This document discusses key concepts related to sampling in research studies. It defines population as the larger group being studied, and sample as the subset of individuals selected for a study. There are different types of sampling methods discussed, including probability sampling which allows calculation of sampling error, and non-probability sampling which is less expensive but does not allow generalization. The document emphasizes that sample size depends on achieving saturation in qualitative research versus statistical validity in quantitative research. Sources of error like sampling error and bias are also outlined.
Sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population
Sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population
Explains the different methods of Sampling with diagram. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.
Sampling is a process of selecting representative units from an entire population of a study.
Two Types
Probability Sampling Techniques
Non- Probability sampling techniques
Explains the different methods of Sampling with diagram. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.
Sampling is a process of selecting representative units from an entire population of a study.
Two Types
Probability Sampling Techniques
Non- Probability sampling techniques
A short lecture about Atomic Spectroscopy: Flame Photometry, Atomic Absorption, and Atomic Emission with Coupled Plasma (FP, AA and ICP-AES). Presented at 28.03.2011, Faculty of Agriculture, Hebrew University of Jerusalem, by Vasiliy Rosen, M.Sc.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
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Sampling for Quantities & Qualitative Research Abeer AlNajjar.docxanhlodge
Sampling for Quantities & Qualitative Research
Abeer AlNajjar
1
Population
Target group (universe in texts)
Census (to study every member of a population)
because measuring every member of a population usually is not feasible most researchers employ a Sample
Sample ( a subgroup of the population)
2
Communication researchers are interested in a population (also called a universe when applied to texts) of communicators, all the people who posses a particular characteristic, or, in the case of those who study texts, all the messages that share a characteristic of interest.
The population of interest to researchers (often called the target group) might be members of a business, communication majors at a university, all students at a university, all people living in a city, all eligible voters in a country.
Texts ( editorials published in a specific newspaper for a week, or a large universe such as every editorial published In every newspaper in the UAE, or even larger such as all persuasive messages).
The best way to generalize to a population is to study every member of a population (Census)
If every member is studied, we know, by definition, the population’s response at the point in time the study was done
Sample
The results from the sample are then generalized back to (used to represent) the population
Representative sample ( population validity)
Its similarity to its parent population
3
The results from the sample are then generalized back to (used to represent) the population). For such generalization to be valid (demonstrate population validity), the sample must be representative of its population. That is, it must accurately approximate the population.
Types of sampling
Random sampling (probability sampling)
Involves selecting a sample in such a way that each person in the population of interest has an equal chance of being included
Nonrandom sampling (nonprobability sampling)
Is what ever researchers do instead of using procedures that ensure that each member of a population has an equal chance of being selected
Sampling error
Is a number that express how much the characteristic of a sample probably differ from the characteristics of a population
5
There are 2 different types of sampling procedures, and differ in terms of how confident we are about the ability of the selected sample to represent the population from which it is drawn
Random sampling (probability sampling)
Involves selecting a sample in such a way that each person in the population of interest has an equal chance of being included
By giving everyone an equal chance , random sampling eliminates the danger of researchers biasing the selection process because of their own opinions or desires. By eliminating bias, random sampling provides the best assurance that the same characteristics of the population exist in the sample, and, therefore, that the sample represents the population.
Nonrandom sampling: it sometimes is .
Research techniques; samling and ethics eltAbdo90nussair
Advance Research Techniques; How to make samples Abdurrahman Abdalla .. كيف تؤخد العينة في طرق البحث المتقدم .. إعداد عبدالرحمن المهدي نصير جامعة الشرق الادنى - قبرص الشمالية
definition of survey
survey and its type
its purpose and uses.
sampling
approaches
survey methods
research designs
probability and non probability
population
cross sectional design
longitudinal design
successive independent sampling design
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Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
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
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)
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.
18.
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
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
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!
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
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.
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
40.
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.”
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
56.
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