Research, statistics, and psychology are important fields that work together. Research involves studying a topic methodically to discover facts or develop a plan using the scientific method. There are two types of data in research: primary data collected directly by researchers through surveys and interviews, and secondary data previously collected and utilized by others. Statistics is the study of collecting, organizing, analyzing, and presenting data, which allows researchers to reasonably interpret large amounts of information and make sense of uncertainty in their findings. Together, research, primary and secondary data collection, and statistical analysis provide psychologists important tools for answering questions and gaining understanding in their work.
The document discusses the benefits of clinical trial authors submitting supplemental materials and making raw trial data publicly available, such as enabling other researchers to verify results, test secondary hypotheses, and aid the design of future trials, while also outlining some arguments against data sharing and proposing a code of conduct for data sharing. It concludes by suggesting medical journals require data availability for publication to help address issues around researchers restricting access to trial data.
The document discusses the benefits of clinical trial authors submitting supplemental materials and making raw trial data publicly available, such as enabling other researchers to verify results, test secondary hypotheses, and aid the design of future trials, while also outlining some arguments researchers give against data sharing and proposing a code of conduct for data sharing.
Introduction to Systematic Review & Meta-Analysis Hasanain Ghazi
The document discusses systematic reviews and meta-analyses. It defines systematic reviews as a summary of available healthcare studies that provides high-level evidence on healthcare interventions. Meta-analyses use statistical methods to quantitatively summarize results across multiple studies. The document outlines the steps in conducting systematic reviews, including developing a protocol, searching for evidence, assessing risk of bias, and synthesizing findings. It also discusses how meta-analyses can help determine the strength and consistency of effects across studies.
Open science LMU session contribution E Steyerberg 2jul20Ewout Steyerberg
1. The document discusses open science approaches to addressing big research questions through multiple analysts studying the same dataset and research question, as well as data and analysis sharing initiatives.
2. It describes challenges in open science including variation in analyses and interpretations due to heterogeneity across datasets and studies.
3. Initiatives like OHDSI are highlighted as bridging data sharing and analyses while keeping data local, but heterogeneity across data sources and their impact on predictions is still a challenge.
Validity of Instruments, Appropriateness of Designs and Statistics in Article...iosrjce
The main purpose of the study is to appraise the validity of research instruments, the appropriateness
of the research designs and the statistics used for data analyses in articles published in education journals in
Nigeria. Currently, higher institutions in Nigeria tend to prefer articles published in foreign/international
journals. Some researchers in the country also question the validity of some of the articles published in local
journals. Appropriate research designs, valid instrument and appropriate use of statisticals tools are some of
the indices that make research results credible and dependable. To assess these important variables, three
questions were posed. Journal articles published in Nigeria education Journals for last five years were selected
through accidental sampling technique. Then purposive sampling technique was used to select 132 empirical
studies. Empirical studies were selected because they are the studies that lend themselves to use of designs, data
collection with instruments and statistical analysis of data. Appraisal guides for instrument, design and
statistics were used to assess the articles. The results showed that 67% of the articles were carried out with
appropriate research designs, and in 78% of the articles, appropriate statistics were applied in data analyses.
However, only 36% of the instruments used for the study would generate data that can lead to valid
interpretation of the results. Programmes that will enhance knowledge and stills of researchers to improve the
quality of research based publications are recommended. Institutions of higher learning can help their staff in
this direction
This document provides an overview of evidence-based approaches to research support. It discusses the development of systematic reviews and Cochrane reviews. Key aspects of developing a systematic search strategy are covered, including defining the research question using PICO/PECO, developing search terms, combining concepts, and testing the search strategy. The importance of searching multiple sources and record keeping are emphasized to ensure transparency and reproducibility. The goal is to identify all relevant evidence to answer the research question.
This document provides an introduction and overview of systematic reviews. It defines systematic reviews and their key characteristics, including having a clearly defined question and methodology for systematically searching, appraising, and synthesizing the available evidence to answer a specific question. It contrasts systematic reviews with other types of literature reviews and outlines the main steps in planning and conducting a systematic review, including developing a protocol and search strategy.
Research, statistics, and psychology are important fields that work together. Research involves studying a topic methodically to discover facts or develop a plan using the scientific method. There are two types of data in research: primary data collected directly by researchers through surveys and interviews, and secondary data previously collected and utilized by others. Statistics is the study of collecting, organizing, analyzing, and presenting data, which allows researchers to reasonably interpret large amounts of information and make sense of uncertainty in their findings. Together, research, primary and secondary data collection, and statistical analysis provide psychologists important tools for answering questions and gaining understanding in their work.
The document discusses the benefits of clinical trial authors submitting supplemental materials and making raw trial data publicly available, such as enabling other researchers to verify results, test secondary hypotheses, and aid the design of future trials, while also outlining some arguments against data sharing and proposing a code of conduct for data sharing. It concludes by suggesting medical journals require data availability for publication to help address issues around researchers restricting access to trial data.
The document discusses the benefits of clinical trial authors submitting supplemental materials and making raw trial data publicly available, such as enabling other researchers to verify results, test secondary hypotheses, and aid the design of future trials, while also outlining some arguments researchers give against data sharing and proposing a code of conduct for data sharing.
Introduction to Systematic Review & Meta-Analysis Hasanain Ghazi
The document discusses systematic reviews and meta-analyses. It defines systematic reviews as a summary of available healthcare studies that provides high-level evidence on healthcare interventions. Meta-analyses use statistical methods to quantitatively summarize results across multiple studies. The document outlines the steps in conducting systematic reviews, including developing a protocol, searching for evidence, assessing risk of bias, and synthesizing findings. It also discusses how meta-analyses can help determine the strength and consistency of effects across studies.
Open science LMU session contribution E Steyerberg 2jul20Ewout Steyerberg
1. The document discusses open science approaches to addressing big research questions through multiple analysts studying the same dataset and research question, as well as data and analysis sharing initiatives.
2. It describes challenges in open science including variation in analyses and interpretations due to heterogeneity across datasets and studies.
3. Initiatives like OHDSI are highlighted as bridging data sharing and analyses while keeping data local, but heterogeneity across data sources and their impact on predictions is still a challenge.
