This document discusses different sampling methods used in research. It describes probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling which give all individuals an equal chance of being selected. It also describes non-probability sampling methods like convenience sampling, quota sampling, voluntary sampling, purposive sampling, and snowball sampling which do not give all individuals an equal chance of being selected. The key advantages and disadvantages of each method are provided along with examples. Overall, the document stresses the importance of selecting the appropriate sampling method to reduce bias and ensure samples are representative of the overall population.
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Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
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..
Dear viewers Check Out my other piece of works at___ https://healthkura.com
Data Collection (Methods/ Tools/ Techniques), Primary & Secondary Data, Assessment of Qualitative Data, Qualitative & Quantitative Data, Data Processing
Presentation Contents:
- Introduction to data
- Classification of data
- Collection of data
- Methods of data collection
- Assessment of qualitative data
- Processing of data
- Editing
- Coding
- Tabulation
- Graphical representation
If anyone is really interested about research related topics particularly on data collection, this presentation will be the best reference.
For Further Reading
- Biostatistics by Prem P. Panta
- Fundamentals of Research Methodology and Statistics by Yogesh k. Singh
- Research Design by J. W. Creswell
- Internet
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..
The data, collected for research, has to be processed, analyzed and interpreted to develop a solution to the research question.
Data analysis is a practice in which unorganized or unfinished data is ordered and organized so that useful information can be extracted from it.
It is the most enjoyable part of carrying out the research since after all of the hard works and waiting the researcher gets the chance to find out the answers. So analyzing the data and interpreting the results are the “reward” for the work of collecting the data.
Types of data sampling,probability sampling and non-probability sampling,Simple random sampling,Systematic sampling,Stratified sampling,Clustered sampling,Convenience sampling,Quota sampling,Judgement (or Purposive) Sampling,Snowball sampling,Bias in sampling.
The data, collected for research, has to be processed, analyzed and interpreted to develop a solution to the research question.
Data analysis is a practice in which unorganized or unfinished data is ordered and organized so that useful information can be extracted from it.
It is the most enjoyable part of carrying out the research since after all of the hard works and waiting the researcher gets the chance to find out the answers. So analyzing the data and interpreting the results are the “reward” for the work of collecting the data.
Types of data sampling,probability sampling and non-probability sampling,Simple random sampling,Systematic sampling,Stratified sampling,Clustered sampling,Convenience sampling,Quota sampling,Judgement (or Purposive) Sampling,Snowball sampling,Bias in sampling.
this is an presentation regarding samples in research methodology in qualitative and quantitative approaches . this will be very useful basically this presentation most significant for university students those who are following and learning for the research methodology. in this i have discussed
what is sampling
why samples for research
sampling methods
size of sample
types of sample
advantages of sample
disadvantages of sample
process
sampling frame
time factor
sampling problems...
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.
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.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. INTRODUCTION
• Sampling is a method or process of selecting respondents or people to answer
questions meant to yield data for field study (Baraceros, 2016)
• Sampling is a method of selecting a subset or individual members of the
population to make statistical inferences from them and estimate characteristics of
the whole population.
• The sample selected should be representative of the population to ensure that we
can generalize the findings from the research sample to the population as a whole.
3. TYPE OF SAMPLING METHODS
• Type of sampling methods can be subdivided into two groups: probability sampling and
non-probability sampling.
• Probability Sampling : It is a sampling technique in which sample from larger population
are chosen using a method based upon theory of probability . For a participants to be
considered as probability sample, he or she must be selected using random selection
(Bhat,2019).
• It start with a complete sampling frame of all eligible individuals from which has been
selected for the sample.
• Non Probability Sampling: It is a sampling technique where samples are gathered in
process that does not give all individuals equal change of being selected.
• It does not start with a complete sampling frame, so some individuals have no chance of
being selected.
4. Type of Probability Sampling
1) Simple Random Sampling
It is a subset of statistical population in which each member of the subset has an equal probability of
being chosen.
Example: Write name in papers and fold then randomly mix and select the names.
This method allows the sampling error to be calculated and reduces selection bias but this simple
random sampling method may not select enough individuals with interest of certain characteristic ,
especially if that characteristic is uncommon.
2)Systematic Sampling
It is a method which sample members from larger population are selected according to a random
starting point and a fixed periodic interval.
