Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampli...Alam Nuzhathalam
An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..
simplest way of explanation from a smart study.Sample techniques used in sampling. there are two types of techniques used in the process of sampling such as probability sampling and non probability sampling and here i have explained only Non- probability sampling.
Probability Sampling and Types by Selbin Babuselbinbabu1
The presentation will cover probability sampling and all the types of probability sampling like Random sampling , systematic random sampling, strtified random sampling, cluster random sampling and multi stage sampling.
Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampli...Alam Nuzhathalam
An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..
simplest way of explanation from a smart study.Sample techniques used in sampling. there are two types of techniques used in the process of sampling such as probability sampling and non probability sampling and here i have explained only Non- probability sampling.
Probability Sampling and Types by Selbin Babuselbinbabu1
The presentation will cover probability sampling and all the types of probability sampling like Random sampling , systematic random sampling, strtified random sampling, cluster random sampling and multi stage sampling.
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.
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.
Population and Sampling Techniques.pptxDrHafizKosar
The first step in the process of collecting quantitative data is to identify the people and places you plan to study. This involves determining whether you will study individuals or entire organizations (e.g., schools) or some combination. If you select either individuals or organizations, you need to decide what type of people or organizations you will actually study and how many you will need for your research. These decisions require that you decide on a unit of analysis, the group and individuals you will study, the procedure for selecting these individuals, and assessing the numbers of people needed for your data analysis.
Identify Your Unit of Analysis
Who can supply the information that you will use to answer your quantitative research questions or hypotheses? Some possibilities might be students, teachers, parents, adults, some combination of these individuals, or entire schools. At this early stage in data collection, you must decide at what level (e.g., individual, family, school, school district) the data needs to be gathered. This level is referred to as the unit of analysis.
In some research studies, educators gather data from multiple levels (e.g., individuals and schools), whereas other studies involve collecting data from only one level (e.g., principals in schools). This decision depends on the questions or hypotheses that you seek to answer. Also, the data for measuring the independent variable may differ from the unit for assessing the dependent variable. For example, in the study of the impact of adolescent aggression on school climate, a researcher would measure the independent variable, adolescent aggression, by collecting data from individuals while measuring the dependent variable, school climate, based on data from entire schools and their overall climates (e.g., whether students and teachers believe the school curriculum supports learning).
If Faiza wants to answer the question “Why do students carry weapons in high school?” what unit of analysis will she study? Alternatively, if she wanted to compare answers to the question “Why do students carry weapons in rural high schools and urban high schools?” what two types of units of analysis will she study?
Specify the Population and Sample
If you select an entire school to study or a small number of individuals, you need to consider what individuals or schools you will study. In some educational situations, you will select individuals for your research based on who volunteers to participate or who is available (e.g., a specific classroom of students). However, those individuals may not be similar (in personal characteristics or performance or attitudes) to all individuals who could be studied. A more advanced research process is to select individuals or schools who are representative of the entire group of individuals or schools.
Similar to sampling in research methodology. qualitative and quantitative approach (20)
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. What is sampling
Once the researcher has chosen a hypothesis to
test in a study, the next step is to select a pool of
participants to be in that study.
Sampling, as it relates to research, refers to the
selection of individuals, units, and/or settings to be
studied.
Whereas quantitative studies strive for random
sampling,
qualitative studies often use purposeful or criterion-
based sampling, that is, a sample that has the
characteristics relevant to the research questions.
3. Why Sample for researches?
First, it is usually too costly to test the entire population
The second reason to sample is that it may be impossible to
test the entire population.
The third reason to sample is that testing the entire population
often produces error.
The final reason to sample is that testing may be destructive
To draw conclusions about populations from samples.
4. Sampling methods in qualitative and
quantitative research
Assumptions of quantitative
sampling……….
We want to generalize to
the population.
Random events are
predictable.
We can compare
random events to our
results.
Therefore
Probability sampling is
the best approach.
Assumptions of qualitative
sampling………
Social actors are not
predictable like objects.
Randomized events are
irrelevant to social life.
Probability sampling is
expensive and inefficient.
therefore
Non-probability sampling
is the best approach.
5. size of the Sample
• sample size not matter in qualitative research Because
of the assumptions that qualitative researchers make,
namely, that the social world is not predictable.
Qualitative researchers believe that people are not like
molecules or other objects; people’s actions are not
predictable.
• But quantitative researchers believe that social
activity is predictable. So when they compare their
observations of social activity to what would happen in
purely random results, the difference says something.