Validity of Instruments, Appropriateness of Designs and Statistics in Article...iosrjce
The main purpose of the study is to appraise the validity of research instruments, the appropriateness
of the research designs and the statistics used for data analyses in articles published in education journals in
Nigeria. Currently, higher institutions in Nigeria tend to prefer articles published in foreign/international
journals. Some researchers in the country also question the validity of some of the articles published in local
journals. Appropriate research designs, valid instrument and appropriate use of statisticals tools are some of
the indices that make research results credible and dependable. To assess these important variables, three
questions were posed. Journal articles published in Nigeria education Journals for last five years were selected
through accidental sampling technique. Then purposive sampling technique was used to select 132 empirical
studies. Empirical studies were selected because they are the studies that lend themselves to use of designs, data
collection with instruments and statistical analysis of data. Appraisal guides for instrument, design and
statistics were used to assess the articles. The results showed that 67% of the articles were carried out with
appropriate research designs, and in 78% of the articles, appropriate statistics were applied in data analyses.
However, only 36% of the instruments used for the study would generate data that can lead to valid
interpretation of the results. Programmes that will enhance knowledge and stills of researchers to improve the
quality of research based publications are recommended. Institutions of higher learning can help their staff in
this direction
This document provides an overview of evidence-based approaches to research support. It discusses the development of systematic reviews and Cochrane reviews. Key aspects of developing a systematic search strategy are covered, including defining the research question using PICO/PECO, developing search terms, combining concepts, and testing the search strategy. The importance of searching multiple sources and record keeping are emphasized to ensure transparency and reproducibility. The goal is to identify all relevant evidence to answer the research question.
This document provides an introduction and overview of systematic reviews. It defines systematic reviews and their key characteristics, including having a clearly defined question and methodology for systematically searching, appraising, and synthesizing the available evidence to answer a specific question. It contrasts systematic reviews with other types of literature reviews and outlines the main steps in planning and conducting a systematic review, including developing a protocol and search strategy.
The document outlines the process of research according to Anjali Rai. It describes research as the systematic collection and analysis of information to increase understanding of a topic. It then lists the 11 steps in the management research process: 1) formulating the research problem, 2) extensive literature review, 3) developing working hypotheses, 4) preparing the research design, 5) determining the sample design, 6) collecting data, 7) executing the project, 8) analyzing data, 9) testing hypotheses, 10) generalizing and interpreting results, and 11) preparing the report. Each step is then further explained in bullet points providing details of the process.
How to handle discrepancies while you collect data for systemic review – pubricaPubrica
1. Population specification error:
2. Sample error:
3. Selection error:
4. Non- response error:
Continue Reading: https://bit.ly/36i7iYo
For our services: https://pubrica.com/services/research-services/systematic-review/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Evaluation of the clinical value of biomarkers for risk predictionEwout Steyerberg
This document summarizes a presentation on evaluating the clinical value of lipidomics biomarkers for risk prediction after traumatic brain injury (TBI). It discusses key challenges in using lipidomics for prediction, including defining static and dynamic lipid biomarkers, assessing incremental predictive value over other factors, performing valid internal and external validation, and demonstrating clinical utility. Validation is important to address overfitting and evaluate generalizability. The goal is to determine if lipidomics biomarkers can improve individualized predictions and clinical decision-making for patients with TBI.
This document provides information about engineering projects and research. It defines engineering projects as the development of software or hardware to solve engineering problems. It lists example projects for computer science and electrical engineering students. The objectives of projects are outlined as function, cost containment, and timeline. Research is defined as systematic inquiry to describe, explain, predict, and control observed phenomena using inductive and deductive methods. Pure and applied research are described, with pure research advancing knowledge without specific goals and applied research analyzing real-world problems. The goals of exploratory, descriptive, explanatory, and correlation research are summarized. Effective hypothesis formation and the importance of testable hypotheses are discussed. Steps for literature reviews are provided, including selecting topics, setting contexts, and
QUANTIFYING THE IMPACT OF DIFFERENT APPROACHES FOR HANDLING CONTINUOUS PREDIC...GaryCollins74
Continuous predictors are often dichotomized or categorized in prognostic models, despite recommendations against this practice. This study investigated the impact of different approaches to handling continuous predictors on model performance and validation. The researchers found that dichotomizing continuous predictors, either at the median or an "optimal" cut-point, led to substantially worse model discrimination, calibration, and clinical utility compared to analyzing predictors linearly or with fractional polynomials. The negative impact of dichotomizing was more pronounced at smaller sample sizes. Maintaining continuous predictors yielded better prognostic performance and validation than dichotomizing.
This document provides tips for increasing citations of one's research, including using succinct titles with colons and carefully chosen keywords, publishing review articles and open access papers, presenting work at conferences, promoting work on social media and altmetrics platforms, and publishing on hot topics. Consistently formatting one's name and only citing previous relevant work can also help increase citations.
The document discusses the expectations publishers and authors have of each other in the academic publishing process. Publishers expect authors to submit high quality manuscripts on relevant topics and to cite and promote journals to increase their impact factor and readership. Authors expect publishers to provide encouragement, communicate clearly about manuscripts, and market their work professionally. Both seek to maintain standards and further scholarly communication through the peer review and publication of research.
This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
Research means an objective and systematic search for pertinent information on a specific topic.
Research has to be an original contribution to the existing stock of knowledge.
What will it take for patients and clinicians to use data from mobile health apps and sensors in routine care? Watch how Linq, a new product from Open mHealth, offers a new "bring your own app" approach that puts the focus back on patients and clinicians rather than on technology.
A presentation covering research fraud, and some basic concepts for interpreting papers. The presentation was made at the annual congress of PainSA, Johannesburg, South Africa, 2015.
This module was developed at the School of Public Health, University for the Western Cape for the Postgraduate Certificate in Public Health which was offered as a distance learning module between 2001 and 2008. Health Systems Research is an integral part of the vision for a quality, comprehensive, community-based, participatory and equitable system. This module aims to provide an introduction to the kinds of research conducted within a health system, the research designs and methods used, and how to develop a research protocol.
