An App for Comparing the Epidemic Impacts in Continents and Trends of the Confirmed Cases on COVID-19 using the Online Rasch Model: An Observational Study (Preprint)

2020 ◽  
Author(s):  
CHIEN WEI
Keyword(s):  
Observational Study ◽  
Rasch Model ◽  
Online Algorithm ◽  
Forest Tree ◽  
Google Maps ◽  
Line Chart ◽  
Weighted Score ◽  
Novel Coronavirus ◽  
The Rasch Model ◽  
Outbreak Situation

BACKGROUND When a novel coronavirus (e.g., COVID-19) starts to spread, two of the most frequently asked questions are about (1) the overall trend of daily confirmed cases increasing or decreasing during the on-going outbreak epidemic and (2) the worst-hit continents for COVID-19 in the recent weeks. Finding the trend of the outbreak spread and the epidemic impacts on continents amid COVID-19 is continuously an urgent concern. OBJECTIVE This study aims to (1) inspect the epidemic trend over days, (2) develop an online algorithm to draw the epidemic impacts for COVID-19 among continents, and (3) design an app for a better understanding of the outbreak situation on Google Maps. METHODS We downloaded the COVID-19 outbreak numbers from Jun 24 to July 13, 2020, from Github that contains the number of confirmed cases in countries/regions. Three methods were used to compare differences in COVID-19-struck measures, including (1)the traditional summation score, (2) the Rasch logit score, and (3) the weighted score(i.e., adjusted by the estimated variance). Rasch model was applied to estimate the overall item (i.e., day) difficulties and the COVID-19-struck measures for all countries/areas. The epidemic trend was assessed by the correlation coefficient (CC) computed by the item difficulties over the observed days. An online algorithm based on the Rasch model was built for displaying the outbreak trend and the epidemic effects in comparison for continents using the forest tree plot and the analysis of variance(ANOVA). An app was developed to understand the daily epidemic trends on Google Maps. RESULTS The three methods used for comparing differences in COVID-19-struck measures were displayed somewhat different. A line chart was drawn online to present the trend measured by item(i.e., day) difficulties approaching stability with CC=-0.07. Differences in COVID-19-struck impacts were observed among continents using ANOVA(p<0.001= Chidist(160.31, 5)) and the forest tree plot. A dashboard was created to present the COVID-19 situation on Google Maps. CONCLUSIONS The three methods used for comparing differences in COVID-19-struck measures were displayed somewhat different. A line chart was drawn online to present the trend measured by item(i.e., day) difficulties approaching stability with CC=-0.07. Differences in COVID-19-struck impacts were observed among continents using ANOVA(p<0.001= Chidist(160.31, 5)) and the forest tree plot. A dashboard was created to present the COVID-19 situation on Google Maps. CLINICALTRIAL Nil

An App for Inspecting the Epidemic Trend of the Confirmed Cases on COVID-19 using the Online Rasch Model and Strength Coefficients(SC): An Observational Study (Preprint)

2020 ◽  
Author(s):  
Wei-Chih Kan ◽  
Jui-Chung John Lin ◽  
Tsair-Wei Chien
Keyword(s):  
Rasch Model ◽  
Online Algorithm ◽  
Turning Point ◽  
Daily Basis ◽  
School Students ◽  
Google Maps ◽  
Infected Case ◽  
Line Chart ◽  
The Rasch Model ◽  
Outbreak Situation

BACKGROUND When a novel coronavirus (e.g., COVID -19) starts to spread, one of the many questions asked is with regards to the trend of new confirmed cases increasing or decreasing during the on-going outbreak epidemic. Finding the turning point of the outbreak spread (i.e., from ascending to declining status) is continuously an urgent concern. Due to different weights (e.g.., an extremely high proportion of confirmed cases in Hubei, China) of the cases confirmed in countries/regions, using either the overall total number or a partially small portion of the infected-case regions to determine the turning point (e.g., the trend to decline) is problematic and unreliable. Rasch analysis used for examining individual performances of school students was thus considered as a tool to inspect the epidemic trend through the pattern of item (e.g., day in epidemic) difficulties over days. OBJECTIVE This study aims to (1) inspect the epidemic trend by performing Rasch model and observing the pattern of item difficulties over days, (2) develop an online algorithm to draw the trend plot, and (3) design an app for a better understanding of the outbreak situation on Google Maps. METHODS We downloaded the COVID-19 outbreak numbers from January 21 to February 27, 2020, from Github that contains information on confirmed cases in more than 30 Chinese locations and other countries/regions. Item (i.e., day) difficulties based on the recent 20 days were calibrated using the Rasch model. All responses were derived from the ordinal scores by using the logarithm function (i.e.., round(ln(confirmed cases),0) from 0 to 5). The epidemic trend was assessed by the correlation coefficients (CC) computed by the item difficulties along with the time points of days. The recent several CCs were plotted with a line chart. An online algorithm based on the Rasch model was built for displaying the outbreak trend on a daily basis. A strength coefficient(SC) was complemented to examine the outliers for each region in the recent three days. An app was developed to understand the daily epidemic trends on Google Maps. RESULTS The CCs measured by item(i.e., day) difficulties have been monotonously increased from -0.28(start from Feb. 8) to 0.36(till Feb. 27), indicating the epidemic trend has gradually declined. However, the trend out of China is increasing with the CC=-0.88. The SC was taken into consideration in three countries/regions: Italy(=0.87), Iran(0.59), and Shandong(China)(=0.58) on Feb. 21, 2020. A line chart was drawn online using item difficulties calibrated by the Rasch model for examining the epidemic trend. A dashboard was created to present the COVID-19 situation on Google Maps. CONCLUSIONS We created an online Rasch modeling algorithm that can calibrate daily item difficulties and then draw a line chart to analyze the epidemic trend. The SC is complemental to the trend observation. An app developed for displaying the epidemic trend helps us better understand the outbreak situation. CLINICALTRIAL Not available

