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.
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
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.
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
Sampling and different ways of sampling under public opinion and survey research.Advantages and disadvantages of different sampling methods with pictures and examples.
Sampling and different ways of sampling under public opinion and survey research.Advantages and disadvantages of different sampling methods with pictures and examples.
A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the researcher would adopt in selecting items for the sample. Sample design may as well lay down the number of items to be included in the sample i.e., the size of the sample. Sample design is determined before data are collected. There are many sample designs from which a researcher can choose. Some designs are relatively more precise and easier to apply than others. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.
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.
Business level strategy- BCG matrix for companies products and products under Products Life Cycle(PLC). this matrix is proposed by Boston Consulting Group.
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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.
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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
3. Non- probability sampling
Non-probability sampling is a sampling
technique where the samples are gathered in
a process that does not give all the
individuals in the population equal chances
of being selected.
4. Convenience Sampling:-
Convenience
sampling is probably the most common
of all sampling techniques. With
convenience sampling, the samples are
selected because they are accessible to
the researcher. Subjects are chosen
simply because they are easy to recruit.
This technique is considered easiest,
cheapest and least time consuming.
5. Consecutive Sampling:-
Consecutive
sampling
is very similar to convenience sampling
except that it seeks to include all accessible
subjects as part of the sample. This non-
probability sampling technique can be
considered as the best of all non-
probability samples because it includes all
subjects that are available that makes the
sample a better representation of the entire
population.
6. Quota Sampling:-
Quota sampling
is a sampling methodology wherein
data is collected from a homogeneous
group. It involves a two-step process
where two variables can be used to
filter information from the population. It
can easily be administered and helps in
quick comparison.
7. Steps to select Quota
sampling
1. Divide the population into strata or
groups of individuals that are similar
(homogeneous) in some way that is
important to the response.
2. Choose a separate sample from each
stratum. This does not have to be a
random sample
3. Combine these sample to form a Quota
sample
8. Example for Quota sampling
Example: 1-
If basis of the quota is college year
level and the researcher needs equal
representation, with a sample size of 100, he
must select 25 1st year students, another 25 2nd
year students, 25 3rd year and 25 4th year
students. The bases of the quota are usually age,
gender, education, race, religion and
socioeconomic status.
9. Example 2-
Suppose a researchers is interested in
the shopping preference of consumers at a local
mall. Since he believes men and women have
different preference, the researcher decides to
stratify(sub group) the population by gender.
From past data, he knows that roughly 60% of
mall shoppers are female. He wants a sample size
200. To get a proportional sample, he decide to
sample 120 females and 80 males.
To save time, he post a sign in the mall to
solicit volunteers. He include 120 females
volunteers and 80 male volunteers in his sample
12. When to Use Quota Samples?
1. it allows the researchers to sample a subgroup that is
of great interest to the study. If a study aims to
investigate a trait or a characteristic of a certain
subgroup.
2. Researchers can use quota sampling to study a
characteristic of a particular subgroup, or observe
relationships between different subgroups.
3. Quota sampling can also be used at times when
detailed accuracy is not important.
4. when the company is short of time or the budget of
the person who is researching on the topic is limited.
13. Judgmental Sampling:-
Judgmental sampling
is more commonly known as purposive
sampling. In this type of sampling, subjects are
chosen to be part of the sample with a specific
purpose in mind. With judgmental sampling,
the researcher believes that some subjects are
more fit for the research compared to other
individuals. This is the reason why they are
purposively chosen as subjects.
14. Snowball Sampling:-
Snowball
sampling is usually done when there is a
very small population size. In this type of
sampling, the researcher asks the initial
subject to identify another potential
subject who also meets the criteria of the
research. The downside of using a
snowball sample is that it is hardly
representative of the population.
15. When to Use Non-Probability Sampling
1. This type of sampling can be used when demonstrating that a
particular trait exists in the population.
2. It can also be used when the researcher aims to do a qualitative
, pilot or exploratory study.
3. It can be used when the research does not aim to generate results
that will be used to create generalizations pertaining to the entire
population.
4. It is also useful when the researcher has limited budget, time and
workforce.
5. It can be used when randomization is impossible like when the
population is almost limitless.
6. This technique can also be used in an initial study which will be
carried out again using a randomized, probability sampling.
16. Terminology used in
research
Sampling Error:-
The degree to which the results from the sample
deviate from those that would be obtained from the entire
population, because of random error in the selection of respondent
and the corresponding reduction in reliability
Sampling Frame:-
A listing that should include all those in the
population to be sampled and exclude all those who are not in the
population
17. Sample:-
The population researched in a particular study. Usually,
attempts are made to select a "sample population" that is considered
representative of groups of people to whom results will be
generalized or transferred
Survey:-
A research tool that includes at least one question which is
either open-ended or close-ended and employs an oral or written
method for asking these questions
Control Group:-
The group in an experimental design that receives
either no treatment or a different treatment from the experimental
group. This group can thus be compared to the experimental group.
18. Controlled Experiment:-
An experimental design with two or
more randomly selected groups [an experimental group and control
group] in which the researcher controls or introduces the
independent variable and measures the dependent variable at least
two times
Population:-
The target group under investigation. The
population is the entire set under consideration. Samples are drawn
from populations.
Random Sampling:-
A process used in research to draw a sample
of a population strictly by chance, yielding no discernible pattern
beyond chance