Author(s): Mickey Chopra, John Coveney
Institution(s): University of the Western Cape
This resource is part of the African Health Open Educational Resources Network: http://www.oerafrica.org/healthoer. The original resource is also available from the authoring institution at http://freecourseware.uwc.ac.za/
Creative Commons license: Attribution-Noncommercial-Share Alike 3.0
The document discusses different types of research methods including quantitative, qualitative, and mixed methods research. Quantitative research uses objective measurements and statistical analysis, while qualitative research explores underlying reasons and motivations through methods like interviews. Mixed methods research incorporates both quantitative and qualitative data collection. The document also describes observational studies like case studies and longitudinal studies, as well as experimental research methods like randomized controlled trials that manipulate variables and use control groups.
Big Data: Big Opportunities or Big Trouble?Shea Swauger
Big data is changing how research is being conducted and allowing new kinds of questions to be asked. Meanwhile, data management has enabled a rapid increase in the dissemination and preservation of research products and many funding agencies like the National Science Foundation and National Institute of Health now require data management plans in their grant applications. The combination of big data applications and data management processes has created new opportunities and pitfalls for researchers. In the past year, prominent scientists including the Director of the NIH have suggested that inappropriate methodology for data acquisition, analysis and storage has led to a gap in the translation of basic research findings to clinical cures. In this session we will track data through all research stages, describe best practices and university resources available to faculty grappling with these important issues.
The document discusses the role and responsibilities of statisticians in clinical trials. It notes that statisticians can help at all stages of clinical trials from study design to analysis and reporting. Specifically, statisticians can help choose appropriate study designs, determine sample sizes, implement randomization and blinding procedures, monitor trial safety and conduct interim analyses, and analyze and interpret trial results. The document emphasizes that involving statisticians early in the research process allows them to provide valuable input that can improve study design and ensure the research question is properly addressed.
Cemal H. Guvercin MedicReS 5th World Congress MedicReS
Ethical Issues in Artifical Intelligence Applied to Medicine Presentation to MedicReS 5th World Congress on October 19,25,2015 in New York by Cemal H. Guvercin, MD, PhD
How to structure your table for systematic review and meta analysis – PubricaPubrica
According to the, a systematic review is "a scholarly method in which all empirical evidence that meets pre-specified eligibility requirements is gathered to address a particular research question."
Continue Reading: https://bit.ly/3AeFIYY
For our services: https://pubrica.com/services/research-services/systematic-review/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Presentation by Dr Davina Ghersi, NHMRC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Pandemic Preparedness Results and Recommendations.pdfbkbk37
This chapter discusses the findings and recommendations from a study on pandemic preparedness. The study used a cohort study design to assess preparedness levels in local hospitals. A questionnaire was administered to emergency management coordinators to collect data on facility planning, workforce capacity, and surge capacity. Qualitative data was also collected through interviews. The results showed both strengths and limitations in pandemic plans and capacity. Recommendations include continued planning and identification of gaps to improve readiness for future pandemics.
The document outlines the process of research according to Anjali Rai. It describes research as the systematic collection and analysis of information to increase understanding of a topic. It then lists the 11 steps in the management research process: 1) formulating the research problem, 2) extensive literature review, 3) developing working hypotheses, 4) preparing the research design, 5) determining the sample design, 6) collecting data, 7) executing the project, 8) analyzing data, 9) testing hypotheses, 10) generalizing and interpreting results, and 11) preparing the report. Each step is then further explained in bullet points providing details of the process.
How to handle discrepancies while you collect data for systemic review – pubricaPubrica
1. Population specification error:
2. Sample error:
3. Selection error:
4. Non- response error:
Continue Reading: https://bit.ly/36i7iYo
For our services: https://pubrica.com/services/research-services/systematic-review/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Evaluation of the clinical value of biomarkers for risk predictionEwout Steyerberg
This document summarizes a presentation on evaluating the clinical value of lipidomics biomarkers for risk prediction after traumatic brain injury (TBI). It discusses key challenges in using lipidomics for prediction, including defining static and dynamic lipid biomarkers, assessing incremental predictive value over other factors, performing valid internal and external validation, and demonstrating clinical utility. Validation is important to address overfitting and evaluate generalizability. The goal is to determine if lipidomics biomarkers can improve individualized predictions and clinical decision-making for patients with TBI.
This document provides information about engineering projects and research. It defines engineering projects as the development of software or hardware to solve engineering problems. It lists example projects for computer science and electrical engineering students. The objectives of projects are outlined as function, cost containment, and timeline. Research is defined as systematic inquiry to describe, explain, predict, and control observed phenomena using inductive and deductive methods. Pure and applied research are described, with pure research advancing knowledge without specific goals and applied research analyzing real-world problems. The goals of exploratory, descriptive, explanatory, and correlation research are summarized. Effective hypothesis formation and the importance of testable hypotheses are discussed. Steps for literature reviews are provided, including selecting topics, setting contexts, and
QUANTIFYING THE IMPACT OF DIFFERENT APPROACHES FOR HANDLING CONTINUOUS PREDIC...GaryCollins74
Continuous predictors are often dichotomized or categorized in prognostic models, despite recommendations against this practice. This study investigated the impact of different approaches to handling continuous predictors on model performance and validation. The researchers found that dichotomizing continuous predictors, either at the median or an "optimal" cut-point, led to substantially worse model discrimination, calibration, and clinical utility compared to analyzing predictors linearly or with fractional polynomials. The negative impact of dichotomizing was more pronounced at smaller sample sizes. Maintaining continuous predictors yielded better prognostic performance and validation than dichotomizing.
This document provides tips for increasing citations of one's research, including using succinct titles with colons and carefully chosen keywords, publishing review articles and open access papers, presenting work at conferences, promoting work on social media and altmetrics platforms, and publishing on hot topics. Consistently formatting one's name and only citing previous relevant work can also help increase citations.
The document discusses the expectations publishers and authors have of each other in the academic publishing process. Publishers expect authors to submit high quality manuscripts on relevant topics and to cite and promote journals to increase their impact factor and readership. Authors expect publishers to provide encouragement, communicate clearly about manuscripts, and market their work professionally. Both seek to maintain standards and further scholarly communication through the peer review and publication of research.
This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
Research means an objective and systematic search for pertinent information on a specific topic.
