scholarly journals Integration of various scales for measurement of insomnia

2021 ◽  
pp. 263208432110100
Author(s):  
Satyendra Nath Chakrabartty
Keyword(s):  
Test Scores ◽  
Data Driven ◽  
Weighted Sum ◽  
Future Studies ◽  
Normal Probability ◽  
Equivalent Test ◽  
Item Scores ◽  
Using Data ◽  
Set Up ◽  
Different Levels

Background Scales for evaluating insomnia differ in number of items, response format, and result in different scores distributions and score ranges and may not facilitate meaningful comparisons. Objectives Transform ordinal item-scores of three scales of insomnia to continuous, equidistant, monotonic, normally distributed scores, avoiding limitations of summative scoring of Likert scales. Methods Equidistant item-scores by weighted sum using data-driven weights to different levels of different items, considering cell frequencies of Item-Levels matrix, followed by normalization and conversion to [1, 10]. Equivalent test-scores (as sum of transformed item- scores) for a pair of scales were found by Normal Probability curves. Empirical illustration given. Results Transformed test-scores are continuous, monotonic and followed Normal distribution with no outliers and tied scores. Such test-scores facilitate ranking, better classification and meaningful comparison of scales of different lengths and formats and finding equivalent score combinations of two scales. For a given value of transformed test-score of a scale, easy alternate method avoiding integration proposed to find equivalent scores of another scales. Equivalent scores of scales help to relate various cut-off scores of different scales and uniformity in interpretations. Integration of various scales of insomnia is achieved by finding one-to-one correspondence among the equivalent score of various scales with correlation over 0.99 Conclusion Resultant test-scores facilitated undertaking analysis in parametric set up. Considering the theoretical advantages including meaningfulness of operations, better comparison, use of such method of transforming scores of Likert items/test is recommended test and items, Future studies were suggested.

scholarly journals Significant Influence of EC50 Estimation by Model Choice and EC50 Type

Plant Disease ◽  
2018 ◽  
Vol 102 (4) ◽  
pp. 708-714 ◽  
Author(s):  
Zachary A. Noel ◽  
Jie Wang ◽  
Martin I. Chilvers
Keyword(s):  
Dose Response ◽  
Logistic Models ◽  
Effective Control ◽  
Model Choice ◽  
Fusarium Virguliforme ◽  
Future Studies ◽  
Software Packages ◽  
The Difference ◽  
Using Data ◽  
Set Up

The effective control to 50% growth inhibition (EC50) is a standard statistic for evaluating dose-response relationships. Many statistical software packages are available to estimate dose-response relationships but, recently, an open source package (“drc”) in R has been utilized. This package is highly adaptable, having many models to describe dose-response relationships and flexibility to describe both hormetic relationships and absolute and relative EC50. These models and definitions are generally left out of phytopathology literature. Here, we demonstrate that model choice and type of EC50 (relative versus absolute) can matter for EC50 estimation using data from Pythium oopapillum and Fusarium virguliforme. For some P. oopapillum isolates, the difference between absolute and relative EC50 was significant. Hormetic effects changed F. virguliforme EC50 distributions, leading to higher estimates than when using four- or three-parameter log-logistic models. Future studies should pay careful attention to model selection and interpretation in EC50 estimation and clearly indicate which model and EC50 measure (relative versus absolute) was used. We provide guidelines for model choice and interpretation for those wishing to set up experiments for accurate EC50 estimation.

scholarly journals A data-driven model for predicting the course of COVID-19 epidemic with applications for China, Korea, Italy, Germany, Spain, UK and USA

2020 ◽  
Author(s):  
Norden E. Huang ◽  
Fangli Qiao ◽  
Ka-Kit Tung
Keyword(s):  
Turning Point ◽  
Turning Points ◽  
Epidemiological Data ◽  
Data Driven ◽  
Additional Tool ◽  
Daily Data ◽  
Practical Tool ◽  
Using Data ◽  
Main Components ◽  
Different Levels

