scholarly journals Influence of year-on-year performance on final degree classification in a chiropractic master's degree program

2016 ◽  
Vol 30 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Philip Dewhurst ◽  
Jacqueline Rix ◽  
David Newell
Keyword(s):  
Logistic Regression ◽  
Problem Solving ◽  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
The Difference ◽  
Degree Classification ◽  
Grade Performance ◽  
Final Degree

Objective: We explored if any predictors of success could be identified from end-of-year grades in a chiropractic master's program and whether these grades could predict final-year grade performance and year-on-year performance. Methods: End-of-year average grades and module grades for a single cohort of students covering all academic results for years 1–4 of the 2013 graduating class were used for this analysis. Analysis consisted of within-year correlations of module grades with end-of-year average grades, linear regression models for continuous data, and logistic regression models for predicting final degree classifications. Results: In year 1, 140 students were enrolled; 85.7% of students completed the program 4 years later. End-of-year average grades for years 1–3 were correlated (Pearson r values ranging from .75 to .87), but the end-of-year grades for years 1–3 were poorly correlated with clinic internship performance. In linear regression, several modules were predictive of end-of-year average grades for each year. For year 1, logistic regression showed that the modules Physiology and Pharmacology and Investigative Imaging were predictive of year 1 performance (odds ratio [OR] = 1.15 and 0.9, respectively). In year 3, the modules Anatomy and Histopathology 3 and Problem Solving were predictors of the difference between a pass/merit or distinction final degree classification (OR = 1.06 and 1.12, respectively). Conclusion: Early academic performance is weakly correlated with final-year clinic internship performance. The modules of Anatomy and Histopathology year 3 and Problem Solving year 3 emerged more consistently than other modules as being associated with final-year classifications.

scholarly journals Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

scholarly journals Asymmetric Fixed-effects Models for Panel Data

2019 ◽  
Vol 5 ◽  
pp. 237802311982644 ◽  
Author(s):  
Paul D. Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

Performance simulations for categorical mediation: Analyzing khb estimates of mediation in ordinal regression models

2019 ◽  
Vol 19 (4) ◽  
pp. 913-930 ◽  
Author(s):  
E. Keith Smith ◽  
Michael G. Lacy ◽  
Adam Mayer
Keyword(s):  
Logistic Regression ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Experimental Conditions ◽  
Logistic Regression Models ◽  
Nonlinear Regression Models ◽  
Size Number ◽  
Confidence Interval Coverage ◽  
The Difference

Standard mediation techniques for fitting mediation models cannot readily be translated to nonlinear regression models because of scaling issues. Methods to assess mediation in regression models with categorical and limited response variables have expanded in recent years, and these techniques vary in their approach and versatility. The recently developed khb technique purports to solve the scaling problem and produce valid estimates across a range of nonlinear regression models. Prior studies demonstrate that khb performs well in binary logistic regression models, but performance in other models has yet to be investigated. In this article, we evaluate khb‘s performance in fitting ordinal logistic regression models as an exemplar of the wider set of models to which it applies. We examined performance across 38,400 experimental conditions involving sample size, number of response categories, distribution of variables, and amount of mediation. Results indicate that under all experimental conditions, khb estimates the difference (mediation) coefficient and its associated standard error with little bias and that the nominal confidence interval coverage closely matches the actual. Our results suggest that researchers using khb can assume that the routine reasonably approximates population parameters.

Ordinal regression model and the linear regression model were superior to the logistic regression models

2006 ◽  
Vol 59 (5) ◽  
pp. 448-456 ◽  
Author(s):  
Colleen M. Norris ◽  
William A. Ghali ◽  
L. Duncan Saunders ◽  
Rollin Brant ◽  
Diane Galbraith ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Regression Models ◽  
Ordinal Regression ◽  
Logistic Regression Models ◽  
Ordinal Regression Model

scholarly journals Does healthcare inequity reflect variations in peoples’ abilities to access healthcare? Results from a multi-jurisdictional interventional study in two high-income countries