Research has to be an original contribution to the existing stock of knowledge.
What will it take for patients and clinicians to use data from mobile health apps and sensors in routine care? Watch how Linq, a new product from Open mHealth, offers a new "bring your own app" approach that puts the focus back on patients and clinicians rather than on technology.
A presentation covering research fraud, and some basic concepts for interpreting papers. The presentation was made at the annual congress of PainSA, Johannesburg, South Africa, 2015.
This module was developed at the School of Public Health, University for the Western Cape for the Postgraduate Certificate in Public Health which was offered as a distance learning module between 2001 and 2008. Health Systems Research is an integral part of the vision for a quality, comprehensive, community-based, participatory and equitable system. This module aims to provide an introduction to the kinds of research conducted within a health system, the research designs and methods used, and how to develop a research protocol.
Author(s): Mickey Chopra, John Coveney
Institution(s): University of the Western Cape
This resource is part of the African Health Open Educational Resources Network: http://www.oerafrica.org/healthoer. The original resource is also available from the authoring institution at http://freecourseware.uwc.ac.za/
Creative Commons license: Attribution-Noncommercial-Share Alike 3.0
The document discusses different types of research methods including quantitative, qualitative, and mixed methods research. Quantitative research uses objective measurements and statistical analysis, while qualitative research explores underlying reasons and motivations through methods like interviews. Mixed methods research incorporates both quantitative and qualitative data collection. The document also describes observational studies like case studies and longitudinal studies, as well as experimental research methods like randomized controlled trials that manipulate variables and use control groups.
Big Data: Big Opportunities or Big Trouble?Shea Swauger
Big data is changing how research is being conducted and allowing new kinds of questions to be asked. Meanwhile, data management has enabled a rapid increase in the dissemination and preservation of research products and many funding agencies like the National Science Foundation and National Institute of Health now require data management plans in their grant applications. The combination of big data applications and data management processes has created new opportunities and pitfalls for researchers. In the past year, prominent scientists including the Director of the NIH have suggested that inappropriate methodology for data acquisition, analysis and storage has led to a gap in the translation of basic research findings to clinical cures. In this session we will track data through all research stages, describe best practices and university resources available to faculty grappling with these important issues.
The document discusses the role and responsibilities of statisticians in clinical trials. It notes that statisticians can help at all stages of clinical trials from study design to analysis and reporting. Specifically, statisticians can help choose appropriate study designs, determine sample sizes, implement randomization and blinding procedures, monitor trial safety and conduct interim analyses, and analyze and interpret trial results. The document emphasizes that involving statisticians early in the research process allows them to provide valuable input that can improve study design and ensure the research question is properly addressed.
Cemal H. Guvercin MedicReS 5th World Congress MedicReS
Ethical Issues in Artifical Intelligence Applied to Medicine Presentation to MedicReS 5th World Congress on October 19,25,2015 in New York by Cemal H. Guvercin, MD, PhD
How to structure your table for systematic review and meta analysis – PubricaPubrica
According to the, a systematic review is "a scholarly method in which all empirical evidence that meets pre-specified eligibility requirements is gathered to address a particular research question."
Continue Reading: https://bit.ly/3AeFIYY
For our services: https://pubrica.com/services/research-services/systematic-review/
Why Pubrica:
When you order our services, We promise you the following – Plagiarism free | always on Time | 24*7 customer support | Written to international Standard | Unlimited Revisions support | Medical writing Expert | Publication Support | Biostatistical experts | High-quality Subject Matter Experts.
Contact us:
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44-1618186353
Presentation by Dr Davina Ghersi, NHMRC, to the 'Unlocking value from publicly funded Clinical Research Data' workshop, cohosted by ARDC and CSIRO at ANU on 6 March 2019.
Pandemic Preparedness Results and Recommendations.pdfbkbk37
This chapter discusses the findings and recommendations from a study on pandemic preparedness. The study used a cohort study design to assess preparedness levels in local hospitals. A questionnaire was administered to emergency management coordinators to collect data on facility planning, workforce capacity, and surge capacity. Qualitative data was also collected through interviews. The results showed both strengths and limitations in pandemic plans and capacity. Recommendations include continued planning and identification of gaps to improve readiness for future pandemics.
Covid 19 methods of data collection-sharoon mushtaqShawn Mad
This document discusses different methods for collecting data in research. It defines data as facts or information gathered for a study. Data collection involves gathering information to answer research questions through primary or secondary sources. Primary methods include interviews, observations, questionnaires, focus groups, and experiments, which involve directly collecting original data. Secondary methods use previously collected data from sources like books, websites, journals and government reports. The document also distinguishes between qualitative and quantitative data, where qualitative data describes attributes and quantitative data uses numbers that can be measured.
Journal Club - Best Practices for Scientific ComputingBram Zandbelt
This document discusses the importance of best practices in scientific computing. It notes that scientists rely heavily on software for research, with many writing their own code. However, most scientists are self-taught in software skills and may be unaware of best practices that could help them write more reliable and maintainable code. The document advocates treating software like a scientific instrument and following practices such as version control, testing, and automation. Adopting these practices could help reduce errors and make software easier to reuse.
The document provides an overview of research methodology in 9 steps:
1. Identifying an area of interest or problem to study.
2. Conducting a literature review to understand previous work.
3. Defining clear objectives based on gaps in knowledge.
4. Developing a methodology to achieve the objectives, including study design.
5. Stating a hypothesis or research question.
6. Establishing the importance and rationale of the study.
7. Creating a detailed methodology plan involving statistics experts.
8. Ensuring valid conclusions can be drawn from the methods.
9. Determining feasibility considering time, resources, and necessary approvals.
Running Head: WEEK 1 1
WEEK 1 4
Analysis of Asthma Patients
Course
March 25, 2020
Introduction:
Information processing is any method by which the retrieval or assistance in the retrieval of information is planned. Selection is accomplished by seeking and receiving the necessary data from persons or organizations via the correct vehicle. The data is given explicitly by the respondent (self-enumeration) or by the investigator. Set also involves the retrieval of institutional details. Data collection applies to any mechanism that transforms the information given to the respondent into an electronic format. The process is either automatic or requires the workers to plugging the gathered data (keys). Data coding is any method that assigns a numeric value to an answer. Programming is frequently automatic, but more complicated decisions typically need human input (coders). Survey operations also require a large level of optimization, which contributes to the accessibility of data, knowledge relevant to the survey phase. Instances of para data include an indication of whether or not a device is in the survey, a list of calls and meetings, a record of keystrokes (audit record), a system of compilation, managerial details (e.g. interview blog) and expense details. Data is not just a source of statistics, it is also the primary interaction that a polling organization has with the population who wants to be encouraged to participate. Data collection and encoding are the structured data for use as output by all future survey operations. Data processing, data analysis, and coding activities frequently entail a substantial portion of the research expenditure and require considerable humor. Plan the collection procedure to reduce the stress on the participant and the expense of processing, and to optimize timeliness and quality of the results. Data may be obtained by self-reporting, voice interviews or informal interviews through either a document or an online survey (e.g. automated data recording, the Web, computer-assisted interviewing).