ABSTRACTFor an emergent disease, such as Covid-19, with no past epidemiological data to guide models, modelers struggle to make predictions of the course of the epidemic (Cyranoski, Nature News 18 February 2020). The wildly varying predictions make it difficult to base policy decisions on. On the other hand much empirical information is already contained in data of evolving epidemiological profiles. We offer an additional tool, based on general theoretical principles and validated with data, for tracking the turning points, peak and accumulated case numbers of infected and recovered for an epidemic, and to predict its course. Ability to predict the turning points and the epidemic’s end is of crucial importance for fighting the epidemic and planning for a return to normalcy. The accuracy of the prediction of the peaks of the epidemic is validated using data in different regions in China showing the effects of different levels of quarantine. The validated tool can be applied to other countries where Covid-19 has spread, and generally to future epidemics. US is found to have the largest net infection rate, and is predicted to have the largest total infected cases (708K) and will take two weeks longer than Wuhan to reach its turning point, and one week longer than Italy and Germany.SIGNIFICANCEWe offer a practical tool for tracking and predicting the course of an epidemic using the daily data on the infection and recovery. This data-driven tool can predict the turning points two weeks in advance, with an accuracy of 2-3 days, validated using data from various regions in China selected to show the effects of quarantine. It also gives information on how rapid the rise and fall of the case numbers are, and what the peak and total number of infected are. Although empirical, this approach has a sound theoretical foundation; the main components of the results are validated after the epidemic is near an end, as is the case for China, and therefore is generally applicable to future epidemics.

Implications of Person Fluctuation for the Stability and Validity of Test Scores

Methodology ◽  
2006 ◽  
Vol 2 (4) ◽  
pp. 142-148 ◽  
Author(s):  
Pere J. Ferrando
Keyword(s):  
Item Response ◽  
Test Scores ◽  
Temporal Stability ◽  
Person Fit ◽  
Item Parameters ◽  
Individual Trait ◽  
The Stability ◽  
The Individual ◽  
Different Levels ◽  
Fluctuation Model

In the IRT person-fluctuation model, the individual trait levels fluctuate within a single test administration whereas the items have fixed locations. This article studies the relations between the person and item parameters of this model and two central properties of item and test scores: temporal stability and external validity. For temporal stability, formulas are derived for predicting and interpreting item response changes in a test-retest situation on the basis of the individual fluctuations. As for validity, formulas are derived for obtaining disattenuated estimates and for predicting changes in validity in groups with different levels of fluctuation. These latter formulas are related to previous research in the person-fit domain. The results obtained and the relations discussed are illustrated with an empirical example.

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

Organizing and Managing Data Analytics Teams for Procurement

2020 ◽  
Author(s):  
Benjamin Shao ◽  
Robert D. St. Louis
Keyword(s):  
Best Practices ◽  
Business Strategy ◽  
Data Analytics ◽  
Team Members ◽  
Procurement Process ◽  
Using Data ◽  
And Training ◽  
Different Levels ◽  
The Way

Many companies are forming data analytics teams to put data to work. To enhance procurement practices, chief procurement officers (CPOs) must work effectively with data analytics teams, from hiring and training to managing and utilizing team members. This chapter presents the findings of a study on how CPOs use data analytics teams to support the procurement process. Surveys and interviews indicate companies are exhibiting different levels of maturity in using data analytics, but both the goal of CPOs (i.e., improving performance to support the business strategy) and the way to interact with data analytics teams for achieving that goal are common across companies. However, as data become more reliably available and technologies become more intelligently embedded, the best practices of organizing and managing data analytics teams for procurement will need to be constantly updated.

Enhanced Resilient State Estimation Using Data-Driven Auxiliary Models

2020 ◽  
Vol 16 (1) ◽  
pp. 639-647 ◽  
Author(s):  
Olugbenga Moses Anubi ◽  
Charalambos Konstantinou
Keyword(s):  
State Estimation ◽  
Data Driven ◽  
Using Data
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