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Jeannie Haggerty ◽  
Jean-Frederic Levesque ◽  
Mark Harris ◽  
Catherine Scott ◽  
Simone Dahrouge ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Primary Healthcare ◽  
Social Vulnerability ◽  
Regression Models ◽  
Social Systems ◽  
Social Characteristics ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
Wide Range ◽  
Social Vulnerabilities

Abstract Background Primary healthcare services must respond to the healthcare-seeking needs of persons with a wide range of personal and social characteristics. In this study, examined whether socially vulnerable persons exhibit lower abilities to access healthcare. First, we examined how personal and social characteristics are associated with the abilities to access healthcare described in the patient-centered accessibility framework and with the likelihood of reporting problematic access. We then examined whether higher abilities to access healthcare are protective against problematic access. Finally, we explored whether social vulnerabilities predict problematic access after accounting for abilities to access healthcare. Methods This is an exploratory analysis of pooled data collected in the Innovative Models Promoting Access-To-Care Transformation (IMPACT) study, a Canadian-Australian research program that aimed to improve access to primary healthcare for vulnerable populations. This specific analysis is based on 284 participants in four study regions who completed a baseline access survey. Hierarchical linear regression models were used to explore the effects of personal or social characteristics on the abilities to access care; logistic regression models, to determine the increased or decreased likelihood of problematic access. Results The likelihood of problematic access varies by personal and social characteristics. Those reporting at least two social vulnerabilities are more likely to experience all indicators of problematic access except hospitalizations. Perceived financial status and accumulated vulnerabilities were also associated with lower abilities to access care. Higher scores on abilities to access healthcare are protective against most indicators of problematic access except hospitalizations. Logistic regression models showed that ability to access is more predictive of problematic access than social vulnerability. Conclusions We showed that those at higher risk of social vulnerability are more likely to report problematic access and also have low scores on ability to seek, reach, pay, and engage with healthcare. Equity-oriented healthcare interventions should pay particular attention to enhancing people’s abilities to access care in addition to modifying organizational processes and structures that reinforce social systems of discrimination or exclusion.

Development of post-fire crown damage mortality thresholds in ponderosa pine

2010 ◽  
Vol 19 (5) ◽  
pp. 583 ◽  
Author(s):  
James F. Fowler ◽  
Carolyn Hull Sieg ◽  
Joel McMillin ◽  
Kurt K. Allen ◽  
Jose F. Negrón ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Forest Management ◽  
Linear Regression ◽  
Linear Relationship ◽  
Ponderosa Pine ◽  
Regression Models ◽  
Intermountain West ◽  
Logistic Regression Models ◽  
Crown Scorch ◽  
Mortality Analysis

Previous research has shown that crown scorch volume and crown consumption volume are the major predictors of post-fire mortality in ponderosa pine. In this study, we use piecewise logistic regression models of crown scorch data from 6633 trees in five wildfires from the Intermountain West to locate a mortality threshold at 88% scorch by volume for trees with no crown consumption. For trees with >40% crown consumption volume, linear regression indicates >85% mortality, but for trees with crown consumption volume <40%, there is a statistically significant, linear relationship between increasing crown scorch and increasing probability of mortality. Analysis of an independent 600+ tree dataset from Colorado produced similar results and supports the analysis approach. Crown scorch volume (>85%), crown consumption volume (>40%), and crown consumption between 5 and 40% combined with crown scorch volume >50% mortality thresholds could be incorporated into post-fire marking guidelines for forest management goals.