What is PHIS?
Global Health Information Systems (PHIS) are the essential elements of public health services, offering details about how community programs obtain and manage public health treatment data. Such results help public health initiatives, such as the monitoring of illnesses or the implementation of public health systems for teen smokers. Countries also create PHIS via the state health department in order to collect data that will be used relevantly to assess the health condition of the country. In this report, the PHIS toolkit will be used in order to analyze the patients of asthma, and database records have been collected.
Asthma Database Analyses:
Create effective sample management protocols and controls for all data gathering .
Running Head: WEEK 1 1
WEEK 1 4
Analysis of Asthma Patients
Course
March 25, 2020
Introduction:
Information processing is any method by which the retrieval or assistance in the retrieval of information is planned. Selection is accomplished by seeking and receiving the necessary data from persons or organizations via the correct vehicle. The data is given explicitly by the respondent (self-enumeration) or by the investigator. Set also involves the retrieval of institutional details. Data collection applies to any mechanism that transforms the information given to the respondent into an electronic format. The process is either automatic or requires the workers to plugging the gathered data (keys). Data coding is any method that assigns a numeric value to an answer. Programming is frequently automatic, but more complicated decisions typically need human input (coders). Survey operations also require a large level of optimization, which contributes to the accessibility of data, knowledge relevant to the survey phase. Instances of para data include an indication of whether or not a device is in the survey, a list of calls and meetings, a record of keystrokes (audit record), a system of compilation, managerial details (e.g. interview blog) and expense details. Data is not just a source of statistics, it is also the primary interaction that a polling organization has with the population who wants to be encouraged to participate. Data collection and encoding are the structured data for use as output by all future survey operations. Data processing, data analysis, and coding activities frequently entail a substantial portion of the research expenditure and require considerable humor. Plan the collection procedure to reduce the stress on the participant and the expense of processing, and to optimize timeliness and quality of the results. Data may be obtained by self-reporting, voice interviews or informal interviews through either a document or an online survey (e.g. automated data recording, the Web, computer-assisted interviewing).
What is PHIS?
Global Health Information Systems (PHIS) are the essential elements of public health services, offering details about how community programs obtain and manage public health treatment data. Such results help public health initiatives, such as the monitoring of illnesses or the implementation of public health systems for teen smokers. Countries also create PHIS via the state health department in order to collect data that will be used relevantly to assess the health condition of the country. In this report, the PHIS toolkit will be used in order to analyze the patients of asthma, and database records have been collected.
Asthma Database Analyses:
Create effective sample management protocols and controls for all data gathering .
Project Estimation Techniques And Methods For The Data...Jennifer Baker
1. The document discusses project estimation techniques and methods for collecting data on projects in Dubai by following UK methodologies. It will use a cross-sectional research design and qualitative data collection methods like questionnaires.
2. It explores different data collection methods for statistical analysis projects, including surveys, experiments, and self-reports. It also discusses quantitative and qualitative research approaches.
3. It evaluates research methods and data collection, referencing the LOTS of data framework - life records, observations, tests, and self-reports. It also discusses nomothetic and ideographic measures.
Data Presentation & Analysis Meaning, Stages of data analysis, Quantitative & Qualitative data analysis methods, Descriptive & inferential methods of data analysis
1) The document discusses techniques for collecting quantitative data, including observation, surveys using questionnaires and interviews, experiments, content analysis, and psychological and physiological measures. It emphasizes the importance of using accurate and appropriate data collection techniques.
2) Hypothesis testing is covered, including the types of hypotheses (descriptive and statistical), testing for significance, and the steps involved which include stating the problem, hypothesis, statistical tool, analyzing data, and interpreting results to draw conclusions.
3) The differences between one-tailed and two-tailed hypothesis tests are explained. One-tailed tests specify the direction of the relationship while two-tailed tests do not. The level of significance and determining critical values are also important aspects of hypothesis
Research Evaluation And Data Collection MethodsJessica Robles
The document discusses research evaluation and data collection methods used by Healthy People 2020, a US government program that sets national health objectives. It identifies measuring objectives, increasing public awareness of health determinants, providing measurable goals, engaging stakeholders, and identifying research methods as key missions. Healthy People 2020 aims to improve quality and length of life, achieve health equity, create health-promoting environments, and promote well-being across all life stages. One focus area is reducing the disease and economic burden of diabetes and improving quality of life for those with diabetes.
1
Methods and Statistical Analysis
Name xxx
United State University
Course xxx
Professor xxxx
Date xxx
The Evaluative Criteria
The process of analyzing a healthcare plan to see if it meets its goals takes some time. Because it promotes an evidence-based approach, assessment is crucial in practice consignment. Evaluation can be used to assess the effectiveness of the research. It helps determine what changes could be recommended to improve service delivery and the study's persuasiveness. An impact evaluation analyzes the intervention's direct and indirect, positive and negative, planned and unplanned consequences. If an evaluation fails to deliver fresh recognition regularly, it may result in inaccurate results and conclusions. A healthcare practitioner can utilize the indicators or variables to evaluate programs and determine whether they are legal or not (Dash et al., 2019). The variables are also used to assess if the mediation is on track to meet its objectives and obligations. Participation rates, prevalence, and individual behaviors are among the measures to be addressed.