Example: Population of 1000 , sample size 100, every 100th person in the list is selected.
Systematic Sampling is easier to administer but may also lead to bias, for example if there are
underlying patterns in the order of the individuals in the sampling frame, such that the sampling
technique coincides with the periodicity of the underlying pattern. For example , choosing every 5th
road user for road hazard in a college would result bias in sample of all males or all females.
5. 3) Stratified Sampling
• It is a type of sampling method in which total populations is divided into a smaller group
or strata to complete the sampling process.
• Example: Population size 1000, Sample size 100, group population by age then get the
sample by age.
• Samples within should be randomly selected for example, in a study of the health
outcomes of nursing staff in Malaysia, if to select from three hospitals, each with
different numbers of nursing staff (hospital A has 500 nurses, hospital B has 1000 and
hospital C has 2000), then it would be appropriate to choose the sample numbers from
each hospital proportionally as 10 nurses from hospital A, 20 nurses from hospital B and
40 nurses from hospital C.
• This ensures a more realistic and accurate estimation of the health outcomes of nurses
across the county by reducing sampling bias but it requires knowledge of the appropriate
characteristics of the sampling frame and it can be difficult to decide which characteristic
to stratify.
6. 4) Cluster Sampling
• It is a sampling method where multiple clusters of people are created from a populations , rather than
individuals where they are indicative of homogeneous characteristics and have an equal chance of
being a part of the sample known as clusters
• Example, Population 10,000, sample size 1000, group the population by age then get the samples of
ages.
• This method can be more efficient that simple random sampling, especially when the population is
large or when it involves subjects residing in large geographic area but if the chosen clusters are not
representative of the population, resulting in an increased sampling error. This would increase the
risk of bias.
7. Types of Non-Probability Sampling Methods
1. Convenience sampling
• Also known as availability, grab, opportunity or accidental sampling and can be considered as easier
method of sampling because participants are selected based on availability and willingness to take
part but the results are prone to significant bias, because those who volunteer to take part may be
different from those who choose not to participate. It creates volunteer bias and the sample may not
be representative of other characteristics, such as age or sex. Example, status of mental disorder
among students, only certain males was willing to participate
2. Quota Sampling.
• It is non probability sampling technique wherein the assembled sample has the same proportions of
individuals as the entire populations. Often used by market researchers as interviewers are given a
quota of subjects of a specified type to attempt to recruit.
• Example, an interviewer might be told to go out and select 20 adult men, 20 adult women, 10
teenage girls and 10 teenage boys so that they could interview them about their television viewing.
Ideally the quotas chosen would proportionally represent the characteristics of the underlying
population. Advantage of being relatively straightforward and potentially representative but the chosen
sample may not be representative of other characteristics that weren’t considered.
8. 3. Voluntary Sampling
Sampling method where people are voluntary to participate in a survey. For example
a game show in the television request the viewers to visit the relevant website and
respond to the online poll. The people who watched the show and understand the game
show will be oversample from the people who don’t understand the game show. This
create respond bias.
4.Purposive Sampling
Also known as Judgement sampling, selective, or subjective sampling as this sampling
method relies on the judgement of the researcher when choosing who to ask to
participate. It is selected based on characteristics of a populations and the purpose of
the study. This approach is often used by the media when canvassing the public for
opinions and in qualitative research.
The advantage of Purposive Sampling are being time-and cost-effective to perform but
volunteer bias is present and it is also prone to errors of judgement by the researcher
and the findings, whilst being potentially broad, will not necessarily be representative.
9. 5.Snowball Sampling
Where research participants recruits other participants for a test or research. Example, when
carrying out a survey of risk behaviors amongst intravenous drug users, participants may be
asked to nominate other users to be interviewed.
Advantages of snowballing is effective when a sampling frame is difficult to identify but
selecting friends of subjects already investigated by choosing a large number of people with
similar characteristics or views will create a risk of selection bias.
10. CONCLUSION
• Sampling is very common phenomenon in decision making process. Before delving
deeply into sampling process, one must be aware of several basic constructs involved with
sampling namely; population, target population, elements, sampling units and sampling
frame. Determining the final sample size for the research involves various qualitative and
quantitative considerations.
• Selecting a suitable sampling methods not only able to reduce cost and time but also
produce a valid and reliable information if the sample size with appropriate method and
bias is taken considerations.