1. Daxaben N Mehta
Smt.S.C.U.Shah Home Science
and C.U.Shah Arts &
Commerce Mahila College
Wadhwancity

2. Define population (N) to be sampled
Quantitative assumptions in sampling
Qualitative assumptions in sampling
Types of sampling
Ethnographic sampling
Interview sampling
Content analysis sampling
How many? Determine sample size (n)
A famous sampling mistake
Control for bias and error

3.  The process of selecting a number
of individuals for a study in such
a way that the individuals
represent the larger group from
which they were selected

4. Sample…
…the representatives selected for a
study whose characteristics
exemplify the larger group from
which they were selected

5. Population…
…the larger group from which
individuals are selected to
participate in a study

6.  To gather data about the
population in order to make an
inference that can be generalized
to the population

7. Identify the group of interest and
its characteristics to which the
findings of the study will be
generalized
…called the “target” population
(the ideal selection)
…oftentimes the “accessible” or
“available” population must be
used (the realistic selection)

8. POPULATION
INFERENCE
SAMPLE

9. POPULATION (N)
IS THE SAMPLE
REPRESENTATIVE?
SAMPLE (n)

10. POPULATION (N)
INFERENCE
IS THE
INFERENCE
SAMPLE (n) GENERALIZABLE?

11. 11

13. A subset of the population,
selected by either
“probability” or “non-
probability” methods. If
you have a “probability
sample” you simply know
the likelihood of any
member of the population
being included (not
necessarily that it is
“random.”

14. I really spend
I want to a lot of time I want to
know what wondering make sure
I wonder how
causes how to others can
small patterns
something measure repeat my
generalize to
else. things. findings.
big patterns.

15. We want to generalize
to the population.
Random events are
predictable.
We can compare
random events to our Therefore…
results.
Probability sampling is
the best approach.

16. I really want
my research
I want to see I want to approach to
describe the I want to show
the world be flexible
context in a how social
through the and able to
lot of detail. change occurs.
eyes of my change.
I’m interested
respondents.
in how things
come to be.

17. Social actors are not
predictable like objects.
Randomized events are
irrelevant to social life.
Probability sampling is
Therefore…
expensive and
inefficient.
Non-probability
sampling is the best
approach.

19. 1 Get a list or “sampling frame”
This is the hard part! It must not
systematically exclude anyone.
Remember the famous sampling mistake?
2 Generate random numbers
3 Select one person per random
number

20. advantages…
easy to conduct
strategy requires minimum knowledge
of the population to be sampled

21. disadvantages…
need names of all population members
may over- represent or under-
estimate sample members
there is difficulty in reaching all
selected in the sample

22. 1 Select a random number, which will
be known as k
2 Get a list of people, or observe a flow
of people (e.g., pedestrians on a
corner)
3 Select every kthperson
Careful that there is no systematic rhythm
to the flow or list of people.
If every 4th person on the list is, say, “rich” or
“senior” or some other consistent pattern,
avoid this method

23. advantages…
sample selection is simple

24. disadvantages…
all members of the population do not
have an equal chance of being
selected
the Kth person may be related to a
periodical order in the population list,
producing unrepresentativeness in the
sample

25. 1 Separate your population into
groups or “strata”
2 Do either a simple random sample
or systematic random sample from
there
Note you must know easily what the “strata”
are before attempting this
If your sampling frame is sorted by, say,
school district, then you’re able to use
this method

26. advantages…
…more precise sample
…can be used for both proportions and
stratification sampling
…sample represents the desired strata

27. disadvantages…
…need names of all population members
…there is difficulty in reaching all
selected in the sample
…researcher must have names of all
populations

28. 1 Get a list of “clusters,” e.g., branches
of a company
2 Randomly sample clusters from that
list
3 Have a list of, say, 10 branches
4 Randomly sample people within those
branches
This method is complex and expensive!