scholarly journals A Competency-Based Approach to Pass/Fail Decisions: An Observational Study

2021 ◽  
Author(s):  
Nazdar E. Alkhateeb ◽  
Ali Al-Dabbagh ◽  
Yaseen Mohammed ◽  
Mohammed Ibrahim
Keyword(s):  
Observational Study ◽  
Rasch Model ◽  
Standard Setting ◽  
Careful Consideration ◽  
Validity And Reliability ◽  
High Stakes ◽  
High Stakes Assessment ◽  
Sufficient Degree ◽  
Competency Based ◽  
The Rasch Model

Any high-stakes assessment that leads to an important decision requires careful consideration in determining whether a student passes or fails. Despite the implementation of many standard-setting methods in clinical examinations, concerns remain about the reliability of pass/fail decisions in high stakes assessment, especially clinical assessment. This observational study proposes a defensible pass/fail decision based on the number of failed competencies. In the study conducted in Erbil, Iraq, in June 2018, results were obtained for 150 medical students on their final objective structured clinical examination. Cutoff scores and pass/fail decisions were calculated using the modified Angoff, borderline, borderline-regression, and holistic methods. The results were compared with each other and with a new competency method using Cohen’s kappa. Rasch analysis was used to compare the consistency of competency data with Rasch model estimates. The competency method resulted in 40 (26.7%) students failing, compared with 76 (50.6%), 37 (24.6%), 35 (23.3%), and 13 (8%) for the modified Angoff, borderline, borderline regression, and holistic methods, respectively. The competency method demonstrated a sufficient degree of fit to the Rasch model (mean outfit and infit statistics of 0.961 and 0.960, respectively). In conclusion, the competency method was more stringent in determining pass/fail, compared with other standard-setting methods, except for the modified Angoff method. The fit of competency data to the Rasch model provides evidence for the validity and reliability of pass/fail decisions.

An App for Classifying the Trend Magnitudes and the Multiple Infection Rates(MIR) of Novel Coronavirus (2019-nCoV) in Countries/Regions: A Data-Driven Analysis (Preprint)

2020 ◽  
Author(s):  
Tsair-Wei Chien ◽  
Wei-Chih Kan ◽  
Yu-Tsen Yeh ◽  
Shu-Chun Kuo
Keyword(s):  
Real Time ◽  
Daily Basis ◽  
Public Understanding ◽  
Multiple Infection ◽  
Google Maps ◽  
The Public ◽  
Choropleth Maps ◽  
Novel Coronavirus ◽  
Cutting Point ◽  
Outbreak Situation