6. Types of Samples
6
• Probability (Random) Samples
• Simple random sample
– Systematic random sample
– Stratified random sample
– Multistage sample
– Multiphase sample
– Cluster sample
• Non-Probability Samples
– Convenience sample
– Purposive sample
– Quota
– Snowball
– Theoretical
7. Simple random sample
• Simple random sampling is the most straight forward
of the random sampling strategies. We use this
strategy when we believe that the population is
relatively homogeneous for the characteristic of
interest.
• For example, let's say you were surveying first-time
parents about their attitudes toward mandatory seat
belt laws. You might expect that their status as new
parents might lead to similar concerns about safety.
On campus, those who share a major might also have
similar interests and values; we might expect
psychology majors to share concerns about access to
mental health services on campus.
8. Systematic sampling
• Systematic sampling yields a probability sample
but it is not a random sampling strategy (it is one
of our exceptions). Systematic sampling strategies
take every nth person from the sampling frame.
• For example, you choose a random start page
and take every 45th name in the directory until
you have the desired sample size. Its major
advantage is that it is much less cumbersome to
use than the procedures outlined for simple
random sampling.
9. Stratified random sampling
• Stratified random sampling is used when we have
subgroups in our population that are likely to differ
substantially in their responses or behavior. This sampling
technique treats the population as though it were two or
more separate populations and then randomly samples
within each.
• For example, you are interested in visual-spatial reasoning
and previous research suggests that men and women will
perform differently on these types of task. So, you divide
your sample into male and female members and randomly
select equal numbers within each subgroup (or "stratum").
With this technique, you are guaranteed to have enough of
each subgroup for meaningful analysis.
10. Multistage sampling
• Multistage sampling. This is our most
sophisticated sampling strategy and it is often
used in large epidemiological studies. To obtain a
representative national sample, researchers may
select zip codes at random from each state.
Within these zip codes, streets are randomly
selected. Within each street, addresses are
randomly selected. While each zip code
constitutes a cluster, which may not be as
accurate as other probability sampling strategies,
it still can be very accurate.
11. Cluster sampling
• Cluster sampling is useful when it would be impossible or
impractical to identify every person in the sample.
• Suppose a college does not print a student directory. It would
be most practical in this instance to sample students from
classes. Rather than randomly sample 10% of students from
each class, which would be a difficult task, randomly sampling
every student in 10% of the classes would be easier.
Sampling every student in a class is not a random procedure.
However, by randomly selecting the classes, you have a
greater probability of capturing a representative sample of the
population. Many students believe that it is not possible to
gather a representative sample for a class project or a thesis.
However, this type of cluster sampling is easily done,
especially since all colleges publish lists of classes for
registration.
12. Convenience sampling
• Convenience sampling selects a particular group
of people but it does not come close to sampling all
of a population.
• Convenience sampling is widely used in student
research projects. Students contact professors that
they know and ask if they can use their classes to
recruit research subjects. Convenience sampling
looks just like cluster sampling. The major difference
is that the clusters of research participants are
selected by convenience rather than by a random
process.
13. Purposive sampling
• Purposive sampling targets a particular group of people. When the
desired population for the study is rare or very difficult to locate and
recruit for a study, purposive sampling may be the only option.
• For example, you are interested in studying cognitive processing
speed of young adults who have suffered closed head brain injuries
in automobile accidents. This would be a difficult population to find.
Your city has a well-established rehabilitation hospital and you
contact the director to ask permission to recruit from this population.
The major problem with purposive sampling is that the type of
people who are available for study may be different from those in the
population who can't be located and this might introduce a source of
bias. For example, those available for study through the
rehabilitation hospital may have more serious injuries requiring
longer rehabilitation, their families may have greater education and
financial resources (which resulted in their choosing this hospital for
care).
14. Theoretical Sampling
• Theory-Based or Operational Construct or Theoretical
Sampling—dentifies manifestations of a theoretical
construct of interest so as to elaborate and examine
the construct. This strategy is similar to criterion
sampling, except it is more conceptually focused. This
strategy is used in grounded theory studies. You would
sample people/incidents, etc., based on whether or
not they manifest/represent an important theoretical
or operational construct. For example, if you were
interested in studying the theory of “resiliency” in
adults who were physically abused as children, you
would sample people who meet theory-driven criteria
for “resiliency.”
