample from a larger population. As the
name Signifies ,it involves arranging the study population According to some
ordering scheme. The subject are randomly selected from the population at a
fixed interval that is predetermined by the researcher. This fixed interval
called the sampling interval.
Sampling interval(K) = Number of subjects in target population(N)
Size of sample (n)

35. 3. Systematic RANDOM SAMPLING
• Example – Target Population Is 800 & Sample size is 50
K = 800
50
= 16
Randomly Selected First Sample suppose we select Number 6
6+ 16 = 22 - 2nd Sample
22+16=38 - 3rd Sample
38+16 = 44 - 4th Sample

36. 3. SYSTEMATIC RANDOM SAMPLING
 MARITS
• The method is simple to use .
• It reduce the potential of personal bias in selecting the sample.
• It is cost effective & Save time .
• It is easy to understand.
• Easy to mange large Population.

37. 3. Systematic RANDOM SAMPLING
 DEMARITS
• Not all member have the equal chance to selection ,only the initial
respondent is selected on random basis.
• It is not possible to select the required sample size if the population is too
small.
• The researcher needs to find out the sampling interval before proceeding
further.
• Sampling may be biased if ordering of population in the beginning is not
random.

39. 4. Cluster or Multistage SAMPLING
• In this type of sampling population is divided into small recognizable group
which is called cluster .
• Once the population is divided into Clusters ,then clusters are selected on
random basis, and all the members of that cluster are included in the sample.
• Clusters should be small .
• No. Of units in each cluster should be approximately same.

41. Example – A researcher looking to understand smartphone usage in Germany. In this
case, the cities of Germany will form clusters and each cluster we select the sample .

43. 4. Cluster or Multistage SAMPLING
 One stage cluster / Single Stage Cluster Sampling:- The researcher
performs sampling just once. It is a technique of sampling wherein each
element of the chosen cluster is observed for testing purposes. Such a
technique is not preferred when the population is large, and the clusters
are too big to be included fully.

45. 4. Cluster or Multistage SAMPLING
 Two-Stage Cluster Sampling:- Rather than selecting all the members of a
cluster, only a few members are picked from each group. In short, the
researcher will gather data from a random subsample of individual units
within each selected cluster. The researcher does so by implementing
simple random sampling or systematic sampling to narrow down the
desired sample.

47. 4. Cluster or Multistage SAMPLING
 Multi Stage Cluster Sampling:- Multiple Stage Cluster Sampling involves a
few more steps than two-stage cluster sampling. Effective research is
conducted when complicated clusters are formed. So, the researcher
performs the process of random sampling units within the groups again
and again until he arrives at a manageable sample.

49. 4. Cluster or Multistage SAMPLING
 MARITS
• The method cheap, quick and Easy for a large population .
• Large population can be studied and require only list of the members.
• Same cluster can be used again for study.
• Suitable for survey of institutions.

50. 4. Cluster or Multistage SAMPLING
 DEMARITS
• Least representative of the population.
• High probability of sampling error.
• Small homogeneous population is under study, this technique Is not at all
useful.

52. 5. Sequential SAMPLING
• This method of sample selection is slightly different from method .here
sample size is not fixed ,the investigators initially selects small sample and
tries out to make inference if not able draw results, he or she then adds
more subjects until clear cut inference can draw .

54. 5. Sequential SAMPLING
 MARITS
• Facilitates to conduct a study on best possible smallest representative
sample.
• Helping in ultimately finding the inference of the study.

55. 5. Sequential SAMPLING
 DEMARITS
• With this sampling technique it is not possible to study a phenomenon
which needs to be studied at one point of a time.
• Require repeated entries into the field to collect the sample.

56. NON PROBABILITY
SAMPLING

57. • 1. PURPOSIVE SAMPLING
• Purposive sampling more commonly called as a Judgemental
sampling or Authoritative sampling and subjective sampling.
• In this sampling technique are chosen by choice not by chance