Individual behaviors are actions taken by individuals to improve their health. People have been denied the assistance and resources they seek because of ethics and plans. In addition, different people have varied perspectives about pressure ulcers treatment. Relevance refers to how the study may contribute to a worthwhile cause (Li et al., 2019). Quality variables give statistics on the precariously rising service consignment while also attempting to provide information on the part of the care that may be changed. The participation rate refers to the total number of people participating in the study.
On the other hand, individuals may be unable to engage in the study due to a lack of cultural knowledge and ineffective consent processes. The overall number of persons in a population who have a health disease at a given time is referred to as prevalence (Li et al., 2019). Although prevalence shows the rate at which new facts arrive, it aids in determining the suitable, complete outcome-positive prestige of people.
Research Approaches
The word "research approaches" refers to techniques and procedures to draw general conclusions concerning data collection, analysis, and explanation methods. In my research, I'll employ both quantitative and qualitative methods. A qualitative research technique will reveal deterrents and hindrances to practicing change by rationalizing the reasons behind specific demeanors (Li et al., 2019). Qualitative research will collect and evaluate non-numerical data to comprehend perspectives or opinions. It will also be utilized to learn everything there is to know about a subject or to develop new research ideologies.
The quantitative method focuses on goal data and statistical or numerical analysis of data collected through a questionnaire. In the healthcare field, quantitative research may develop and execute new or enhanced work meas ...
The field of statistics is the study of learning from data. Statistical learning causes you to utilize the best possible strategies to gather the information, utilize the right investigations, and adequately present the outcomes
This systematic review analyzed 5 case-control studies to determine if bicycle helmets reduce head, brain, and facial injuries. The studies found that helmets provide a 63-88% reduction in the risk of head, brain, and severe brain injuries for bicyclists of all ages. Helmets also provide a 65-88% reduction in the risk of facial injuries. The review concludes that bicycle helmets effectively reduce injuries to the head and face for bicyclists involved in crashes or falls.
Tugas 1_Septiani Wulandari_engineering.pptxEriskaAgustin
The document summarizes chapters from a book on research design and methodology. It covers topics such as data preparation and analysis, descriptive and inferential statistics, ethical considerations in research including informed consent and institutional review boards, and disseminating research results. The overall aim is to provide guidance to researchers on key aspects of conducting research studies ethically and following established practices.
This document outlines an agenda and case studies for a healthcare analytics bootcamp. The bootcamp will use healthcare data to develop machine learning solutions to predict heart disease and identify high-risk patients. Case Study 1 will involve exploratory data analysis of tuberculosis data to analyze global trends, hotspots, and mortality rates. Case Study 2 will use a heart disease screening dataset and logistic regression to build a model to predict heart disease risk and develop treatment plans for high-risk patients. The document discusses the types of structured and unstructured healthcare data, sources of data, and applications of machine learning in healthcare analytics.
This document discusses key concepts in statistical and critical thinking. It defines important statistical terms like population, sample, data, parameters and statistics. It explains how to analyze sampling data by considering the context, source and sampling methods. It also distinguishes between statistical significance, which indicates unlikely results, and practical significance, which considers if findings make an meaningful difference. The document provides examples of how to identify populations and samples, and outlines the steps of preparing, analyzing and concluding when doing statistics. It discusses issues with voluntary response samples and concludes with an example comparing statistical and practical significance.
This document outlines the requirements for a research proposal to investigate whether there is an association between body mass index (BMI) and asthma. The proposal must include:
1) A justification for the chosen study design over other options.
2) Selection of a statistical measure to describe any association between BMI and asthma.
3) Details on subject selection, measurement of exposure/outcome, and potential biases and how to address them.
4) Consideration of possible confounding factors and effect modifiers.
The proposal must be 750-1000 words and address the key elements outlined, including study design, statistical measures, subject selection, and measurement issues. At least three scholarly sources must be referenced.
This document provides an overview of evidence-based strategies for preventing opioid overdose that are working in the United States. It begins with an introduction describing the purpose and creation of the document. It then outlines four guiding principles for effective overdose prevention strategies: 1) Know your epidemic and response; 2) Make collaboration your strategy; 3) Nothing about us without us; and 4) Meet people where they are. The document concludes by describing nine evidence-based strategies for preventing overdose, with the first being targeted naloxone distribution. Targeted naloxone distribution programs equip individuals at high risk of witnessing an overdose, such as people who use drugs and first responders, with naloxone kits to
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
2. Principles of Data science
Essential
steps
1. ResearchTopic
2. Research Question
3. Hypothesis
4.Data collection plan
5. Data analysis
6.Data Reporting
Research Question
Hypothesis
Experiment/
Data collection plan
Data Analysis
Conclusion/
Data Reporting
Replication
3. Principles of Data science
Research
Topic
Example:
First responders long term health is at risk when involved in
combating wildfire for several years.
Can monitoring individual emission exposure, help manage long
term health risks and extend their active life?
A problem or a need statement with a broad area of interest
Majority of First responders suffer from Cardiac Arrest andTrauma
4. Principles of Data science
Research
Question
A clearly articulated list of specific research question will define the
data types required to collect.
Example:
RQ1. Are toxic emissions negatively associated with long-term health?
RQ2.Are the current data collection measures, useful in monitoring the individual
emission burden?
RQ3. Are the current methods of Health risk assessments accurate?
5. Principles of Data science
Hypothesis
Example:
Ho3: Current methods of Health risk assessments are effective.
Ha3: Current methods of Health risk assessments are not sufficient.
H0: null hypothesis is a general statement or default position that there is
no relationship between two measured phenomena, or no association
among groups.
Ha: The alternative hypothesis is the hypothesis used
in hypothesis testing that is contrary to the null hypothesis.
