The document summarizes research analyzing author-level bibliometric indicators across four disciplines. It examines how publication and citation counts are affected by factors like gender, origin, seniority, and academic age. Key findings include that academic age is the strongest predictor of publications and citations, and that indicators are estimates that should report confidence intervals due to skewed data and correlations between metrics. The research aims to identify quantitative metrics that can objectively evaluate researchers while accounting for disciplinary and individual differences.
Fluwitter : An Ontology-Based Framework for Formulating Spatio-Temporal Infl...Udaya Jayawardhana
Fluwitter is a Twitter based fully functional real time influenza monitoring system and it produces interpolated web maps showing the current influenza situation within the respective geographical region.
https://etd.ohiolink.edu/pg_10?0::NO:10:P10_ETD_SUBID:115789
Traffic-related air pollution may contribute to increasing rates of childhood asthma. Studies show mixed results but most find positive associations between exposure to traffic pollution and developing asthma, though not all associations are statistically significant. Exposure models also influence results. While the relationship is unclear, reducing traffic pollution could help prevent some asthma cases. Further research is needed to identify the specific pollutants and susceptible groups involved.
Presented at 'Changing Perspectives: 1st International Conference on Transport and Health', London, 6 -8 July 2015
Haneen Khreis, Charlotte Kelly, James Tate, Roger Parslow, Karen Lucas
Lecture workshop 2 am open access and altmetricsThed van Leeuwen
Traditionally, advanced bibliometrics have been the ‘gold standard’ in research evaluations in many fields. Due to changes in communication patterns in various fields, we now see alternative ways of assessing research appearing on the landscape. One of the major developments in scientific communication patterns is the advent of the Openness movement, through which various activities in academic life become more democratic, transparent, and hopefully fairer. This stretches out to publishing and the costs involved, how data are shared, and how peer review is organized, to name some instances in which the issue of Openness is raised. Of a somewhat more recent nature is the way assessment of scholarly activity is organized, in particular with respect to the way the various audiences with whom scholars are communicating are considered. A new way of looking at research assessment is through the recent ‘alternative metrics’ or also referred to as Altmetrics.
As more classical bibliometrics are under pressure, due to international (DORA-Declaration) and national debates and initiatives (SiT) related to the organization of research assessment in various layers of the science system. This stirs a re-focus from science policy towards alternative ways to assess research performance. In this presentation we will show, by a recent example, how careful we have to be in making choices for metrics in order to support research assessment practices as well science policy decision making.
Measure up! The extent author-level bibliometric indicators are appropriate m...Lorna Wildgaard
PhD defense about the appropriate use and validity of bibliometric indicators at the individual level. The presentation considers the familial relationships between indicators, the extent concepts being measured are captured by the indicator model as well as the general characteristics of the indicators and the profile of the researchers they are designed to measure
Keynote at NEFUS (Danish Network for Research Support services) Workshop on ethics and leadership in the use of bibliometric data. My talk discusses the challenges we face in bibliometric analysis of the individual researcher. The slides are a mixture of English and Danish. The workshop link is here: https://www.dfdf.dk/index.php/arrangementer/details/41-NEFUS?xref=35
Early Career Tactics to Increase Scholarly ImpactElaine Lasda
This document provides early career researchers with tactics to increase their scholarly impact and visibility. It discusses key ways to measure impact, such as journal metrics like impact factor and h-index, and author-level metrics. It also recommends strategies for increasing impact, such as publishing in highly cited journals, engaging in open access publishing models, using scholarly networking platforms, collaborating with others, and optimizing discoverability. Quick tips highlighted for getting started include getting published in a key journal, engaging in open access, and using scholarly networking platforms.
Fluwitter : An Ontology-Based Framework for Formulating Spatio-Temporal Infl...Udaya Jayawardhana
Fluwitter is a Twitter based fully functional real time influenza monitoring system and it produces interpolated web maps showing the current influenza situation within the respective geographical region.
https://etd.ohiolink.edu/pg_10?0::NO:10:P10_ETD_SUBID:115789
Traffic-related air pollution may contribute to increasing rates of childhood asthma. Studies show mixed results but most find positive associations between exposure to traffic pollution and developing asthma, though not all associations are statistically significant. Exposure models also influence results. While the relationship is unclear, reducing traffic pollution could help prevent some asthma cases. Further research is needed to identify the specific pollutants and susceptible groups involved.
Presented at 'Changing Perspectives: 1st International Conference on Transport and Health', London, 6 -8 July 2015
Haneen Khreis, Charlotte Kelly, James Tate, Roger Parslow, Karen Lucas
Lecture workshop 2 am open access and altmetricsThed van Leeuwen
Traditionally, advanced bibliometrics have been the ‘gold standard’ in research evaluations in many fields. Due to changes in communication patterns in various fields, we now see alternative ways of assessing research appearing on the landscape. One of the major developments in scientific communication patterns is the advent of the Openness movement, through which various activities in academic life become more democratic, transparent, and hopefully fairer. This stretches out to publishing and the costs involved, how data are shared, and how peer review is organized, to name some instances in which the issue of Openness is raised. Of a somewhat more recent nature is the way assessment of scholarly activity is organized, in particular with respect to the way the various audiences with whom scholars are communicating are considered. A new way of looking at research assessment is through the recent ‘alternative metrics’ or also referred to as Altmetrics.
As more classical bibliometrics are under pressure, due to international (DORA-Declaration) and national debates and initiatives (SiT) related to the organization of research assessment in various layers of the science system. This stirs a re-focus from science policy towards alternative ways to assess research performance. In this presentation we will show, by a recent example, how careful we have to be in making choices for metrics in order to support research assessment practices as well science policy decision making.
