Sampling is the process of selecting individual cases from a larger population to gain insights without studying every member, often used for cost and time efficiency. Different sampling techniques, including probability and nonprobability methods, impact the representativeness of the sample and the generalizability of the results. Various sources of error, such as coverage error and nonresponse error, can affect sampling accuracy, making careful selection of sampling methods essential.

1. Sampling
Chapter 5

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

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

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

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

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

15. 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 40th
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?

16. 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

17. 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

18. 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

19. 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.

20. 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?

21. Focal Research
 “Calling Cell Phones in ’08 Pre-Election Polls”
 Examines the hypothesis than Barack Obama
fared better in probability samples including
landline- and cell phone-users than in samples
including landline users alone.

22. Focal Research
 Thinking about ethics
 Because of the sampling technique employed,
the Pew pollsters never knew the identity of
their respondents, so respondent anonymity
was never in danger.
 Moreover, participation in the survey was
voluntary.

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

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

25. 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?

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

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

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

29. 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?

30. 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

31. Sources of Error Associated with
Sampling
 Sampling Error
 Sampling Variability
 The variability in sample statistics that occurs
when different samples are drawn from the same
population

32. 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

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

34. 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
 One way to draw a simple random sample, is to
put all possibilities on paper, cut them up, and
then draw a sample from a hat
 Research Randomizer (http://randomizer.org)

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

36. Types of Probability Sampling

37. 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 4th
element from that number

38. Types of Probability Sampling
 Systematic Sampling
 Selection interval
 The distance between the elements selected in a
sample
Selection Interval (k) = population size
sample size

39. 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

40. 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

41. 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

42. Types of Probability Sampling
 Cluster Sampling
 Example
 You are conducting a study of Kentucky high school
students
 You could obtain a list of all high school students in the
state and complete random sampling
 A cluster sample would be more practical
 Obtain a list of all high schools in Kentucky
 Random sample the high schools from the list
 Obtain a list of students for each high school
selected and then contact each of those students

43. 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

44. 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

45. 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?

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

47. 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

48. 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

49. 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

50. 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

51. 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

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