Sampling and Types of sampling are given along with questions.ppt

Sampling types

1. Sampling
Dr. Hima Gupta
Professor

2. Introduction
Sampling
The process of drawing a number of individual cases
from a larger population
A way to learn about a larger population by obtaining
information from a subset of a larger population
Example

Presidential polls are based upon samples of the
population that might vote in an election

4. Introduction
Why Sample?
To learn something about a large group
without having to study every member of that
group
Time and cost

Studying every single instance of a thing is
impractical or too expensive
Example

Census

5. Introduction
Why Sample?
Improve data quality

Obtain in-depth information about each subject
rather than superficial data on all

6. Introduction
Why Sample?
We want to minimize the number of things we
examine or maximize the quality of our
examination of those things we do examine.

7. Introduction
Why Sample?
When is sampling unnecessary?

The number of things we want to sample is small

Data is easily accessible

Data quality is unaffected by the number of things
we look at

Example
You are interested in the relationship between team
batting average and winning percentage of major
league baseball teams
There are only 30 major league teams
Data on team batting averages and winning
percentages are readily available

8. Introduction
Why Sample?
Elements

A kind of thing the researcher wants to look at

9. Quiz – Question 1
Suppose you are interested in describing the
nationality of Nobel prize-winning scientists.
What would an element in your study be?
What would the population be?

10. Answer to Q1
Population – Nobel Prize winning Scientists
Element - Nationality

11. Introduction
Why Sample?
Population

The group of elements from which a researcher
samples and to which she or he might like to
generalize

12. Quiz – Question 2
In the case of presidential elections in the
United States the population is ________ and
the elements of this population are
_________.

13. Answer – Q2
Population - US Citizens
Element – People above 18 years

14. Introduction
Why Sample?
Sample

A number of individual cases drawn from a larger
population

15. Introduction
Sampling Frames, Probability versus
Nonprobability Samples
Target population

A population of theoretical interest

16. Introduction
Sampling Frames, Probability versus
Nonprobability Samples
Sampling frame or study population

The group of elements from which a sample is
actually selected

17. Quiz – Question 3
The local television station conducted a study of TV
viewers in the local viewing region. A list of all
residential customers who subscribed to cable TV
was obtained from the cable company. The list had
200,000 households as subscribers. The TV station
samples every 40
th
household on the subscriber list.
An interviewer visited each household and conducted
the survey on viewing habits of household members.
What is the sampling frame of the study?

18. Answer – Q3
Sampling Frame – 2,00, 000 Households who
subscribes Cable TV

19. Introduction
Sampling Frames, Probability versus
Nonprobability Samples
Nonprobability Samples

A sample that has been drawn in a way that
doesn’t give every member of the population a
known chance of being selected

20. Introduction
Sampling Frames, Probability versus
Nonprobability Samples
Probability

A sample drawn in a way to give every member
of the population a known (nonzero) chance of
inclusion

Probability samples are usually more
representative than nonprobability samples of the
populations from which they are drawn

21. Introduction
Sampling Frames, Probability versus
Nonprobability Samples
Biased Samples

A sample that is not representative from the
population which it is drawn
Probability samples are LESS likely to be biased samples
An example of sample bias is
 conducting research with a
group of participants that do not accurately represent the
population.
Asking a group of 9th graders what they believe the
speed limit should be on highway is an example of
sample bias

22. Introduction
Sampling Frames, Probability versus
Nonprobability Samples
Generalizability

The ability to apply the results of a study to groups or
situations beyond those actually studied.

A probability sample tends to be more generalizable
because it increases the chances that samples are
representative of the populations from which they are
drawn.
Imagine you have a population of 100 people.
 In this scenario,
every person would have odds of 1 in 100 for getting
selected.
Probability sampling gives you the best chance to create a
sample representative of the population.

23. Sampling Frame & Target
Population
The target population is the entire group of
observational units you want to make a
statement about (generalize to).
 
A sampling frame is a list of observational units
to be sampled.
 

24. Introduction
STOP AND THINK
Can you think why researchers haven’t used
cell phone numbers in polling until recently?
What problem may result from only using
landline numbers?

25. Sources of Error Associated with
Sampling
Types of Survey Error – due to sampling
Coverage Error
Nonresponse Error
Sampling Error

26. Sources of Error Associated with
Sampling
Coverage Errors
Errors that results from differences between the
sampling frame and the target population

27. Sources of Error Associated with
Sampling
Coverage Errors
People are typically left out, if samples are drawn from
phone books, car registrations, etc…
Unlisted Phone Numbers – one of the greatest potentials for
coverage error
Pollsters use random digit dial to avoid unlisted numbers
Random-digit dialing
A method for selecting participants in a telephone
survey that involves randomly generating telephone
numbers
What are potential future problems, with using
telephone listings to draw a sample?