15. Snowball or Chain Sampling
• Snowball or Chain Sampling—Identifies cases of
interest from people who know people who know
what cases are information-rich, that is, who would be
a good interview participant. Thus, this is an approach
used for locating information-rich cases. You would
begin by asking relevant people something like: “Who
knows a lot about ?”
• For example, you would ask for nominations, until the
nominations snowball, getting bigger and bigger.
Eventually, there should be a few key names that are
mentioned repeatedly.
16. Advantages & disadvantages of
samples.
Technique advantages disadvantages
Quota Ensures selection of
adequate numbers of
subjects with appropriate
characteristics
Not possible to prove
that the sample is
representative of
designated population
Snowball Possible to include
members of groups
where no lists or
identifiable clusters even
exist (e.g., drug abusers,
criminals)
No way of knowing
whether the sample is
representative of the
population
convenience Inexpensive way of
ensuring sufficient
numbers of a study
Can be highly
unrepresentative
17. Technique Advantages disadvantages
Simple
random
Highly representative if all subjects
participate; the ideal
Not possible without
complete list of
population members;
Stratified
random
Can ensure that specific groups are
represented, even proportionally, in
the sample(s) (e.g., by gender), by
selecting individuals from strata list
More complex, requires
greater effort than simple
random; strata must be
carefully defined
Cluster Possible to select randomly when no
single list of population members
exists, but local lists do; data
collected on groups may avoid
introduction of confounding by
isolating members
Clusters in a level must be
equivalent and some natural
ones are not for essential
characteristics (e.g.,
geographic: numbers equal,
but unemployment rates
differ)
Purposive Ensures balance of group sizes when
multiple groups are to be selected
Samples are not easily
defensible as being
representative of populations
due to potential subjectivity
of researcher
18. Process
18
• The sampling process comprises several stages:
– 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 collecting
– Reviewing the sampling process
19. Sampling Frame
• The list or procedure defining the POPULATION.
(From which the sample will be drawn.)
• Distinguish sampling frame from sample.
Examples:
Telephone book
Voter list
Random digit dialing
• Essential for probability sampling, but can be
defined for non-probability sampling
20. The time factor of sampling
• A sample may provide to us with needed information quickly.
Ex: you are a Doctor and a disease has broken out in a village
within your area of jurisdiction, the disease is contagious and it
is killing within hours nobody knows what it is. You are
required to conduct quick tests to help save the situation. If
you try a census of those affected, they may be long dead
when you arrive with your results. In such a case just a few of
those already infected could be used to provide the required
information.
• Accuracy and sampling A sample may be more accurate
than a census. A sloppily conducted census can provide less
reliable information than a carefully obtained sample
21. Sampling Problems
missing elements - individuals who should be on the list but for some
reason are not on the list.
Ex: if my population consists of all individuals living in a particular city
and I use the phone directory as my sampling frame or list, I will
miss individuals with unlisted numbers or who can not afford a
phone.
Foreign elements Elements which should not be included in
population and sample appear on the sampling list.
Ex: if I were to use property records to create my list of individuals
living within a particular city, landlords who live elsewhere would be
foreign elements. In this case, renters would be missing elements.
Duplicates These are elements who appear more than once on the
sampling frame.
Ex: if I am a researcher studying patient satisfaction with emergency
room care, I may potentially include the same patient more than
once in my study. If the patients are completing a patient satisfaction
questionnaire, I need to make sure that patients are aware that if
they have completed the questionnaire previously, they should not
complete it again. If they complete it more that once, their second
set of data respresents a duplicate.
22. References
• Camic, P. M, Rhodes, J. E., & & Yardley, L. (Ed.). (2003). Qualitative
research in psychology: Expanding perspectives in methodology and
design. Washington, DC: American Psychological Association.
• Creswell, J. W. (1998). Qualitative Inquiry & Research Design: Choosing
Among Five Traditions. Thousand Oaks: CA. Sag Publications, Inc.
• Dey, I. (1999). Grounding grounded theory: Guidelines for qualitative
inquiry. San Diego, CA: Academic Press.
• Harter, S. (1978). Effectance motivation reconsidered: Toward a
developmental model. Human Development, 21, 34–64.
• Harter, S. (1999). The construction of the self: A developmental perspective.
New York: Guilford.
• Hitchcock, J. H, Nastasi, B. K., Dai, D. C., Newman, J., Jayasena, A.,
Bernstein-Moore, R., Sarkar, S., & Varjas, K. (2004). Illustrating a mixed-
method approach for identifying and validating culturally specific constructs.
Accepted for publication in Journal of School Psychology.