Sampling techniques

1. SAMPLING
TECHNIQUES IN
EPIDEMIOLOGIC
AL STUDIES
BY: DR ASHISH PALIWAL (PG- 𝐼𝑠𝑡
Year)
MODERATOR : DR SRISTHI
KUKREJA
(ASSISTANT
PROFESSOR)
DEPARTMENT OF COMMUNITY

2. CONTENTS
1) Introduction
2) Need for sampling
3) Sampling Process
4) Essentials of Sampling
5) Methods of Sampling
 Non Probability Sampling
 Probability Sampling
References

3. INTRODUCTION
 Population/Universe: in statistics denotes the aggregate from which sample (items) is to
be taken.
 A population can be defined as including all people or items with the characteristic one
wishes to understand.
 Because there is very rarely enough time or money to gather information from everyone
or everything in a population, the goal becomes finding a representative sample (or
subset) of that population.

4. INTRODUCTION
 Sampling frame is the list from which the potential respondents are drawn .
 A sample is “a smaller (but hopefully representative) collection of units from a
population used to determine truths about that population”

5. SAMPLING
Sampling: the process of learning about population on the basis of sample drawn from it.
Three elements in process of sampling:
1. Selecting the sample
2. Collecting the information
3. Making inference about population
Statistics: values obtained from study of a sample.
Parameters: such values from study of population

6. METHODS
 Whole Population: Census method or complete
enumeration method
 Few Individuals :Sample method

7. CENSUS (Complete Enumeration
Survey)
Merits:
Data obtained from each and every unit of population.
Results: more representative, accurate, reliable. Basis of various surveys.
Demerits:
More effort ,money , time.
Big problem in underdeveloped countries.

8. ADVANTAGES OF SAMPLING
 Less resources (time, money)
 Less workload.
 Gives results with known accuracy that can be calculated mathematically.

9. SAMPLING PROCESS
 Defining the population of concern.
 Specifying a sampling frame, a set of items or events possible to measure.
 Specifying a sampling method for selecting items or events from the frame.
 Determining the sample size.
 Implementing the sampling plan.
 Sampling and data collection

10. NON PROBABILITY SAMPLING
Principal:
1. Selection of sampling units does not depend on probability.
2. Non-random in nature.

11. TYPES OF NON PROBABILITY
SAMPLING
1. Judgmental /Purposive Sampling
2. Convenience Sampling
3. Self selection Sampling
4. Snowball Sampling
5. Criterion Sampling
6. Quota Sampling

12. JUDGMENT /PURPOSIVE SAMPLING
Judgment/Purposive/Deliberate sampling.
Depends exclusively on the judgment of investigator.
Sample selected which investigator thinks to be most typical of the universe.
Merits:
Small no. of sampling units
Study unknown traits/case sampling
Urgent public policy & business decisions
Demerits:
Personal prejudice & bias
No objective way of evaluating reliability of results

14. CONVENIENCE SAMPLING
 Convenient sample units selected.
 Selected neither by probability nor by judgment.
Merit – useful in pilot studies.
Demerit – results usually biased and unsatisfactory.

16. SELF SELECTION SAMPLING
 Participants take part in the research on their own as volunteers.
 Commonly seen in online surveys.

17. SNOWBALL SAMPLING
 A special non probability method used when the desired sample characteristic is rare.
 It may be extremely difficult or cost prohibitive to locate respondents in these situations.
 Snowball sampling relies on referrals from initial subjects to generate additional
subjects.
 STEPS:
1. Make contact with one or two cases in the population.
2. Ask these cases to identify further cases.
3. Ask these new cases to identify further new cases.
4. Stop when either no new cases are given or the sample is as large as is manageable.

18. SNOWBALL SAMPLING
 Merit :
Access to difficult to reach populations (other methods may not yield any results).
 Demerit :
Not representative of the population and will result in a biased sample as it is self-
selecting.

19. QUOTA SAMPLING
 Researchers are given quotas to fill from different strata of population.
 Keeping the proportions of quota same as observed in population.
 EXAMPLE :
A village has a population where 60% are Hindus and 40% Muslims.
So, the proportion is 6:4
Now, when selecting participants of investigators choice , the investigator has to select
sample based on 6:4 proportion.

20. PROBABILITY SAMPLING
 Obeys law of probability.
 Each sampling unit has a known: non-zero probability of being selected.
 Superior to non-probability sampling technique.
 TYPES:
1. Simple random sampling.
2. Systematic random sampling.
3. Stratified random sampling.
4. Cluster sampling.
5. Multistage sampling.
6. Multiphase sampling.
7. Sequential sampling.

