Undirected Bipartite Networks as an Alternative Methodology to Probabilistic Exploration

2018 ◽  
pp. 75-94
Author(s):  
Juan-Francisco Martínez-Cerdá ◽  
Joan Torrent-Sellens
Keyword(s):  
Learning Analytics ◽  
Online Courses ◽  
Binary Logistic Regression ◽  
First Year ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Online Behavior ◽  
Binary Logistic Regression Model ◽  
Speed Up ◽  
Alternative Methodology

This chapter explores how graph analysis techniques are able to complement and speed up the process of learning analytics and probability theory. It uses a sample of 2,353 e-learners from six European countries (France, Germany, Greece, Poland, Portugal, and Spain), who were enrolled in their first year of open online courses offered by HarvardX and MITX. After controlling the variables for socio-demographics and online content interactions, the research reveals two main results relating student-content interactions and online behavior. First, a multiple binary logistic regression model tests that students who explore online chapters are more likely to be certified. Second, the authors propose an algorithm to generate an undirected bipartite network based on tabular data of student-content interactions (2,392 nodes, 25,883 edges, a visual representation based on modularity, degree and ForceAtlas2 layout); the graph shows a clear relationship between interactions with online chapters and chances of getting certified.

Credit constraints and Rural Migration: Evidence from Six Villages in Uttar Pradesh

2018 ◽  
Vol 15 (3) ◽  
pp. 389-398
Author(s):  
Ruchi Singh
Keyword(s):  
Credit Constraints ◽  
Uttar Pradesh ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Male Migration ◽  
Rural Economies ◽  
And Migration ◽  
The Relationship ◽  
The Empirical Analysis

Rural economies in developing countries are often characterized by credit constraints. Although few attempts have been made to understand the trends and patterns of male out-migration from Uttar Pradesh (UP), there is dearth of literature on the linkage between credit accessibility and male migration in rural Uttar Pradesh. The present study tries to fill this gap. The objective of this study is to assess the role of credit accessibility in determining rural male migration. A primary survey of 370 households was conducted in six villages of Jaunpur district in Uttar Pradesh. Simple statistical tools and a binary logistic regression model were used for analyzing the data. The result of the empirical analysis shows that various sources of credit and accessibility to them play a very important role in male migration in rural Uttar Pradesh. The study also found that the relationship between credit constraints and migration varies across various social groups in UP.

Learning Analytics Considered Harmful

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Laurie P Dringus
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Learning Analytics ◽  
Online Courses ◽  
Research Community ◽  
Current Status ◽  
Online Course ◽  
Full Potential

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.

Prevalence of facemask use among general public when visiting wet market during Covid-19 pandemic: An observational study. (Preprint)

2020 ◽  
Author(s):  
Gobi Hariyanayagam ◽  
Sera Selvanthan Sundram Gunasekaran ◽  
Shargunan Selvanthan Gunasekaran ◽  
Nur Syafina Insyirah Zaimi ◽  
Nor Amirah Abdul Halim
Keyword(s):  
High Risk ◽  
General Population ◽  
Binary Logistic Regression ◽  
Final Analysis ◽  
High Risk Population ◽  
Binary Logistic Regression Model ◽  
Chi Square ◽  
Hygiene Measures ◽  
Wet Market ◽  
Medical Grade

BACKGROUND In late December 2019, an outbreak of a novel coronavirus disease (COVID-19; previously known as 2019-nCoV) was epidemiologically linked to seafood and wet animal market in Wuhan, Hubei, China. This event has instigated negative stigma among the general population to view the wet market as a high-risk location for potential transmission of coronavirus. OBJECTIVE This study investigated the prevalence of facemask use among general population visiting the wet market as well as factors contributing to unacceptable facemask practice. Setting The visitors to a district wet market selling range of live or freshly slaughtered animals during COVID-19 pandemic outbreak was observed for facemask practice. METHODS All Individuals visiting the market were observed for the type, category and practice of wearing facemas. Subjects were categorized into two groups of acceptable and unacceptable facemask practice. The Pearson chi-square was used to test for differences in investigated variables in the univariate setting and Binary Logistic regression model was used in the multivariate setting. Main outcome measure Prevalence, acceptance practice and odds ratio of unacceptance of facemask use. RESULTS Among 1697 individuals included in the final analysis, 1687 (99.7%) was observed wearing facemask with 1338 (78.8%) using medical-grade facemask. Among them, 1615 (95.7%) individuals facemask practice was acceptable while the reaming 72 (4.3%) individuals were observed with unacceptable facemask practice. Individuals using medical-grade facemask and high-risk age group are 6.4 times (OR=6.40; 95% CI, 2.00-20.43; p=.002) and 2.06 times practice (OR=2.06; 95% CI, 1.08-3.94; p=.028) more likely to have unacceptable facemask practice respectively. CONCLUSIONS High saturation of facemask among the general population is an adequate indicator of public hygiene measures strategy which can help to mitigate the COVID-19 epidemic impact. Alarmingly, the unacceptable facemask practice among high-risk population raises the need for a targeted approach by healthcare authorities to ensure satisfactory facemask use.