BACKGROUND An outbreak of the novel coronavirus (2019-nCoV) pneumonia hits the city of Wuhan, China, in December 2019 and subsequently spread to other provinces/regions of China as well as foreign countries. An online dashboard regularly updating the worldwide status of the coronavirus outbreak would be beneficial to the public understanding of the almost-real-time 2019-nCoV situation. Some online dashboards were equipped with wow-features on a world map. However, only displaying the case numbers of the outbreak across countries/provinces/regions is insufficient to the public. The trends of the outbreak and variations of multiple infection rate (MIR) would be greatly informative in displaying on a dashboard in the form of an app. OBJECTIVE This study aims to (1) present the MIR in comparison for each counties/regions, (2) develop an algorithm that classifies entities into four clusters (e.g., ready to rise, increasing, slowing down, and ready to decrease with four steps and quadrants named 4SQ diagram for short) shown on Google Maps, and (3) design an app for better understanding the outbreak situation. METHODS We downloaded 2019-nCoV outbreak numbers in countries/regions on a daily basis from Google Sheet that contains information on confirmed cases in more than 30 Chinese locations and other countries/regions. Choropleth maps and Kano diagrams were drawn based on the 4SQ diagram. The Kano diagram was applied to present the classification feature for each country/region using a dashboard presenting on Google Maps. One novel presentation was used to identify the recent MIR changes across sectors. Four clusters of the 2019-nCoV outbreak were dynamically classified. The other four basic features were involved including (1) an overall visual display on case counts, (2) a choropleth map, (3) daily MIR trend changes, and (4)three-type trend charts. The Separation Index (SI) was applied to assess the role Hubei(China) played in the outbreak situation. An app aimed for public understandings based on a dashboard to classify and visualize with Google Maps was introduced. RESULTS We made improvements on the display of classification of the outbreak and the death rate for each region, for example, 2.01% and 2.87% for all cases and Hubei(China) only, respectively. Three-type trend-charts were automatically linked to choropleth maps and the Kano diagrams in near real-time. Importantly, the sequential trend for each region on a daily basis classifies outbreak attributes (e.g., Japan was increasing and Taiwan ready to rise on February 6, 2019). The SI for Hubei(China) reaches 0.96, extremely higher than the cutting point at 0.7. The highest MIR(=0.26) was British Columbia(Canada) on February 9, 2020. CONCLUSIONS The unique features for display the outbreak situation of the 2019-nCoV were proposed in this study. Visualizations using the 4SQ diagram, SI, and the MIR based on time series were present displaying dashboards on Google Maps. An app developed for visualizing the data is required for application in the future.

scholarly journals A competency-based approach to pass/fail decisions in an objective structured clinical examination: An observational study

2020 ◽  
Author(s):  
Nazdar Ezzaddin Alkhateeb ◽  
Ali Al-Dabbagh ◽  
Yaseen Omar Mohammed ◽  
Mohammed Ibrahim
Keyword(s):  
Observational Study ◽  
Clinical Examination ◽  
Rasch Model ◽  
Careful Consideration ◽  
Objective Structured Clinical Examination ◽  
Validity And Reliability ◽  
High Stakes ◽  
Sufficient Degree ◽  
Competency Based ◽  
The Rasch Model

BackgroundAny high-stakes assessment that leads to an important decision requires careful consideration in determining whether a student passes or fails. This observational study conducted in Erbil, Iraq, in June 2018 proposes a defensible pass/fail decision based on the number of failed competencies.MethodsResults were obtained for 150 medical students on their final objective structured clinical examination. Cutoff scores and pass/fail decisions were calculated using the modified Angoff, borderline, borderline-regression and holistic methods. The results were compared with each other and with a new competency method using Cohen’s kappa. Rasch analysis was used to compare the consistency of competency data with Rasch model estimates.ResultsThe competency method resulted in 40 (26.7%) students failing, compared with 76 (50.6%), 37 (24.6%), 35 (23.3%) and 13 (8%) for the modified Angoff, borderline, borderline regression and holistic methods, respectively. The competency method demonstrated a sufficient degree of fit to the Rasch model (mean outfit and infit statistics of 0.961 and 0.960, respectively).Conclusionsthe competency method was more stringent in determining pass/fail, compared with other standard-setting methods, except for the modified Angoff method. The fit of competency data to the Rasch model provides evidence for the validity and reliability of pass/fail decisions.

A new approach of testing the Rasch model

2011 ◽  
Author(s):  
Klaus Kubinger ◽  
D. Rasch ◽  
T. Yanagida
Keyword(s):  
Rasch Model ◽  
New Approach ◽  
The Rasch Model

scholarly journals Measurement Precision and Efficiency of Computerized Adaptive Testing for the Activities-specific Balance Confidence Scale in People With Stroke

2021 ◽  
Author(s):  
Bryant A Seamon ◽  
Steven A Kautz ◽  
Craig A Velozo
Keyword(s):  
Rasch Model ◽  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Measurement Precision ◽  
Strongly Correlated ◽  
Computerized Adaptive Test ◽  
Balance Confidence ◽  
Adaptive Test ◽  
The Rasch Model ◽  
Confidence Scale