Measure up! The extent author-level bibliometric indicators are appropriate m...Lorna Wildgaard
PhD defense about the appropriate use and validity of bibliometric indicators at the individual level. The presentation considers the familial relationships between indicators, the extent concepts being measured are captured by the indicator model as well as the general characteristics of the indicators and the profile of the researchers they are designed to measure
Keynote at NEFUS (Danish Network for Research Support services) Workshop on ethics and leadership in the use of bibliometric data. My talk discusses the challenges we face in bibliometric analysis of the individual researcher. The slides are a mixture of English and Danish. The workshop link is here: https://www.dfdf.dk/index.php/arrangementer/details/41-NEFUS?xref=35
Early Career Tactics to Increase Scholarly ImpactElaine Lasda
This document provides early career researchers with tactics to increase their scholarly impact and visibility. It discusses key ways to measure impact, such as journal metrics like impact factor and h-index, and author-level metrics. It also recommends strategies for increasing impact, such as publishing in highly cited journals, engaging in open access publishing models, using scholarly networking platforms, collaborating with others, and optimizing discoverability. Quick tips highlighted for getting started include getting published in a key journal, engaging in open access, and using scholarly networking platforms.
Running head DATA ANALYSIS1DATA ANALYSIS 7Dat.docxhealdkathaleen
Running head: DATA ANALYSIS 1
DATA ANALYSIS 7
Data Analysis
Tammie Witcher
Columbia Southern University
Data Analysis: Descriptive Statistics and Assumption Testing
Details of how data is collected and analyzed is presented here. The research that led to the achievement of Sun Coast objectives was done using quantitative research methods since they offer detailed insights pertaining to the study. Research design is the specific type of study that one would conduct and is usually consistent with one’s philosophical worldview and the methodological approach the researcher chooses
Correlation: Descriptive Statistics and Assumption Testing
Frequency distribution table
Histogram.
Descriptive statistics table.
Measurement scale. Causal-comparative research methods which was sometimes combined with the descriptive statistics one (Creswell & Creswell, 2018). The former was used to find the relationship between dependent and independent variables after the occurrence of any action in Sun Coast.
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were employed for the frequency table to justify various aspects tested in the research.
Evaluation. Sun Coast’s leadership and other business objectives could render descriptive statistics significant since the researchers could use the past figures to analyze the current ones and make a sound forecast of future organizational performance.
Simple Regression: Descriptive Statistics and Assumption Testing
Frequency distribution table.
Histogram.
Descriptive statistics table.
Measurement scale. Regression analysis procedure would be appropriate for RQ3 since the variable, DB levels of work would be predicted before placing employees on-site for future contracts. There is no independent sample among those provided by this RQ.
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were employed for the frequency table to justify various aspects tested in the research.
Evaluation. DB levels of work would be predicted before placing employees on-site for future contracts. There is no independent sample among those provided by this RQ.
Multiple Regression: Descriptive Statistics and Assumption Testing
Frequency distribution table.
Histogram.
Descriptive statistics table.
Measurement scale. The measurement for this case applied the regression procedure to use to test different hypotheses since the interest is whether a relationship exists between an independent variable (IV) and dependent variable (DV). Correlation will indicate if there is a relationship between PM size (IV) and the employee health (DV) and the magnitude of that impact if at all there is one
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were also used.
Evaluation. The outcomeinvolved dividing populations in Sun Coa ...
Comparing scientific performance across disciplines: Methodological and conce...Ludo Waltman
Presentation at the 7th International Conference on Information Technologies and Information Society (ITIS2015) in Novo Mestro, Slovenia on November 5, 2015.
The Effect of Radiology Data Mining Software on Departmental Scholarly ActivityEric Hymer
The document discusses how the implementation of radiology data mining software (Illuminate) impacted scholarly activity at a university medical center radiology department. After installing the software, residents were 3-4 times more likely to author abstracts and manuscripts. The department saw a large increase in resident publications and presentations after using the software. While not proving causation, the results suggest that data mining software can increase research efficiency and output by reducing time barriers to conducting research.
- The document analyzed Twitter usage data from 391 scholars across various disciplines who reported having a Twitter account.
- The data showed differences in Twitter usage based on factors like gender, discipline, and academic age/title. However, there was no strong relationship found between Twitter usage and publication output or average citations, indicating no clear relationship between social media usage and scholarly impact.
- The data did reveal small differences between disciplines in how scholars used features like hashtags, mentions, and retweets, but further analysis is needed, especially of retweets by the scholars themselves.
Critical Analysis Journal club how to do as a beginnerebinroshan07
The document discusses critical analysis of research articles. It provides guidelines for analyzing different aspects of a research paper, including the journal, abstract, introduction, methods, results, discussion, and funding/conflicts of interest. As an example of critical analysis, the document analyzes a research article on ectodermal dysplasia. Key points analyzed include the objectives, methodology, results presented, and limitations. The document emphasizes the importance of systematically evaluating the strengths and weaknesses of studies.
This is a North Central University paper about analyzing emperimental research designs. It is written in APA format, includes references, and is graded an instructor.
Standard wording for formulating evidence conclusions and implications for re...CEBaP_rkv
There is a document outlining guidelines for standardizing the wording used in evidence conclusions and recommendations in evidence-based reviews developed by the Centre for Evidence-Based Practice of Belgian Red Cross-Flanders. The document provides criteria for evidence conclusions, including specifying the number and type of studies, intervention, comparison, outcome, level of evidence, and direction of effect. It establishes standard wording for different categories of evidence conclusions based on statistical significance, level of evidence, and precision of results. The implications for drafting recommendations based on the evidence conclusions are also discussed.
This document provides details about a study that examined symptoms in patients with chronic obstructive pulmonary disease (COPD) and moderate or severe air flow limitation. The study found that patients reported an average of 7-8 symptoms regardless of the severity of their air flow limitation. The most common symptoms in both groups were shortness of breath, cough, dry mouth, and lack of energy. There were no significant differences in demographic characteristics, smoking history, medication use, or reported symptoms between the moderate and severe groups.