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

Predictive Factors for Patients’ Failure to Show for Initial Outpatient Physical Therapist Evaluation

2021 ◽  
Author(s):  
Matthew Briggs ◽  
Christine Ulses ◽  
Lucas VanEtten ◽  
Cody Mansfield ◽  
Anthony Ganim ◽  
...  
Keyword(s):  
Predictive Factors ◽  
Employment Status ◽  
Physical Therapist ◽  
Demographic Data ◽  
Binary Logistic Regression ◽  
Final Analysis ◽  
Outpatient Setting ◽  
Binary Logistic Regression Model ◽  
Number Of Patients ◽  
Payer Source

Abstract Objective The objective of this study was to xamine primary factors which may predict patients’ failure to show at initial physical therapist evaluation in an orthopedic and sports outpatient setting. Methods A retrospective analysis of patients’ demographic data for physical therapist evaluations between January 2013 and April 2015 was performed. A binary logistic regression model was used to evaluate the odds of a no-show at evaluation. Demographic variables of age, employment status, days waited for the appointment, payer source, and distance traveled to clinic were analyzed. Independent variables were considered significant if the 95% Cis of the odds ratios did not include 1.0. Results A total of 6971 patients were included in the final analysis with 10% (n = 698) of the scheduled patients having a no-show event for their initial evaluation. The following factors increased the odds of patients having a no-show event: days to appointment (OR = 1.058; 95% CI = 1.042 to 1.074), unemployment status (OR = 1.96; 95% CI = 1.41 to 2.73), unknown employment status (OR = 3.22; 95% CI = 1.12 to 8.69), Medicaid insurance (OR = 4.87; 95% CI = 3.43 to 6.93), Medicare insurance (OR = 2.22; 95% CI = 1.10 to 4.49), unknown payer source (OR = 262.84; 95% CI = 188.72 to 366.08), and distance traveled ≥5 miles (OR = 1.31; 95% CI = 1.01 to 1.70). Female sex [OR = 0.73; 95% CI = 0.57 to 0.95) and age ≥ 40 years (OR = 0.44; 95% CI = 0.33 to 0.60) decreased the odds of a no-show event. Conclusion Results from this study indicate there may be some demographic factors that are predictive of patients failing to attend their first physical therapist visit. Impact Understanding the predictive factors and identifying potential opportunities for improvements in scheduling processes might help decrease the number of patients failing to show for their initial physical therapy appointment, with the ultimate goal of positively influencing patient outcomes.

scholarly journals Hair Cortisol Concentrations Are Associated with Dental Anxiety during Pregnancy

2021 ◽  
Vol 9 (4) ◽  
pp. 42
Author(s):  
Hilja Viitaniemi ◽  
Auli Suominen ◽  
Linnea Karlsson ◽  
Paula Mustonen ◽  
Susanna Kortesluoma ◽  
...  
Keyword(s):  
Hair Sample ◽  
Psychological Symptoms ◽  
Smoking Status ◽  
Dental Anxiety ◽  
Binary Logistic Regression ◽  
Birth Cohort Study ◽  
Hair Cortisol ◽  
Binary Logistic Regression Model ◽  
Gestational Week ◽  
Independent Association

Dental anxiety (DA) and hair cortisol concentrations (HCC) are associated with psychological symptoms and vary during pregnancy. We aimed to examine the association between HCC and DA at two points of pregnancy. Participants were pregnant mothers (n = 533) drawn from the FinnBrain Birth Cohort Study donating a hair sample at gestational week (gwk) 24 (n = 442) and/or at delivery (n = 176) and completed questionnaires on DA. Two groups, HCC1 and HCC2, treated as separate in the analyses, were formed according to the hair sample donation time i.e., gwk24 and delivery. 85 subjects were included in both groups. MDAS, EPDS, and SCL-90 were used to measure DA, depressive and anxiety symptoms, respectively, at gwk14 for the HCC1 group and gwk34 for the HCC2 group. The association between DA and HCC was studied with a binary logistic regression model, adjusted for anxiety and depressive symptoms, age, BMI, and smoking status. Individuals with high DA had lower HCC levels at gwk24 (OR = 0.548; 95% CI = 0.35–0.86; p = 0.009), but the association was not statistically significant at the delivery (OR = 0.611; 95% CI = 0.28–1.33; p = 0.216). The independent association between HCC and DA in pregnant women suggests that long-term cortisol levels could play a role in the endogenous etiology of DA. Further studies are however, needed.