29. advantages…
…efficient
…researcher doesn’t need names of all
population members
…reduces travel to site
…useful for educational research

30. disadvantages…
…fewer sampling points make it less like
that the sample is representative

31. Convenience sampling
Purposive sampling
Quota sampling

32. Convenience sampling
the process of including whoever
happens to be available at the time
…called “accidental” or “haphazard”
sampling
Find some people that are easy to find

33. disadvantages…
…difficulty in determining how
much of the effect (dependent
variable) results from the cause
(independent variable)

34. Purposive sampling
the process whereby the researcher
selects a sample based on experience
or knowledge of the group to be
sampled
…called “judgment ” sampling

35. 1. Find a few people that are
relevant to your topic.
2. Ask them to refer you to more of
them.

36. disadvantages…
…potential for inaccuracy in the
researcher’s criteria and resulting
sample selections

37. Quota sampling
the process whereby a researcher
gathers data from individuals
possessing identified characteristics
and quotas

38. 1 Determine what the population
looks like in terms of specific
qualities.
2 Create “quotas” based on those
qualities.
3 Select people for each quota.
4 the process whereby a researcher
gathers data from individuals
possessing identified
characteristics and quotas

39. disadvantages…
…people who are less accessible (more
difficult to contact, more reluctant
to participate) are under-
represented

41. “The average man is
35% more likely to
“Our findings have a
choose this option over
margin of error of +
the average woman.”
or - 4%, 19 times out
of 20.”

42. People Places Contexts
Times Events

43. People Places
Times

44. Media
Dates

45. …qualitative research is characterized
by in-depth inquiry, immersion in a
setting, emphasis on context,
concern with participants’
perspectives, and description of a
single setting, not generalization to
many settings

46. …because samples need to be small
and many potential participants
are unwilling to undergo the
demands of participation, most
qualitative research samples are
purposive

47. …representativeness is secondary to the
quality of the participants’ ability to
provide the desired information
about self and setting

48. 1. Intensity sampling: selecting
participants who permit study of
different levels of the research topic
2. Homogeneous sampling: selecting
participants who are very similar in
experience, perspective, or outlook

49. 3. Criterion sampling: selecting all
cases that meet some pre-defined
characteristic
4. Snowball sampling: selecting a few
individuals who can identify other
individuals who can identify still
other individuals who might be
good participants for a study

50. 5. Random purposive sampling: with a
small sample, selecting by random
means participants who were
purposively selected and are too
numerous to include all in the study

51.  Qualitative researchers seek
“saturation”
 “How many” isn’t the issue. Do you
understand the phenomenon? Have you
learned enough?
 Mere numbers are irrelevant. You want
“verstehn” or deep understanding
 Quantitative researchers seek statistical
validity
 Can you safely generalize to the population?
Have you systematically excluded anyone?

52.  The size of the sample influences
both the representativeness of the
sample and the statistical
analysis of the data
…larger samples are more likely
to detect a difference between
different groups
…smaller samples are more likely
not to be representative

53. 1. The larger the population size, the
smaller the percentage of the
population required to get a
representative sample
2. For smaller samples (N ‹ 100), there
is little point in sampling. Survey
the entire population.

54. 3. If the population size is around 500
(give or take 100), 50% should be
sampled.
4. If the population size is around
1500, 20% should be sampled.
5. Beyond a certain point (N =
5000), the population size is almost
irrelevant and a sample size of 400
may be adequate.

55. 1. Sampling error
2. Sampling bias
…which threaten to render a study’s
findings invalid

57. That’s
Truman
They only asked rich,
white people with
telephones who’d they
vote for. Sadly, they
published their
mistake

58. “…predicting behavior on
the basis of knowledge of
attitude is a very
hazardous venture.”
Meaning, predicting
social behavior is often
misguided. Keep that in
mind!

59. Sampling error…
…the chance and random variation in
variables that occurs when any
sample is selected from the population
…sampling error is to be expected

60. …to avoid sampling error, a census of
the entire population must be taken
…to control for sampling error,
researchers use various sampling
methods

61. Sampling bias…
…nonrandom differences, generally the
fault of the researcher, which cause
the sample is over-represent
individuals or groups within the
population and which lead to
invalid findings
…sources of sampling bias include the
use of volunteers and available
groups

62.  Be aware of the sources of sampling
bias and identify how to avoid it
 Decide whether the bias is so severe
that the results of the study will be
seriously affected
 In the final report, document
awareness of bias, rationale for
proceeding, and potential effects

63. Questions???
63