The authors propose modifying the PICO framework for formulating clinical research questions to PICOS by adding an "S" for statistical analysis. The PICOS framework would help authors more robustly and reproducibly present the methodology of scientific studies. It would also serve as a checklist for reviewers to thoroughly evaluate manuscripts. The PICOS components are: P - patient population, I - intervention, C - comparative controls, O - outcomes, S - statistical analysis. Adopting the PICOS framework could help ensure scientific thoroughness and objectivity in reporting methodology and improve reproducibility and comparability of studies.
A two-way ANOVA and binary logistic regression were conducted to analyze factors influencing knowledge of calorie and BMI among students and staff of the Faculty of Health Sciences, UKM. The two-way ANOVA found no significant interaction between race and school but both school and race had a main effect on knowledge scores. Post-hoc tests found significant differences between diagnostic and healthcare schools, and rehabilitation and healthcare schools. The logistic regression found that only education level significantly predicted knowledge, with graduates having 15 times higher odds of higher knowledge than undergraduates. No other factors like gender, race, family history or BMI significantly predicted knowledge.
Week 14Analysis and Presentation of Data - Hypothesis Tes.docxmelbruce90096
Week 14
Analysis and Presentation of Data - Hypothesis Testing and Measures of Association
1
RES 500 Academic Writing and Research Skills
2
Hypothesis Testing vs. Theory
“Don’t confuse “hypothesis” and “theory.”
The former is a possible explanation; the
latter, the correct one. The establishment
of theory is the very purpose of science.”
3
Hypothesis Testing
Deductive
Reasoning
Inductive
Reasoning
4
Statistical Procedures
Descriptive
Statistics
Inferential
Statistics
5
Hypothesis Testing and the Research Process
(Source: Cooper & Schindler, 2013, Exhibit 11-1, p. 431)
6
Approaches to Hypothesis Testing
Classical statistics
Objective view of probability
Established hypothesis is rejected or fails to be rejected
Analysis based on sample data
Bayesian statistics
Extension of classical approach
Analysis based on sample data
Also considers established subjective probability estimates
7
Types of Hypotheses
Null
H0: = 50 mpg
H0: < 50 mpg
H0: > 50 mpg
Alternate
HA: = 50 mpg
HA: > 50 mpg
HA: < 50 mpg
8
Two-Tailed Test of Significance
(Source: Cooper & Schindler, 2013, Exhibit 17-2, p. 432)
9
One-Tailed Test of Significance
(Source: Cooper & Schindler, 2013, Exhibit 17-2, p. 432)
10
Statistical Decisions
(Source: Cooper & Schindler, 2013, Exhibit 17-3, p. 434)
11
Critical Values
12
Factors Affecting Probability of Committing a Error
True value of parameter
Alpha level selected
One or two-tailed test used
Sample standard deviation
Sample size
13
Statistical Testing Procedures
Obtain critical test value
Interpret the test
Stages
Choose statistical test
State null hypothesis
Select level of significance
Compute difference value
14
Tests of Significance
Nonparametric
Parametric
15
How to Select a Test
How many samples are involved?
If two or more samples:
are the individual cases independent or related?
Is the measurement scale
nominal, ordinal, interval, or ratio?
16
Parametric Tests
t-test
Z-test
17
One-Sample t-Test ExampleNullHo: = 50 mpgStatistical testt-test Significance level.05, n=100Calculated value1.786Critical test value1.66
(from Appendix C,
Exhibit C-2)
18
One Sample Chi-Square Test ExampleLiving ArrangementIntend to JoinNumber InterviewedPercent
(no. interviewed/200)Expected
Frequencies
(percent x 60)Dorm/fraternity16904527Apartment/rooming house, nearby13402012Apartment/rooming house, distant16402012Live at home15
_____30
_____15
_____ 9
_____Total6020010060
(Source: Cooper & Schindler, 2013, p. 446)
19
Two-Sample Parametric Tests
20
k-Independent-Samples Tests: ANOVA
Tests the null hypothesis that the means of three or more populations are equal
One-way: Uses a single-factor, fixed-effects model to compare the effects of a treatment or factor on a continuous dependent variabl.
The document discusses sensitivity analysis of university ranking systems. It analyzes how single indicator perturbations and multi-indicator perturbations can significantly impact university rankings in the Academic Ranking of World Universities (ARWU) and World Famous University (WFU) systems. Simulation scenarios show how adding factors like highly cited researchers, Nobel prize winners, or publications can boost a university's ranking. The analysis aims to identify efficient ways for universities to increase their rankings.
(1) The study aimed to construct and test a science diagnostic test for trainees at primary teacher training colleges to assess their competencies in science.
(2) The test was administered to 1,023 trainees from 20 colleges in Ahmedabad and Gandhinagar districts. Test scores were analyzed based on gender, area, and academic stream.
(3) The results showed no significant difference between male and female scores but significant differences in scores based on area and academic stream, with rural trainees and those in the science stream scoring higher.
This document provides guidance and resources for a Doctor of Nursing Practice (DNP) course that focuses on evidence-based practice. It includes discussion questions (DQs) on various topics related to translating research into practice and implementing evidence-based interventions. The DQs address the DNP nurse's role in leading clinical practice changes, identifying appropriate levels of research evidence, addressing biases, ensuring reliability and validity of measurement tools, and evaluating outcomes. Strategies and challenges for promoting an organizational culture of evidence-based practice are also discussed. The document aims to equip DNP students with the skills needed to implement evidence-based projects and translate research into practice.
improving the utilization and presentation of p valuesRamachandra Barik
In isolation, the P value may be dangerous and misleading as it does not provide the directionality, magnitude or variability of treatment effects. Careful study design and statistical analysis planning will help reduce the overuse of P values while making the presentation of results more meaningful by complementing P values with effect estimates and confidence intervals will help mitigate misuse of P values.