H0
Ha
6. Principles of Data science
Hypothesis
What is
Type I error
Type II error
Hypothesis
Ho: Current Health Risk
Assessments are effective in
associating to toxic emission
(isTrue)
Ho: Current Health Risk Assessments are
effective in associating to toxic emission
(is False)
Reject Ho TYPE I Error
Correct Conclusion
(p < 0.05)
Fail to Reject Ho
Correct Conclusion
(p >= 0.05)
Type II Error
For Example:
7. Principles of Data science
Data
Collection Plan
Type of Data
1. Act, Behavior, or Events
2. Economic data
3. Organizational data
4. Demographic data
5. Self-identity
6. Cultural knowledge
7. Expert knowledge
8. Personal and psychological traits
9. Hidden social patters
Data Location
Operational
Definition
8. Principles of Data science
Data
Collection Plan
Dataset Who What Why Where When
Firefighters
Dataset
Firefighters
research associate
Wildfire events and
firefighter’s data
To assess the
emission exposure
The National
Institute for
Occupational
Safety and Health
(NIOSH)
For the period 2008
to 2018
Health Report
Dataset
Health report
research associate
Firefighters health
records
To capture the
disease diagnosis
Search Firefighter
fatalities in the
United States
For the period 2008
to 2018
Data Collection plan for Firefighters dataset
9. Principles of Data science
Data
Collection Plan
Sampling techniques
Simple random sample
Clustered sampling
Representative subgroup sampling
Possible sources of uncertainty
Sampling Error
Researcher Bias
Validity of Instrument
10. Principles of Data science
Data
Management
Themes of concerns of big data
Growing data
Real-time can be Complex
Data Security
SQL NoSQL
• Relational,Tabular format
• Schema is essential
• GrowVertically
• Unstructured, Semi structured
• No schema
• Grow horizontally
TYPES OF DATA STORAGE (Key Differences)
Example of SQL database: MySql,Oracle, SQLite, Postgres, and MS-SQL.
Examples of NoSQL database: MongoDB, BigTable, Redis, RavenDb, Cassandra,
HBase, Neo4j, and CouchDB
11. Principles of Data science
DataAnalysis
Flow of data
based on its type
to create insights
Categorical OrdinalInterval-Ratio/
Continuous
Calculate
Frequency,
Distribution
Calculate
Mode
Calculate
Mean,
Median, SD
Vary
Report No
change
No
T-Test | Chi-Squared | Correlation | OLS Regression | Logistic Regression
Report Table, Pie chart, Bar chart
Yes
Descriptive
Statistics
Inferential
Statistics
13. Principles of Data science
DataAnalysis
Exploratory Data Analysis
Descriptive statistics on Health Risk ,
Emission level, Exposure duration and
Age
14. Principles of Data science
Data
Reporting
The most common data reporting formats in business are as follows:
Research
Report
Executive
Summary
Short
Answers
Slide
Presentation
White Paper
15. Principles of Data science
Summary
Basic research design consists of six core steps:
Develop a good research question, identifying a small section of
wider topic that is worth exploring.
Choose a logical structure for research.
Identify the type of data needed.
Select a data collection method.
Choose data collection site, the data source.
The research question, the type of data, and the data collection
method together leads us to the correct data analysis method to
use.
16. Principles of Data science
Ethics in Data
Science
A detailed Informed consent form with the scope of
the research and a transparent method with only
the required information will be collected.
When accessing the first responder's information,
utmost care will be given to maximize benefits and
minimize harm.
For the most part, this research should enable
interventions that are designed solely to enhance
the mental being of an individual firefighter or
subject and that have a reasonable expectation of
success.
All participants will get equal treatment, and every
measurement will be analyzed with the same
method without any bias.
The assessment of risk and benefits requires a
careful collection of relevant data or any alternate
way of obtaining the benefits sought in the
research.
Informed
Consent
Maximize
benefit
Enhance
Wellbeing
Equal
Treatment
Risk vs Benefit
Editor's Notes
This slide deck was created to demonstrate my learnings in this course and some of the interesting observations are included to show my level of understanding.
The essential steps of data science research are discussed in this presentation. All six steps discussed here ensure all critical elements are considered in the research process and provide a clear insight for any other researchers to learn.
The six steps are,
Research Topic: Describes a problem or need statement
Research Question: A precise list of questions that directly gives clues on the data type, unit of measure, and data source.
Hypothesis: Clearly defines the relationship between the variable. It starts with the baseline assumption that there is no relationship between the independent variable and the dependent variable.
Data collection plan: A suitable and successful method of collecting the data by following the right sampling methods
Data analysis: Descriptive and Inferential statistics performed on the collected data
Data Reporting: Discuss various reporting techniques for varying levels of audiences.
In recent decades, the Western United States has seen heightened wildfire activity, characterized by a higher frequency of massive wildfires, a more extended fire season, larger fire size, and a higher total area burned. With projected temperature increases, soil moisture reduction, and more frequent air stagnation, the burden of wildfires on air quality, public health, and environmental management will likely increase. With state-of-the-art wearable sensors, AI models, and detailed health information, we propose to investigate the impacts of historical and future wildfires on first respondents long term health risks.
RQ1. Are toxic emissions negatively associated with long-term health?
Study the levels of toxic emissions from past wildfire events and map it to the health records of the first responders to identify any correlation in the data sets. What are the health risks associated with this occupation?
RQ2. Are the current data collection measures, useful in monitoring the individual emission burden?
Study the current data collection methods and evaluate their effectiveness in monitoring individual fire fighter's emission burden. Establish the correlation of current methods and their effectiveness in calculating the duration of emission burden.
RQ3. Are the current methods of Health risk assessments accurate?
What are the different methods used in calculating the health risks and how a specific toxic emission is associated with a Health Risk? What are the thresholds of the Emission burden?
Ho1: Toxic emissions do not affect the long-term health risk
Ha1: Toxic emissions have a negative association with the health risk
Ho2: Current data collection methods are not effective in calculating individual emission burden of the firefighters.
Ha2: Current data collection methods are useful in calculating individual emission burden
Ho3: Current methods of Health risk assessments are not accurate.
Ha3: Current methods of Health risk assessments are accurate.
Type I error is the rejection of a true null hypothesis.
Type II error is the failure of rejecting a false null hypothesis.
In the example
The p-value is > 0.05, the firefighters with longer hours of work in a toxic emission had a higher incidence of health disorder. The null hypothesis was accepted with the conclusion that the methods of health risk assessment are beneficial in associating with the toxic emission.