Abstract Objective Administrative burden often prevents clinical assessment of balance confidence in people with stroke. A computerized adaptive test (CAT) version of the Activities-specific Balance Confidence Scale (ABC CAT) can dramatically reduce this burden. The objective of this study was to test balance confidence measurement precision and efficiency in people with stroke with an ABC CAT. Methods We conducted a retrospective cross-sectional simulation study with data from 406 adults approximately 2-months post-stroke in the Locomotor-Experience Applied Post-Stroke (LEAPS) trial. Item parameters for CAT calibration were estimated with the Rasch model using a random sample of participants (n = 203). Computer simulation was used with response data from remaining 203 participants to evaluate the ABC CAT algorithm under varying stopping criteria. We compared estimated levels of balance confidence from each simulation to actual levels predicted from the Rasch model (Pearson correlations and mean standard error (SE)). Results Results from simulations with number of items as a stopping criterion strongly correlated with actual ABC scores (full item, r = 1, 12-item, r = 0.994; 8-item, r = 0.98; 4-item, r = 0.929). Mean SE increased with decreasing number of items administered (full item, SE = 0.31; 12-item, SE = 0.33; 8-item, SE = 0.38; 4-item, SE = 0.49). A precision-based stopping rule (mean SE = 0.5) also strongly correlated with actual ABC scores (r = .941) and optimized the relationship between number of items administrated with precision (mean number of items 4.37, range [4–9]). Conclusions An ABC CAT can determine accurate and precise measures of balance confidence in people with stroke with as few as 4 items. Individuals with lower balance confidence may require a greater number of items (up to 9) and attributed to the LEAPS trial excluding more functionally impaired persons. Impact Statement Computerized adaptive testing can drastically reduce the ABC’s test administration time while maintaining accuracy and precision. This should greatly enhance clinical utility, facilitating adoption of clinical practice guidelines in stroke rehabilitation. Lay Summary If you have had a stroke, your physical therapist will likely test your balance confidence. A computerized adaptive test version of the ABC scale can accurately identify balance with as few as 4 questions, which takes much less time.

scholarly journals Futuristic Trends and Innovations for Examining the Performance of Course Learning Outcomes Using the Rasch Analytical Model

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 727
Author(s):  
Moustafa M. Nasralla ◽  
Basiem Al-Shattarat ◽  
Dhafer J. Almakhles ◽  
Abdelhakim Abdelhadi ◽  
Eman S. Abowardah
Keyword(s):  
Learning Outcomes ◽  
Analytical Model ◽  
Rasch Model ◽  
Teaching Techniques ◽  
Business Courses ◽  
Learning And Teaching ◽  
Association Pattern ◽  
Engineering Education Research ◽  
Academic Year ◽  
The Rasch Model

The literature on engineering education research highlights the relevance of evaluating course learning outcomes (CLOs). However, generic and reliable mechanisms for evaluating CLOs remain challenges. The purpose of this project was to accurately assess the efficacy of the learning and teaching techniques through analysing the CLOs’ performance by using an advanced analytical model (i.e., the Rasch model) in the context of engineering and business education. This model produced an association pattern between the students and the overall achieved CLO performance. The sample in this project comprised students who are enrolled in some nominated engineering and business courses over one academic year at Prince Sultan University, Saudi Arabia. This sample considered several types of assessment, such as direct assessments (e.g., quizzes, assignments, projects, and examination) and indirect assessments (e.g., surveys). The current research illustrates that the Rasch model for measurement can categorise grades according to course expectations and standards in a more accurate manner, thus differentiating students by their extent of educational knowledge. The results from this project will guide the educator to track and monitor the CLOs’ performance, which is identified in every course to estimate the students’ knowledge, skills, and competence levels, which will be collected from the predefined sample by the end of each semester. The Rasch measurement model’s proposed approach can adequately assess the learning outcomes.

scholarly journals Changing the Order of Factors Does Not Change the Product but Does Affect Students’ Answers, Especially Girls’ Answers

2021 ◽  
Vol 11 (5) ◽  
pp. 201
Author(s):  
Clelia Cascella ◽  
Chiara Giberti ◽  
Giorgio Bolondi
Keyword(s):  
Experimental Design ◽  
Rasch Model ◽  
Achievement Tests ◽  
Mathematical Content ◽  
Probability Sample ◽  
Decimal Numbers ◽  
Common Items ◽  
The Rasch Model ◽  
Grade 8

This study is aimed at exploring how different formulations of the same mathematical item may influence students’ answers, and whether or not boys and girls are equally affected by differences in presentation. An experimental design was employed: the same stem-items (i.e., items with the same mathematical content and question intent) were formulated differently and administered to a probability sample of 1647 students (grade 8). All the achievement tests were anchored via a set of common items. Students’ answers, equated and then analysed using the Rasch model, confirmed that different formulations affect students’ performances and thus the psychometric functionality of items, with discernible differences according to gender. In particular, we explored students’ sensitivity to the effect of a typical misconception about multiplication with decimal numbers (often called “multiplication makes bigger”) and tested the hypothesis that girls are more prone than boys to be negatively affected by misconception.

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