The literature review summarizes 7 papers related to cricket bat design. The papers investigated factors like impact location, bat designs from different eras, adding inserts to improve performance, reliability of tapping tests to assess bats, identifying the sweet spot for low speed impacts, comparing standard and novel bat designs, and validity of rigid body impact models. Key findings included identifying a "sweet region" on the bat face for optimal ball speed and direction, newer bat designs providing a performance advantage, inserts improving performance without changing the profile, tapping tests not being a reliable measure across participants, impacts near the center and middle of the bat providing highest rebound speeds and coefficients of restitution, novel designs redistributing mass away from the axis to increase moment of inertia
Determining cognitive distance between publication portfolios of evaluators a...Jakaria Rahman
When an expert panel evaluates research groups in a discipline specific research evaluation, it is an open question how one can determine the extent to which the panel members are able to evaluate the research groups. The expertise of the panel members should be well-matched with the research groups to ensure the quality and trustworthiness of the evaluation. Panel members who are credible experts in the field are most likely to provide valuable, relevant recommendations and suggestions that should lead to improved research quality. Due to absence of methods to determine the cognitive distance between evaluators and evaluees, this doctoral research leads to the development of informetric methods for expert panel composition. This contributes to the literature by proposing six informetric approaches to measure the match between evaluators and evaluees in a discipline specific research evaluation using their publications as a representation of their expertise.
The thesis is available at http://hdl.handle.net/10067/1481100151162165141
Running head DATA ANALYSIS1DATA ANALYSIS 7Dat.docxhealdkathaleen
Running head: DATA ANALYSIS 1
DATA ANALYSIS 7
Data Analysis
Tammie Witcher
Columbia Southern University
Data Analysis: Descriptive Statistics and Assumption Testing
Details of how data is collected and analyzed is presented here. The research that led to the achievement of Sun Coast objectives was done using quantitative research methods since they offer detailed insights pertaining to the study. Research design is the specific type of study that one would conduct and is usually consistent with one’s philosophical worldview and the methodological approach the researcher chooses
Correlation: Descriptive Statistics and Assumption Testing
Frequency distribution table
Histogram.
Descriptive statistics table.
Measurement scale. Causal-comparative research methods which was sometimes combined with the descriptive statistics one (Creswell & Creswell, 2018). The former was used to find the relationship between dependent and independent variables after the occurrence of any action in Sun Coast.
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were employed for the frequency table to justify various aspects tested in the research.
Evaluation. Sun Coast’s leadership and other business objectives could render descriptive statistics significant since the researchers could use the past figures to analyze the current ones and make a sound forecast of future organizational performance.
Simple Regression: Descriptive Statistics and Assumption Testing
Frequency distribution table.
Histogram.
Descriptive statistics table.
Measurement scale. Regression analysis procedure would be appropriate for RQ3 since the variable, DB levels of work would be predicted before placing employees on-site for future contracts. There is no independent sample among those provided by this RQ.
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were employed for the frequency table to justify various aspects tested in the research.
Evaluation. DB levels of work would be predicted before placing employees on-site for future contracts. There is no independent sample among those provided by this RQ.
Multiple Regression: Descriptive Statistics and Assumption Testing
Frequency distribution table.
Histogram.
Descriptive statistics table.
Measurement scale. The measurement for this case applied the regression procedure to use to test different hypotheses since the interest is whether a relationship exists between an independent variable (IV) and dependent variable (DV). Correlation will indicate if there is a relationship between PM size (IV) and the employee health (DV) and the magnitude of that impact if at all there is one
Measure of central tendency. The measure of central tendency majored on the mode even though both mean and median were also used.
Evaluation. The outcomeinvolved dividing populations in Sun Coa ...
Comparing scientific performance across disciplines: Methodological and conce...Ludo Waltman
Presentation at the 7th International Conference on Information Technologies and Information Society (ITIS2015) in Novo Mestro, Slovenia on November 5, 2015.
The Effect of Radiology Data Mining Software on Departmental Scholarly ActivityEric Hymer
The document discusses how the implementation of radiology data mining software (Illuminate) impacted scholarly activity at a university medical center radiology department. After installing the software, residents were 3-4 times more likely to author abstracts and manuscripts. The department saw a large increase in resident publications and presentations after using the software. While not proving causation, the results suggest that data mining software can increase research efficiency and output by reducing time barriers to conducting research.
- The document analyzed Twitter usage data from 391 scholars across various disciplines who reported having a Twitter account.
- The data showed differences in Twitter usage based on factors like gender, discipline, and academic age/title. However, there was no strong relationship found between Twitter usage and publication output or average citations, indicating no clear relationship between social media usage and scholarly impact.
- The data did reveal small differences between disciplines in how scholars used features like hashtags, mentions, and retweets, but further analysis is needed, especially of retweets by the scholars themselves.
Critical Analysis Journal club how to do as a beginnerebinroshan07
The document discusses critical analysis of research articles. It provides guidelines for analyzing different aspects of a research paper, including the journal, abstract, introduction, methods, results, discussion, and funding/conflicts of interest. As an example of critical analysis, the document analyzes a research article on ectodermal dysplasia. Key points analyzed include the objectives, methodology, results presented, and limitations. The document emphasizes the importance of systematically evaluating the strengths and weaknesses of studies.
This is a North Central University paper about analyzing emperimental research designs. It is written in APA format, includes references, and is graded an instructor.