28. Sources of Error Associated with
Sampling
Coverage Errors
Parameter- A summary of a variable characteristic in a population

29. Sources of Error Associated with
Sampling
Coverage Errors

Statistic-A summary of a variable in a sample

30. Sources of Error Associated with
Sampling
Nonresponse Error
Errors that result from differences between
nonresponders and responders to a survey

31. Stop and Think
What kinds of people might not be home to
pick up the phone in the early evening when
most survey organizations make their calls?
What kinds of people might refuse to respond
to telephone polls, even if they were
contacted?

32. Sources of Error Associated with
Sampling
Sampling Error
Any difference between the characteristics of
a sample and the characteristics of the
population from which the sample is drawn

33. Sources of Error Associated with Sampling

34. Non- Sampling Errors

35. Sources of Error Associated with
Sampling
Margin of error
Suggestion of how far away the actual
population parameter is likely to be from the
statistic
Larger samples typically lead to smaller margins of
error, and smaller samples result in larger
margins of error.
For example,
 a survey with 1,000 respondents
might have a margin of error of ±3%.
Doubling the sample size to 2,000 could reduce the
margin of error to ±2%.

36. Sample Size Determination - n
Sample Size = Z
2
* p (1-p)/ M
2
Z = Z Score for confidence level
M = Margin of Error
P = Population proportion
For 95% Confidence level Z = 1.96

37. Types of Probability Sampling
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Multistage Sampling

38. Types of Probability Sampling
Simple Random Sampling
A probability sample in which every member of
a study population has been given an equal
chance of selection
Lottery Method
For example,
 if you randomly select 1000
people from a town with a population of
100,000 residents, each person has a
1000/100000 = 0.01 probability.
That's a simple calculation requiring no
additional knowledge about the population's
composition. Hence, simple random sampling.

39. Types of Probability Sampling
Simple Random Sampling
Sampling distribution

The distribution of a sample statistic

A visual display of the samples

40. Types of Probability Sampling

41. Types of Probability Sampling
Systematic Sampling
A probability sampling procedure that involves
selecting every kth element from a list of
population elements, after the first element has
been randomly selected
Example

Divide the total number of elements by the number
you want in your sample 24/6 = 4

Randomly select a number between 1 and 4 and
then select every 4
th
element from that number

42. Types of Probability Sampling
Systematic Sampling
Selection interval

The distance between the elements selected in a
sample
Selection Interval (k) = population size
sample size
If you had a list of 1,000 customers (your target
population) and you wanted to survey 200 of
them, your interval would be 5.
This means that you would sample every 5th
person in your list of 1,000 customers.
 

43. Types of Probability Sampling
Stratified Sampling
A probability sampling procedure that involves
dividing the population in groups or strata defined
by the presence of certain characteristics and then
random sampling from each stratum
Example

If you had a population that was 10% women and
you want a sample that is also 10% women

44. Types of Probability Sampling
Stratified Sampling
Steps to draw a stratified random sample
1.Group the study population into strata or into
groups that share a given characteristic
2.Enumerate each group separately
3.Randomly sample within each strata
For research, the target market is split into two
strata based on gender, where there are 2,000
males and 6,000 females.
Then, for a sampling fraction of ¼, 500 males and
1,500 females will be selected in the final
sample population

45. Types of Probability Sampling
Cluster Sampling
A probability sampling procedure that involves
randomly selecting clusters of elements from a
population and subsequently selecting every element
in each selected cluster for inclusion in the sample
Cluster sampling is an option if data collection involves
visits to sites that are far apart

46. Types of Probability Sampling
Cluster Sampling
Example
A survey conducted by a company to better
understand the preferences and needs of their
customers.
The company could divide its customer base into
clusters based on age, gender, location, etc., and
then select a random sample from each cluster for
further analysis.

47. Types of Probability Sampling
Multistage Sampling
A probability sampling procedure that involves
several stages, such as randomly selecting
clusters from a population, then randomly
selecting elements from each of the clusters

48. Types of Probability Sampling
Multistage Sampling
Example

Random Digit Dial
Stage 1: Areas Codes randomly sampled
Stage 2: Three digit local exchanges randomly
sampled
Stage 3: Last four digits randomly sampled
Stage 4: Asking the person who answer the phone
for the appropriate person you want to interview

49. There are two types of multistage sampling
1. multistage cluster sampling and
2. multistage random sampling. 

50. Multistage Cluster Sampling
For example, if a researcher wants to conduct
research on the different eating habits in the
India, it is impossible to go from one house to
the other to collect this data from everyone.
So, the researcher will have to select the states
that are of interest to the study.
He/she will select the district needed for the
research and then narrow it down by selecting
specific streets or blocks to represent the state.
The researcher will finally choose specific
respondents from the selected blocks to
participate in the research. 