21. SIMPLE RANDOM SAMPLING
 Most commonly used, simplest of sampling methods.
 Each unit has an equal opportunity of being selected.
 Chance determines which items shall be included.
 Applicable only when population is Small, homogenous and readily available.
 Complete population list must be available to build sampling frame.
 Methods:
1. Random number table.
2. Lottery method
3. Computer generated random number.

22.  Merits
1. No personal bias.
2. Sample more representative of population.
3. Accuracy can be assessed as sampling errors follow principals of chance.
 Demerits
1. Requires completely catalogued universe.
2. Cases too widely dispersed - more time and cost.

23. LOTTERY METHOD
 Assign population with numbers.
Numbers are written in piece of paper and placed in a bowl/container.
Thoroughly mixed/ shuffled.
Researcher picks up card/ piece of paper.
Population members having the number drawn are selected.
Repeat until sample size is reached.

24. SIMPLE RANDOM SAMPLING TYPES:
 With replacement:
Probability each item: 1/N
 Without replacement:
Probability 1st draw: 1/N
Probability 2nd draw: 1/N-1

26. COMPUTER GENERATED RANDOM
NUMBER
 Better used in large population.
 A program assigns number and randomly selects the sample.
Pseudo random in nature as the program is itself run on a underlying formula/algorithm.
Thus certain underlying set of instructions are always present.

27. SYSTEMATIC RANDOM SAMPLING
Done in Large, scattered and heterogenous population.
Method:
1. A random starting point is chosen
2. Remainder of the sample is selected by taking every nth unit.
n= Sample interval Sample interval = Total population/desired sample
size
Example:
A sample of 50 is to be taken of 500 population.
Then n=500/50 i.e. 10.
One random no. is selected ex. 5.
Then every 10th sample is selected following 5, i.e. 15,25,35,45…

28. Merits
 Simple and convenient.
 Less time consuming.
Demerits
 Population with hidden periodicities.

29. STRATIFIED RANDOM SAMPLING
 Done in heterogenous population.
 To assess the distribution of particular variable.
Method:
1. Entire heterogenous population is divided into small homogenous groups called
strata.
2. Then from each strata, required no. of study subjects are selected by S.R.S.
3. The strata should be mutually exclusive.
4. The strata should be based on some known characteristics ex. Religion, occupation
etc.
5. Then, sampling frame of population in each strata is prepared.
6. Finally , samples within each strata is done by probability sampling.

31. Merits
 More representative.
 Greater accuracy.
 Greater geographical concentration.
Demerits
 Utmost care in dividing strata.
 Skilled sampling supervisors.
 Cost per observation may be high.

32. CLUSTER SAMPLING
 A sampling technique in which the entire population of interest is divided into groups, or
clusters, and a random sample of these clusters is selected.
 Each cluster must be mutually exclusive and together the clusters must include the
entire population .
 After clusters are selected, then all units within the clusters are selected.
 No units from non-selected clusters are included in the sample.
In cluster sampling, the clusters are the primary sampling unit
(PSU’s) and the units within the clusters are the secondary sampling
units (SSU’s)

33. CLUSTER SAMPLING- STEPS
 Identification of clusters
 List all cities, towns, villages & wards of cities with their population
falling in target area under study.
 Calculate cumulative population & divide by 30, this gives sampling
interval.
 Select a random no. less than or equal to sampling interval having
same no. of digits.
 This forms 1st cluster.
 Random no.+ sampling interval = population of 2nd cluster.
 Second cluster + sampling interval = 3rd cluster.

34. Merits
 Most economical form of sampling.
 Larger sample for a similar fixed cost.
 Less time for listing and implementation.
 Reduce travel and other administrative costs.
Demerits
 May not reflect the diversity of the community.
 Standard errors of the estimates are high, compared to other sampling designs with
same sample size .

35. STRATIFICATION V/S CLUSTERING

36. MULTISTAGE SAMPLING
 Sampling process carried out in various stages.
 An effective strategy because it banks on multiple randomizations. ( any type of
probability sampling can be applied in each stage.)
 Used frequently when a complete list of all members of the population does not exist
and is inappropriate.

37. Merits
 Introduces flexibility in the sampling method.
 Enables existing divisions and sub divisions of population to be used as
units.
 Large area can be covered.
 Valuable in under developed areas.
Demerits
 Less accurate than a sample chosen by a single stage process.

38. MULTIPHASE SAMPLING
 Part of information is collected from whole sample.
 Another set of information is obtained from subsample.
 Example: 20 fever cases
Blood investigations done
Raised ESR Normal ESR
More blood test More blood test
( causing rise in ESR) ( another battery of test to evaluate further)

39. SEQUENTIAL SAMPLING
 Ultimate sample size is fixed in advance.
 Determined on basis of information obtained as survey progresses by discussion rules.

40. THANK YOU