Standard wording for formulating evidence conclusions and implications for re...CEBaP_rkv
There is a document outlining guidelines for standardizing the wording used in evidence conclusions and recommendations in evidence-based reviews developed by the Centre for Evidence-Based Practice of Belgian Red Cross-Flanders. The document provides criteria for evidence conclusions, including specifying the number and type of studies, intervention, comparison, outcome, level of evidence, and direction of effect. It establishes standard wording for different categories of evidence conclusions based on statistical significance, level of evidence, and precision of results. The implications for drafting recommendations based on the evidence conclusions are also discussed.
This document provides details about a study that examined symptoms in patients with chronic obstructive pulmonary disease (COPD) and moderate or severe air flow limitation. The study found that patients reported an average of 7-8 symptoms regardless of the severity of their air flow limitation. The most common symptoms in both groups were shortness of breath, cough, dry mouth, and lack of energy. There were no significant differences in demographic characteristics, smoking history, medication use, or reported symptoms between the moderate and severe groups.
The authors propose modifying the PICO framework for formulating clinical research questions to PICOS by adding an "S" for statistical analysis. The PICOS framework would help authors more robustly and reproducibly present the methodology of scientific studies. It would also serve as a checklist for reviewers to thoroughly evaluate manuscripts. The PICOS components are: P - patient population, I - intervention, C - comparative controls, O - outcomes, S - statistical analysis. Adopting the PICOS framework could help ensure scientific thoroughness and objectivity in reporting methodology and improve reproducibility and comparability of studies.
A two-way ANOVA and binary logistic regression were conducted to analyze factors influencing knowledge of calorie and BMI among students and staff of the Faculty of Health Sciences, UKM. The two-way ANOVA found no significant interaction between race and school but both school and race had a main effect on knowledge scores. Post-hoc tests found significant differences between diagnostic and healthcare schools, and rehabilitation and healthcare schools. The logistic regression found that only education level significantly predicted knowledge, with graduates having 15 times higher odds of higher knowledge than undergraduates. No other factors like gender, race, family history or BMI significantly predicted knowledge.
Week 14Analysis and Presentation of Data - Hypothesis Tes.docxmelbruce90096
Week 14
Analysis and Presentation of Data - Hypothesis Testing and Measures of Association
1
RES 500 Academic Writing and Research Skills
2
Hypothesis Testing vs. Theory
“Don’t confuse “hypothesis” and “theory.”
The former is a possible explanation; the
latter, the correct one. The establishment
of theory is the very purpose of science.”
3
Hypothesis Testing
Deductive
Reasoning
Inductive
Reasoning
4
Statistical Procedures
Descriptive
Statistics
Inferential
Statistics
5
Hypothesis Testing and the Research Process
(Source: Cooper & Schindler, 2013, Exhibit 11-1, p. 431)
6
Approaches to Hypothesis Testing
Classical statistics
Objective view of probability
Established hypothesis is rejected or fails to be rejected
Analysis based on sample data
Bayesian statistics
Extension of classical approach
Analysis based on sample data
Also considers established subjective probability estimates
7
Types of Hypotheses
Null
H0: = 50 mpg
H0: < 50 mpg
H0: > 50 mpg
Alternate
HA: = 50 mpg
HA: > 50 mpg
HA: < 50 mpg
8
Two-Tailed Test of Significance
(Source: Cooper & Schindler, 2013, Exhibit 17-2, p. 432)
9
One-Tailed Test of Significance
(Source: Cooper & Schindler, 2013, Exhibit 17-2, p. 432)
10
Statistical Decisions
(Source: Cooper & Schindler, 2013, Exhibit 17-3, p. 434)
11
Critical Values
12
Factors Affecting Probability of Committing a Error
True value of parameter
Alpha level selected
One or two-tailed test used
Sample standard deviation
Sample size
13
Statistical Testing Procedures
Obtain critical test value
Interpret the test
Stages
Choose statistical test
State null hypothesis
Select level of significance
Compute difference value
14
Tests of Significance
Nonparametric
Parametric
15
How to Select a Test
How many samples are involved?
If two or more samples:
are the individual cases independent or related?
Is the measurement scale
nominal, ordinal, interval, or ratio?
16
Parametric Tests
t-test
Z-test
17
One-Sample t-Test ExampleNullHo: = 50 mpgStatistical testt-test Significance level.05, n=100Calculated value1.786Critical test value1.66
(from Appendix C,
Exhibit C-2)
18
One Sample Chi-Square Test ExampleLiving ArrangementIntend to JoinNumber InterviewedPercent
(no. interviewed/200)Expected
Frequencies
(percent x 60)Dorm/fraternity16904527Apartment/rooming house, nearby13402012Apartment/rooming house, distant16402012Live at home15
_____30
_____15
_____ 9
_____Total6020010060
(Source: Cooper & Schindler, 2013, p. 446)
19
Two-Sample Parametric Tests
20
k-Independent-Samples Tests: ANOVA
Tests the null hypothesis that the means of three or more populations are equal
One-way: Uses a single-factor, fixed-effects model to compare the effects of a treatment or factor on a continuous dependent variabl.
The document discusses sensitivity analysis of university ranking systems. It analyzes how single indicator perturbations and multi-indicator perturbations can significantly impact university rankings in the Academic Ranking of World Universities (ARWU) and World Famous University (WFU) systems. Simulation scenarios show how adding factors like highly cited researchers, Nobel prize winners, or publications can boost a university's ranking. The analysis aims to identify efficient ways for universities to increase their rankings.
(1) The study aimed to construct and test a science diagnostic test for trainees at primary teacher training colleges to assess their competencies in science.
(2) The test was administered to 1,023 trainees from 20 colleges in Ahmedabad and Gandhinagar districts. Test scores were analyzed based on gender, area, and academic stream.
(3) The results showed no significant difference between male and female scores but significant differences in scores based on area and academic stream, with rural trainees and those in the science stream scoring higher.