51. Multi Stage Random Sampling
For example, a researcher wants to understand
the feeding habits of children under the age of
10 in US, and for the purpose of the research,
the sample size will be 50 respondents. 
The researcher will first randomly select 5
states out of 20. They will then select 5 districts
out of each state randomly.
Now, from the 5 selected districts, the
researcher will randomly choose 6 households
to participate in the research.

52. Quiz – Question 4
You want to draw a sample of the employees at a large
university ensuring that in your sample you have people
represented from all personnel categories including
administrators, faculty, secretarial staff, cleaning staff,
mail room staff, technicians, and students.
What type of probability sample would be best?

53. Types of Nonprobabilty Sampling
Purposive Sampling
Quota Sampling
Snowball Sampling
Convenience Sampling

54. Types of Nonprobability Sampling
Purposive Sampling
A nonprobability sampling procedure that involves
selecting elements based on a researcher's
judgment about which elements will facilitate his or
her investigation

55. Example of Purposive Sampling
A researcher studying the experiences of
individuals living with chronic pain, and
therefore selecting a sample of individuals who
have been diagnosed with chronic pain.
A sample size of 30 individuals is often
considered sufficient for qualitative research
Gather purposive samples for 'consumers
providing feedback on new smartphone
features in 2024.'  Focus on this criterion -
consumers reviewing new smartphone features
in 2024. Exclude those who are not interested in
smartphones or have already given feedback.  

56. Types of Nonprobability Sampling
Quota Sampling
A nonprobability sampling procedure that
involves describing the target population in
terms of what are thought to be relevant
criteria and then selecting sample elements to
represent the “relevant” subgroups in
proportion to their presence in the target
population

57. Example of Quota Sampling
A store determines its customer base of 1000
is comprised of 600 women and 400 men.
Sample based on proportion.
The quota size should be representative of
the collective subgroup population. In the
example above, he should select 60 women
and 40 men.

58. Quota and Stratified Sampling
While both quota sampling and stratified
sampling aim to ensure the representation
of different subgroups within the
population, stratified sampling involves
random selection within each stratum,
whereas quota sampling does not
necessarily involve random selection.
Instead, it focuses on filling predetermined
quotas for each demographic group.

59. Types of Nonprobability Sampling
Snowball Sampling
A nonprobability sampling procedure that
involves using members of the group of interest
to identify other members of the group

60. Example of Snowball Sampling
Snowball sampling is used when researchers
have difficulty finding participants for their
studies.
This typically occurs in studies on hidden
populations, such as criminals, drug dealers,
or sex workers, as these individuals are
difficult for researchers to access.

61. Research Examples - Snowball
•Investigating lifestyles of heroin users
(Kaplan, Korf, & Sterk, 1987).
•Identifying Argentinian immigrant
entrepreneurs in Spain (Baltar & Brunet,
2012).
•Studying illegal drug users over the age of 40
(Waters, 2015).
•Assess the prevalence of irritable bowel
syndrome in South China and its impact on
health-related quality of life (Xiong, 2004).

62. Types of Nonprobability Sampling
Convenience Sampling
A nonprobability sampling procedure that
involves selecting elements that are readily
accessible to the researcher
Sometimes called an available-subjects
sample

63. Convenience Sampling - Example
Online and social media surveys, asking
acquaintances, and surveying people in a
mall, on the street, and in other crowded
locations.

64. Choosing a Sampling Technique
Is it desirable to sample at all or can the whole population
be used?
Is it important to generalize to a larger population?
Political preference polls
Do you have the access and ability to perform probability
sampling?
Major considerations
Methods
Theory
Practicality
Ethics

65. Summary
Sampling is a means to an end.
We sample because studying every element
in our population is frequently beyond our
means or would threaten the quality of our.
 On the other hand, we don’t need to sample
when studying every member of our
population is feasible.

66. 1. School children, some with asthma and some
without, are identified from General Practitioner
records.
Method: children are selected randomly within each of the
two groups and the number of children in each group is
representative of the total patient population for this age
group.
2. Children with and without chronic asthma are identified from
GP records.
Method: the children are selected so that in the sample exactly
50% have chronic asthma and 50% have no asthma. Within
each of these groups, the children are randomly selected.

67. 3. A survey of attitudes of drug users to
rehabilitation services.
Method: drug users are recruited by advertising in
the local newspaper for potential respondents.

68. Thanks