scholarly journals Why Students Have Conflicts in Peer Assessment? An Empirical Study of an Online Peer Assessment Community

2019 ◽  
Vol 11 (23) ◽  
pp. 6807 ◽  
Author(s):  
Yanqing Wang ◽  
Zheng Zong
Keyword(s):  
Learning Environments ◽  
Fixed Effects ◽  
Negative Binomial ◽  
Positive Impact ◽  
Peer Assessment ◽  
Negative Binomial Regression ◽  
Task Conflict ◽  
Negative Binomial Regression Model ◽  
Learning Groups ◽  
Learning Platform

This study highlights the issues in the process of peer assessment in an online environment. As an interactive learning platform, peer assessment will likely lead to conflicts among students, which will hinder the sustainability of peer assessment learning environments. It is still unclear about the particular factors that influence and cause the behavioral conflicts which arise within learning groups and learning environments. To overcome this issue, the current study explores why peer assessment could trigger conflict over a student’s task. The results of a negative binomial regression model with user fixed effects indicate that student’s knowledge self-efficacy, cognitive diversity of general knowledge, and network density have a positive impact on task conflict. Interactive experience and cognition diversity of specific knowledge are not powerful motivations for task conflict in the peer assessment. The findings of this study may be helpful for educators in understanding why students have task conflict in a specific learning environment.

scholarly journals Network Position and Crowdsourcing Innovation Contribution Behavior: The Moderating Role of Knowledge Absorption Capacity

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Qingliang Meng ◽  
Yi Hang ◽  
Xiaojun Chen
Keyword(s):  
Negative Binomial ◽  
Positive Impact ◽  
Knowledge Exchange ◽  
Negative Binomial Regression ◽  
Absorption Capacity ◽  
Negative Binomial Regression Model ◽  
Network Position ◽  
The Individual ◽  
The Relationship ◽  
Knowledge Absorption

Purpose. The purpose of this study is to examine the relationship between network position and crowdsourcing innovation contribution behavior and the moderating effects of knowledge absorption capacity on the aforementioned relationship. Design/Methodology/Approach. Focusing on the Chinese context, the study conducts empirical research with the user’s knowledge-sharing network of the MIUI community to test the research model. The negative binomial regression model which is suitable for processing discrete data is used to examine the main effects of the network position, knowledge absorption capacity, and crowdsourcing innovation contribution behavior. Findings. The findings reveal that the closer the user gets to the center of the network, the more likely they will contribute. The users’ knowledge absorption capacity can help stimulate the users’ crowdsourcing innovation contribution behavior, and the users with stronger knowledge absorption capacity are more likely to transform their network position advantages into innovative contribution behaviors. Practical Implications. The study provides evidence that network position has a positive impact on their crowdsourcing innovation contribution behavior, and knowledge absorption capacity promotes the crowdsourcing innovation behavior of users. Managers should encourage users to occupy a favorable network position and increase knowledge exchange with other users, while at the same time continuously improving their own knowledge absorption capacity. Originality/Value. This study combines social network theory and the individual mindset to introduce knowledge absorptive capacity into the relationship model of the user’s network position and crowdsourcing innovation contribution behavior, thereby constructing a complete path of “knowledge supply-knowledge acquisition-knowledge application-knowledge output.” The study contributes to provide a theoretical basis for an in-depth understanding of the influence relationship between network position and crowdsourcing innovation contribution behavior. Also, it provides a reference for enterprises to carry out practical crowdsourcing innovation community governance and improve innovation performance.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

scholarly journals The association between a conditional cash transfer programme and malaria incidence: a longitudinal ecological study in the Brazilian Amazon between 2004 and 2015

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Layana Costa Alves ◽  
Mauro Niskier Sanchez ◽  
Thomas Hone ◽  
Luiz Felipe Pinto ◽  
Joilda Silva Nery ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Negative Binomial ◽  
Malaria Incidence ◽  
Panel Study ◽  
Negative Binomial Regression ◽  
Brazilian Amazon ◽  
Conditional Cash Transfer ◽  
Cash Transfer ◽  
Percentage Point Increase ◽  
The Impact

Abstract Background Malaria causes 400 thousand deaths worldwide annually. In 2018, 25% (187,693) of the total malaria cases in the Americas were in Brazil, with nearly all (99%) Brazilian cases in the Amazon region. The Bolsa Família Programme (BFP) is a conditional cash transfer (CCT) programme launched in 2003 to reduce poverty and has led to improvements in health outcomes. CCT programmes may reduce the burden of malaria by alleviating poverty and by promoting access to healthcare, however this relationship is underexplored. This study investigated the association between BFP coverage and malaria incidence in Brazil. Methods A longitudinal panel study was conducted of 807 municipalities in the Brazilian Amazon between 2004 and 2015. Negative binomial regression models adjusted for demographic and socioeconomic covariates and time trends were employed with fixed effects specifications. Results A one percentage point increase in municipal BFP coverage was associated with a 0.3% decrease in the incidence of malaria (RR = 0.997; 95% CI = 0.994–0.998). The average municipal BFP coverage increased 24 percentage points over the period 2004–2015 corresponding to be a reduction of 7.2% in the malaria incidence. Conclusions Higher coverage of the BFP was associated with a reduction in the incidence of malaria. CCT programmes should be encouraged in endemic regions for malaria in order to mitigate the impact of disease and poverty itself in these settings.

Bike Count Forecast Model with Multimodal Network Connectivity Measures

2021 ◽  
pp. 036119812110218
Author(s):  
Bingqing Liu ◽  
Divya Bade ◽  
Joseph Y. J. Chow
Keyword(s):  
New York ◽  
Negative Binomial ◽  
Network Connectivity ◽  
Negative Binomial Regression ◽  
Forecast Model ◽  
Negative Binomial Regression Model ◽  
Significance Level ◽  
Commute Time ◽  
Improve Model ◽  
Subway Stations

With the rise of cycling as a mode choice for commuting and short-distance delivery, as well as policy objectives encouraging this trend, bike count models are increasingly critical to transportation planning and investment. Studies have found that network connectivity plays a role in such models, but there remains a lack of measure for the connectivity of a link in a multimodal trip context. This study proposes a connectivity measure that captures the importance of a link in connecting the origins of cyclists and nearby subway stations, and incorporates it in a negative binomial regression model to forecast bike counts at links. Representative bike trips are generated with regard to bike-friendliness using the New York City transit trip planner and used to determine the deviation from the shortest path via the designated link. The measure is shown to improve model fitness with a significance level within 10%. Insights are also drawn for income levels, bike lanes, subway station availability, and average commute time of travelers.

A Negative Binomial Regression Model for Accuracy Tests

2012 ◽  
Vol 36 (2) ◽  
pp. 88-103 ◽  
Author(s):  
Lai-Fa Hung
Keyword(s):  
Negative Binomial ◽  
Social Science Research ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Science Research ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Negative Binomial Regression Model ◽  
Marginal Maximum Likelihood ◽  
The Mean

Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an overdispersion framework and proposes new estimation methods. The parameters in the proposed model can be estimated using the Markov chain Monte Carlo method implemented in WinBUGS and the marginal maximum likelihood method implemented in SAS. An empirical example based on models generated by the results of empirical data, which are fitted and discussed, is examined.

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