This document provides guidance and resources for a Doctor of Nursing Practice (DNP) course that focuses on evidence-based practice. It includes discussion questions (DQs) on various topics related to translating research into practice and implementing evidence-based interventions. The DQs address the DNP nurse's role in leading clinical practice changes, identifying appropriate levels of research evidence, addressing biases, ensuring reliability and validity of measurement tools, and evaluating outcomes. Strategies and challenges for promoting an organizational culture of evidence-based practice are also discussed. The document aims to equip DNP students with the skills needed to implement evidence-based projects and translate research into practice.
improving the utilization and presentation of p valuesRamachandra Barik
In isolation, the P value may be dangerous and misleading as it does not provide the directionality, magnitude or variability of treatment effects. Careful study design and statistical analysis planning will help reduce the overuse of P values while making the presentation of results more meaningful by complementing P values with effect estimates and confidence intervals will help mitigate misuse of P values.
The literature review summarizes 7 papers related to cricket bat design. The papers investigated factors like impact location, bat designs from different eras, adding inserts to improve performance, reliability of tapping tests to assess bats, identifying the sweet spot for low speed impacts, comparing standard and novel bat designs, and validity of rigid body impact models. Key findings included identifying a "sweet region" on the bat face for optimal ball speed and direction, newer bat designs providing a performance advantage, inserts improving performance without changing the profile, tapping tests not being a reliable measure across participants, impacts near the center and middle of the bat providing highest rebound speeds and coefficients of restitution, novel designs redistributing mass away from the axis to increase moment of inertia
Determining cognitive distance between publication portfolios of evaluators a...Jakaria Rahman
When an expert panel evaluates research groups in a discipline specific research evaluation, it is an open question how one can determine the extent to which the panel members are able to evaluate the research groups. The expertise of the panel members should be well-matched with the research groups to ensure the quality and trustworthiness of the evaluation. Panel members who are credible experts in the field are most likely to provide valuable, relevant recommendations and suggestions that should lead to improved research quality. Due to absence of methods to determine the cognitive distance between evaluators and evaluees, this doctoral research leads to the development of informetric methods for expert panel composition. This contributes to the literature by proposing six informetric approaches to measure the match between evaluators and evaluees in a discipline specific research evaluation using their publications as a representation of their expertise.
The thesis is available at http://hdl.handle.net/10067/1481100151162165141
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
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1. (Scaling analysis)
of author-level
bibliometric
indicators.
Lorna Wildgaard
Royal School of Library and Information
Science
Birger Larsen
Department of Communication, AAU-CPH
2. CONTRIBUTE TO THE DISCUSSION:
TODAY’S MEET
https://todaysmeet.com/STI2014
sign in with your email, create a password, confirm and
log in
OR
Log in with Gmail
15/09/2014
Dias 2
3. PURPOSE OF THE INVESTIGATION
Quantifiable and objective alternative to other
metrics when evaluating faculty members for
academic advancement.
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Dias 3
8. SUBJECTIVE GROUPING OF 54 INDICATORS
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"A review of the characteristics of
108 author-level bibliometric
indicators", Scientometrics,
DOI: 10.1007/s11192-014-1423-3
9. METHOD 1: IDENTIFICATION OF CENTRAL INDICATORS
Discipline Index Calculation nCorr.
Astronomy Hg
The square root of (h multiplied by g).
25
Environ. Sci. H, H2
Publications are ranked in descending order
after number of citations. H is where number
of citations and rank is the same.
H2 is where the square of the number of
papers is equal to the number of citations.
26
Philosophy IQP
IQP= expected average performance of
scholar in the field, amount of papers that are
cited more frequently than average and how
much more than average they are cited
(Tc>a)
28
Pub. Health G
Publications are ranked in descending order
after number of citations. G is where the the
square root of the cumulative sum of citations
is equal to the rank
23
11. EXPLORATIVE FACTOR ANALYSIS
Discipline Publication &
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Dias 11
recognition
Normalized for
field or time
Miscellaneas
Astronomy 57.3 % (0.78) 11.8 (0.49) 8.3 (-0.028)
Environ. Sci 57.2% (0.77) 6.2 (0.04) 10.4 (0.89)
Philosophy 53.6 (0.82) 7.0 (0.50) 10.4 (0.03)
Public Health 56.2 (0.77) 6.6 (0.00) 12.1 (0.59)
24-32 indicators in dimension 1
4-9 indicators in dimension 2
3-15 in dimension 3
12. REASSESSING THE METHOD
Purpose: Quantifiable and objective alternative to other metrics
when evaluating faculty members for academic
advancement.
What we have learnt so far:
1. Publication and citation data is highly skewed
2. Transforming the variables with log, inverse, sqrt did not
improve the normality assumption of the data or improve
the MDS or the Factor Analysis,
3. Recoding the variables into categorical groups resulted in
lack of detail and still not significant results (a lot of work,
inconclusive results
So we returned to non-parametric and descriptive analyses of
the data – simple seems to be more informative when we
have skewed data that builds on publications and citations.