scholarly journals Liver-Metabolizing Genes and Their Relationship to the Performance of Elite Spanish Male Endurance Athletes; a Prospective Transversal Study

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
David Varillas Delgado ◽  
Juan José Tellería Orriols ◽  
Carlos Martín Saborido
Keyword(s):  
Binary Logistic Regression ◽  
Endurance Performance ◽  
Endurance Athletes ◽  
Genetic Profile ◽  
Glutathione S Transferase ◽  
Binary Logistic Regression Model ◽  
Genotypic Distribution ◽  
Endurance Runner ◽  
Cytochrome P450 Family ◽  
First Time

Abstract Background The genetic profile that is needed to define an endurance athlete has been studied during recent years. The main objective of this work is to approach for the first time the study of genetic variants in liver-metabolizing genes and their role in endurance performance by comparing the allelic and genotypic frequencies in elite endurance athletes to the non-athlete population. Methods Genotypic and allelic frequencies were determined in 123 elite endurance athletes (75 professional road cyclists and 48 endurance elite runners) and 122 male non-athlete subjects (sedentary). Genotyping of cytochrome P450 family 2 subfamily D member 6 (CYP2D6 rs3892097), glutathione-S transferase mu isoform 1 (GSTM1), glutathione S-transferase pi (GSTP rs1695) and glutathione S-transferase theta (GSTT) genes was performed by polymerase chain reaction (PCR). The combination of the polymorphisms for the “optimal” polygenic profile has been quantified using the genotype score (GS). Results Statistical differences were found in the genetic distributions between elite endurance athletes and non-athletes in CYP2D6 (p < 0.001) and GSTT (p = 0.014) genes. The binary logistic regression model showed a favourable OR (odds ratio) of being an elite endurance runner against a professional road cyclist (OR: 2.403, 95% CI: 1.213–4.760 (p = 0.002)) in the polymorphisms studied. Conclusions Genotypic distribution of liver-metabolizing genes in elite endurance athletes is different to non-athlete subjects, with a favourable gene profile in elite endurance athletes in terms of detoxification capacity.

scholarly journals Association between Blood Potassium Level and Recovery of Postoperative Gastrointestinal Motility during Continuous Renal Replacement Therapy in Patient Undergoing Open Abdominal Surgery

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Yi Yang ◽  
Jingjuan Yang ◽  
Xiner Yao ◽  
Yu Cui ◽  
Xiabing Lang ◽  
...  
Keyword(s):  
Abdominal Surgery ◽  
Gastrointestinal Motility ◽  
Potassium Level ◽  
Potassium Concentration ◽  
Binary Logistic Regression ◽  
Plasma Potassium ◽  
Postoperative Recovery ◽  
Binary Logistic Regression Model ◽  
Open Abdominal Surgery ◽  
Blood Potassium

Background. The aim of this study was to identify the blood potassium level beneficial to the postoperative recovery of gastrointestinal motility during continuous renal replacement therapy (CRRT) in patient undergoing open abdominal surgery. Materials and Methods. 538 critically ill patients after open abdominal surgery and receiving CRRT were retrospectively recruited as the study cohort. Demographic and clinical data were recorded along with an evaluation of the postoperative gastrointestinal motility. Results. Correlation analysis was used to assess the correlation coefficient, and then the variables with correlation coefficient value less than 0.5 were included in the binary logistic regression model. Binary logistic regression model indicated that the postoperative blood potassium level was independently associated with the recovery of gastrointestinal motility (OR=0.109, 95% CI= 0.063 to 0.190, p<0.001). Based on the normal range of blood potassium level, we selected the cut-off point of blood potassium level via Weight of Evidence analysis, which was 4.00 mmol/L. Compared with the patients with insufficient blood potassium levels (plasma potassium concentration < 4.00 mmol/L), those with sufficient blood potassium levels (plasma potassium concentration≥ 4.00 mmol/L) conferred an increase in the rate of 4-day postoperative recovery of gastrointestinal motility (OR= 4.425, 95% CI = 2.933 to 6.667, p<0.001). Conclusions. Maintaining the blood potassium concentrations at a relatively high level of the normal blood potassium range during CRRT would be beneficial to postoperative recovery of gastrointestinal motility.

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