Based on the formulated research questions, retrospective analysis of various wildfire events for the last ten years and an anonymized list of fire fighter's health records are required. Careful selection of both quantitative and qualitative data from specific wildfire events with a duration of containment, level of emission, type of sensor used, firefighters age, shift schedules, reported Injuries and pre-existing conditions need to be collected. Longer-Term details of specific health records related to firefighter's hospital visits, insurance claims information and medicine prescription information, diagnosis date, and diagnosis details need to be collected.
From the data source, a set of vital information will be extracted for each of the wildfire events.
An event is a specific wildfire incident that burnt at least more than 1000 acres or produced significant structural damage or loss of life.
Exposed-days is the number of days each firefighter worked in a job or at a location with the potential for exposure. It will be derived from the employment date and event date.
Fire-runs is the total number of fire-runs made by each firefighter. It will be derived from the event date per event.
Fire-hours is the total time spent at fires by each firefighter. It will be derived from the exposed hours per day.
The individual Emission burden is the total duration of individual emission exposure.
Daily Emission burden is the hours of emission burden in a day. A day is 24 hours and starts at 00:00 hours and ends at 23:59 hours. The emission burden per event is the sum of the daily emission burden per event.
Level of toxicity is a qualitative assessment based on the pollutants, in six different levels Good, Moderate, Unhealthy for the sensitive group, Unhealthy, Very Unhealthy, Hazardous.
The first Noted date is the date on which a specific disease condition was first diagnosed.
The disease condition is the actual finding of the Disease state and its stage.
Both data sources will be quantitatively analyzed using the two main methods, Observations, and Questioners. Careful observation of the types of emission exposure and quantifying its duration for each of the combating firefighters is important. Questioners will be developed to assess the emission levels at the event locations. Each of the identified disease condition and the first noted date will be collected per firefighter. Qualitatively assess the worsening of disease condition from periodic health screening reports, based on its progress. The exposure assessment will be conducted by researchers who will be blinded to healthcare reports, to reduce the likelihood of information bias in the subsequent analyses. The below table shows the high-level plan of who, what, why, where and when for data collection.
While the Descriptive statistics and the Inferential statistics are vital for quantitative analysis, there is a need for careful sample selection to make a meaningful inference of the population statistics. The document discusses various sampling methods and reviews its relations with the population statistic. Each of the sampling techniques was reviewed, and my level of confidence in each of the sample mean to the population mean.
The era of big data has resulted in the development and applications of technologies and methods aimed at effectively using massive amounts of data to support decision-making and knowledge discovery activities. In this paper, the five Vs. of big data, volume, velocity, variety, veracity, and value, are reviewed, as well as new technologies, including NoSQL databases that have emerged to accommodate the needs of big data initiatives.
Both the SQL and NoSQL databases have their applications, based on the development requirements.
The datasets have a combination of continuous data, discrete numerical data, geospatial data, and categorical data types. A standard frequency of data aggregation will be determined before the analysis to calculate daily emission exposure, emission exposure per wildfire event, emission exposure for the entire career. Establish Mean, median level for emission exposure, and corresponding clinical diagnosis. Develop an unsupervised clustering of a dataset based on similar emission exposure and associated health risks. The analysis will help determine an emission exposure threshold that can be used to effectively manage the Health risk proactively and develop recommendations on care pathways.
The infographic shows a typical path of different data types in research activity. From the categorical data, we can calculate the Frequency and Mode before applying a Chi-Squared test or a Logistic regression in case of a classification scenario. From an Interval-Ratio or Continuous data, we can calculate Mean, Median, and Standard deviation to see if there is any variation, and accept the null hypothesis in the case of no variation. Several options are available for continuous data based on the spread and Kurtosis.
Finally, an appropriate method of visualization can be used to view and communicate the behavior of the data.
Fire Fighters Age: It is a continuous variable with type float, rounded to the nearest months of the firefighter's age.
Measure of Central Tendency: Mean and Median for this sample are pretty close to each other because the mean value is the balancing point, and it is also the average. Since all values are unique for this sample, there is no value for Mode.
Measure of Spread: The range or the difference between the minimum value and the maximum values shows the dispersion but in cases of outliers, it does not clearly indicate the spread. The standard deviation measures how far an individual value is from the mean value. In general, for larger sample size, the distribution is normal.
There is a huge variation in the CO emission and shows a great relationship with the Health risk. The duration of exposure varies significantly when compared to various health risks.
Diagnostic Condition: It is the diagnosis reported by the physician and serves as a qualitative variable describing the state of health. It is a discrete variable to map the health condition of the firefighter.
T
Research Report: the longest and most comprehensive presentation format,
Executive Summary: one to two pages providing an overview of the findings with a statement of action items,
Short Answers: a statement of action items,
Slide Presentation: designed for an oral presentation that provides some context of the research, the findings and the action items,
White Paper: a short report that describes the research and findings, action items, and how other needs and broader findings in the research area.
Develop recommendations, possibly a wearable sensor built to collect and managing emission exposure on an individual basis effectively.
Develop an AI/ML model to proactively identify the potential firefighter early on to manage the health conditions effectively.
Post data collection, both the datasets require careful mapping of Independent variables to associate the positive or negative correlations with studying the impact on overall health risk, retrospectively.
Last but not the least, The 45CFR46 and the Belmont study summarizes the ethical principles identified by the commission in the context of its deliberation. Scientific research has produced substantial social benefits. It has also posed some troubling ethical questions. The code consists of rules, some general and other specifics that guide the investigators and other reviewers of research in their work. It was depressing to read about some of the early research participants were treated unethically and helps us learn a systematic method in not repeating the unfair practices.
All interested citizens, including Scientist, Research subjects, and Reviewers will get trained with the research scope and the extent of data collection required for this analysis. The main objective is to follow an analytical framework that will guide the resolution of the ethical problem arising from research involving firefighter’s health reports.
However, some of the firefighters may not be capable of self-determination, or the capacity of self-determination may mature during the research participation, and some participants may not be in a position to assess their liberty due to their illness. Subjects are to be treated in an ethical manner, not only by respecting their decisions, but also protecting them from harm and secure their wellbeing.
Like the principle of respect for persons finds expression in the requirement for Informed consent and the principle of beneficence in risk/benefit assessment, the principle of justice also gives rise to moral requirements that there be fair procedures and outcomes in the selection of firefighter’s event and health history.