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Dias 12
13. DIFFERENCE IN MEDIAN PUBLICATIONS
BETWEEN SENIORITIES
Publications
Median
Post Doc-
PhD
Assis Prof –
Post Doc
Assoc. Prof
– Assis Prof
Prof.-Assoc
Prof
Mean
difference
Astronomy 12.5 20 22 28.5 20.7
Environment 5 9 11 22.5 11.8
Philosophy 3 2.5 0.5 11 4.25
Public Health 5 11 21 33 17.5
DIFFERENCE IN MEDIAN CITATIONS
BETWEEN SENIORITIES
Citations
Median
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Post Doc-
PhD
Assis Prof –
Post Doc
Assoc. Prof
– Assis Prof
Prof.-Assoc
Prof
Mean
difference
Astronomy 51.1 500.5 512 675 434.7
Environment 7 107 178 109 100.2
Philosophy 7.5 -1.5 1.5 21 7.1
Public Health 20.5 86.5 351 436 223.5
14. P & C INCREASE WITH SENIORITY. DO OTHER INDICATORS?
DISCIPLINE OUTPUT EFFECT OF
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OUTPUT
IMPACT OVER
TIME
QUALIFY IMPACT
TO FIELD
RANK
PORTFOLIO
Astronomy P
C, sc, nnc,
Sig, Csc,
Fc,
Cage, AWCR,
AWCRpa, AW,
AR
Sum pp top ncits,
IQP, NprodP
Millers H, h,
A, R, g, hg,
e, Q2, POPh
Enviro. Sci. P, Fp
C, CPP, Sc,
FracCPP,
nnc, Sig,
Csc, Fc
Cage, AWCR,
AWCRpa, AW,
AR
Mcs, sum pp top
ncits, mean mjs
mcs, max mjs
mcs, IQP, NprodP
Millers h, h,
m, A, R, g,
hg, e, Q2,
H2, POPh
Philosophy P, Fp
C, Sc, nnc,
Sig, Csc,
Fc
Cage, AR NprodP
m,A,R,g,e,
H2
Pub. Health P, Fp
C, Sc, nnc,
Sig, Csc,
Fc
AWCR,
AWCRpa, AW,
AR
Mcs, Sum pp rop
ncits, Sum pp top
prop, NprodP
Millers h, m,
A, R, g, hg,
e, Q2, H2,
PopH
15. ARE PUBLICATION & CITATION COUNT EFFECTED BY
GENDER?
nMales nFemales Md P,
male
Md P,
female
Md C,
male
Md C,
female
Astronomy 162 30 48 39 881 518
Environ. Sci 160 35 29 18 321 135
Philosophy 179 43 9 8 12 8
Pub. Health 79 53 31 29 311 353
Environmental Science: Significant difference in the amount of
publications produced by male and female researchers,
U=2036, z=-2.525, p=0.012, r=0.18. Significant difference in
the amount of citations male and female researchers receive,
U=2056, z=-2.460, p=0.014, r=0.176
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16. ARE PUBLICATION & CITATION COUNT EFFECTED BY
ORIGIN?
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17. ARE PUBLICATION & CITATION COUNT
EFFECTED BY ACADEMIC AGE OR SENIORITY?
Purpose:
How well do seniority and academic age predict number of
publications? How much of the variance in publication scores
can be explained by scores on these two scales?
Method: Multiple Regression
Results (ALL FIELDS):
The model which controls for seniority and academic age
explains between 22-36.2% of the variance in publications
(A=36%, E=36%, P=30%, PH=22%) and 1-22% of the
variance in citations, (A=18%, E= 19%, P=0,9%, PH=22%.
Conclusions:
Academic Age makes the largest unique contribution as a
predictor of publications or citations, Seniority makes very
little contribution .
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18. ARE PUBLICATION & CITATION COUNT EFFECTED BY
ACADEMIC AGE OR SENIORITY WHEN CONTROLLING
FOR GENDER AND ORIGIN?
Purpose:
Controlling for the effect of gender and origin, is our set of variables
(academic age and seniority) still able to predict a significant
amount of the variance in publication count?
Method: Hierarchical Regression (ATT: high correlated data,
assumptions of normality violated)
Results (All Disciplines):
Only Academic age and seniority made a statistically significant
contribution to the model. With academic age recording a higher
beta value (.30-.46) than seniority (.18-.25) in each discipline.
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20. CONCLUSIONS (SO FAR)
1. Indicator values are effected to varying degrees,
dependent on discipline, by gender, origin, seniority and
academic age (database & version).
2. Academic age is dependent on how it is calculated. Here
is highly dependent on database coverage. Seniority is
more understandable. But does it make sense?
3. Don’t have to wrap data in algorithims. More informative
to summarize patterns between indicators.
4. It is important to report the database and which version
of the database was used to collect the data.
21. CONCLUSIONS (SO FAR)
5. Variance in amount of publications between scholars
differs from discipline to discipline. Clear difference in
amount of publications and citations.
6. The indicators are estimates. Report confidence intervals
and range to contextualize the values.
7. Strong correlation between indicators. Central and
isolated indicators need further investigation allowing for
confounders.
8. No one indicator can stand alone. Work continues to
identify indicators suitable for discipline and seniority.
22. THANK YOU FOR YOUR ATTENTION!
Q. When does adjusting the data to fit the model
become cherry picking?
Editor's Notes
Then there is no need to wrap a ‘closed’ algebraic graphical solution on your multidimensional matrix. Explorative factor analysis: Determine nature and number of “latent” variables that account for observed variation and covariation among the set of observed indicators. In other words, what “causes” these observed correlations? Summarize patterns of correlation among indicators. Solution is an end (i.e., is of interest) in and of itself. Here you can (colour) code the indicators according to you categorization and then proceed with the analysis; where difference codes correlate on the same dimension you can go back and analyse WHY the correlation, now that we think that they are conceptually different.
The indicators were grouped into 3 or 4 components dependent on field that explain 4.5 to 57% of the total variance in the data. PC methodology for factor extraction allows for non-normal distributed variables. On this slide the dimensions are presented and % variance explained by each dimension. Dimension reduction was supported by parallel analysis (MonteCarlo PA) which showed which components with eigen values greater than the correspodning criterion values for a radomly generated datamatrix of the same size. The rotated solution solution (oblimin rotation) aided interpretation of components. This revealed which items loaded strongly on one component. There was a weak correlation between components, less than 0.3. The reliability of the components was tested using Cronbachs Alpha. The results of this analysis supports our idea of the use of indicators as seperate scales, that all use citations and publications, but measure different aspects of publication performance at the indicvidual level and some indicators are more useful in some disciplines than others.
Determine nature and number of “latent” variables that account for observed variation and covariation among the set of observed indicators. In other words, what “causes” these observed correlations?
: Determining what causes the variation and co-variation
Summarize patterns of correlation among indicators. Solution is an end (i.e., is of interest) in and of itself. Here you can (colour) code the indicators according to you categorization and then proceed with the analysis; where difference codes correlate on the same dimension you can go back and analyse WHY the correlation, now that we think that they are conceptually different. Same circus of publications and citations
The focus should be explorative analyses of the matrices, either factor analysis or simply extract the eigenvalues and vectors of the matrix using Principal Components Analysis.
The amount of publications (P) and citations (C) increased with academic rank across all disciplines, apart from Philosophy where PHD students and assistant professors have the lowest median citation counts. Further examination demonstrated statistical significant increases through all academic ranks in publication levels, Kruskal-Wallis test X2 (2n192)=92.267, p.000 and a statistically significant difference in the amount of citations, X2 (2n192)=68.54, p.000. Tests of the four a priori hypotheses were conducted using Bonferroni adjusted alpha levels
COMPLETE BONFERRONI
The data is highly skewed and attempts to normalize the data to enable regression analysis was not successful. Nonparametric statistics are used, which are less powerful than parametric measures, and tend to be less sensitive and fail to detect differences between groups that actually exist.
As publication and citation counts reliably increase with academic rank and the values between different disciplines vary, it is relevant to investigate if some indicators are more appropriate for some seniorities and disciplines than others.
AR and R measure the same
Withiin field and category – when we rank with these indicators what does this mean for the researcher – do they change position?
As publication and citation counts reliably increase with academic rank and the values between different disciplines vary, it is relevant to investigate if some indicators are more appropriate for some seniorities and disciplines than others. This is where we can start to reduce the amount of potentially useful indicators.
Kruskal Wallis: statistical difference between the values of indicators and academic rank. Yes there was a stsitistical difference, but not all of these increased with academic rank, reducing the set even further
Indicators that increase reliably with rank
How to correct for Gender in Environmental Science? What is it in this discipline that causes the discrepancy?
However, there are some ”confounders” to consider, that might also effect our results. Gender, country, academic age, rather than seniority. Compared the medain publications and median citations of male and female researchers using Mann Whitney U ranks the variables across the two genders. AS the scores are converted to ranks the actual distribution doesn’t matter.
Effect size r=0.18 – what does this mean? But small effect size is weak and might not be a consistent differnece between the amount of publications and citations between men and women.
Group Scholars into top 25, upper middle 50, lower middle 50 and bottom 25% in discipline. Academic age: categorized into 5 year groups as the average for phd in Europe is 4-5 years according pHD regulation not completion time. Landcode WHO classification. The mutlinominal regression was inconclusive, couldn’t get a good model fit – only a little of the variance was explained and analyses were not significant. Inconclusive across all indicators if country, seniority,, academic age and gender have a significant contribution to the model
Grouping defined by WHO member states defined by geography, state of economic and demographic development and mortality stratum. In this study these are the developed countries (Amr,n=9, Eur-A n=645, Eur-B n=37, Eur-C n=45 and Wpr n=7), and high-mortality developing countries (Afr n=5, Sear n=6)
Astro. No diff P, Bonferroni adjustment revealed sig diff between amount of citations in EUR A and all other member states
Enviro: no diff in the amount of P or C
Phil: no diff in the amount of P or C
Public Health: no dif in Pub, statistical sig diff in citations EUR A and other groups, but with small effect size (r=0.18)
Pub Astro: Italy/eastern Europe X2(2=58) U=226, z=-2.971, p=0.003, r=0.3 moderate effect
Cit Astro: France n18 /east n32 U=98, z=-3.840, p=0.00, r=0.54, Scandinavia, n6/East, n32, U=34, z=-2.482, p=.011, r=0.4 Bias towards Eastern Europe (Publishing less and cited least.
Pub Enviro: no stat sig diff between n pub Lowest Germany, n=7, md=16, highest other n=12, md=54Cit Enviro: No Statistially sig. diff between number of citations, lowest: netherlands, n14(md=113)
Pub Phil: Spain producing sig fewer publications than other groups: Bonferroni correction NL/Spain (U=94-5p 0.003, z=-2.964 r=0.4), UK/spain=sig U303, z=-3.610, p=0.00, r=0.4 (bonferroni 0.05/4=0.01,
Cit Phil: Spain producing less and cited less, bias against eastern european: NL 18/eastern Europe 19: x2(2,37)=50.0, z=-3.684, p=0.000, r=0.6
Pub Public Health: no sig difference in amount of publications or Citations
Seniority is a label given by the university, likewise academic age is defined in our study by the number of years since the first article registered in WoS. Seniority, as we have seen is a useful bench mark for expected indicator values, where as academic age is dependent on the database used to source the data or a subjective measure (years since phd defence, years since first meaningful publication?) Knowing the data is very skewed, after studing it so carefullt, I felt confident to do a hierarchical regression to see if academic age or seniority had a greater affect on number of publications.
Beta: distinct contribution of a variable, excluding overlap with other predictor variables.
Controlling for the effect of gender and origin, is out set of variables (academic age and seniority) still able to predict a significant amount of the variance in publication count?
Astro and Public Health wise to normalize citations for country
Enviro normalize for gender
Use h type indicators with care in Phil, coverage limited in WOS
All countries normalise for seniority
Academic age?
Make sure the indactors are calculated in the same version of the same database.
Indicators good at discriminating between top performers and bottom performers.
Fishing trip after the method, which is ok, as this is not a medical investiaqtion with a strict protocolm How to identify homogenous data, within x standard variations?What can be excluded? 2,25% at each end of the scale
Limitation of study that only based on limited data
How much of data must model represent?
Is data manipulation the way forward – ranking, sorting, looking for patterns and trends ”play with the data” without fundementally changing it.