Physical activity accrued as part of public transport use in England

2018 ◽  
Vol 41 (2) ◽  
pp. 222-230 ◽  
Author(s):  
R Patterson ◽  
E Webb ◽  
C Millett ◽  
A A Laverty
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Confidence Interval ◽  
Public Transport ◽  
Regression Models ◽  
Daily Life ◽  
Active Travel ◽  
Linear Regression Models ◽  
Physical Activity Guidelines ◽  
Logistic Regression Models

Abstract Background Walking and cycling for transport (active travel) is an important source of physical activity with established health benefits. However, levels of physical activity accrued during public transport journeys in England are unknown. Methods Using the English National Travel Survey 2010–14 we quantified active travel as part of public transport journeys. Linear regression models compared levels of physical activity across public transport modes, and logistic regression models compared the odds of undertaking 30 min a day of physical activity. Results Public transport users accumulated 20.5 min (95% confidence interval=19.8, 21.2) a day of physical activity as part of public transport journeys. Train users accumulated 28.1 min (26.3, 30.0) with bus users 16.0 min (15.3, 16.8). Overall, 34% (32%, 36%) of public transport users achieved 30 min a day of physical activity in the course of their journeys; 21% (19%, 24%) of bus users and 52% (47%, 56%) of train users. Conclusion Public transport use is an effective way to incorporate physical activity into daily life. One in three public transport users meet physical activity guidelines suggesting that shifts from sedentary travel modes to public transport could dramatically raise the proportion of populations achieving recommended levels of physical activity.

Antipsychotic Medication Dispensing and Associated Odds Ratios of Death In Elderly Veterans and War Widows, 2001

2006 ◽  
Vol 40 (11-12) ◽  
pp. 981-986 ◽  
Author(s):  
Jean Hollis ◽  
Stephen Touyz ◽  
David Grayson ◽  
Loelle Forrester
Keyword(s):  
Logistic Regression ◽  
Drug Toxicity ◽  
Regression Models ◽  
Reference Group ◽  
Antidepressant Medication ◽  
Mortality Rates ◽  
Elderly Subjects ◽  
Odds Ratios ◽  
Logistic Regression Models ◽  
The Difference

Objectives: To explore the odds ratios (ORs) of death associated with antipsychotic (AP) medications dispensed to elderly subjects. Method: Subjects were veterans and war widows 65 years and older dispensed an AP drug in 2001 in NSW or ACT. For all subjects, dispensing records for AP medication, benzodiazepines, lithium, carbamazepine, sodium valproate and antidepressant medication were extracted and combined with age, gender and date of death. A study date was allocated, either the date of death or a random date from 1.5.01 to 31.12.01. Subjects dispensed an AP in 2001, but not dispensed an AP or other psychotropic medication in the 120 days prior to their study date, formed a reference group. Psychotropic dispensing in the 120 days prior to the study date was analysed using nested logistic regression models to produce ORs of death associated with various AP drugs. The ORs for risperidone, olanzapine and pericyazine were compared. Haloperidol ORs were established for those dispensed the drug 0–30 days prior to study date or 31–120 days prior to the study date. Results: The ORs associated with haloperidol, olanzapine, risperidone, pericyazine, thioridazine and chlorpromazine were significant when compared with the reference group. Odds ratios for all three haloperidol periods were significant when compared with olanzapine, risperidone and pericyazine 120 day ORs. Although there was a trend favouring olanzapine when compared with risperidone, the difference in the ORs failed to reach significance (p = 0.066). Conclusions: Haloperidol is associated with significantly higher mortality rates than other AP medication but it is not clear whether this represents drug toxicity or the medical conditions for which it was dispensed. There was no evidence that the conventional AP pericyazine was associated with a higher mortality rate than olanzapine or risperidone.

A Study of the Atmospheric Weighting Mean Temperature in Northwest Plateau of China

2011 ◽  
Vol 137 ◽  
pp. 291-296
Author(s):  
Jing Jiang Zhang ◽  
Yan Li Chu ◽  
Ji Qin Zhong
Keyword(s):  
Water Vapor ◽  
Linear Regression ◽  
Inner Mongolia ◽  
Regression Models ◽  
Precipitable Water ◽  
Linear Regression Models ◽  
Mean Temperature ◽  
Tm Model ◽  
The Difference ◽  
Data Collections

The data from 11 meteorological radiosonde stations in 5 provinces including Shanxi, Shaanxi, Ningxia, Inner Mongolia and Hebei are divided into 9 different data collections which are used to deduce the linear regression models of atmospheric weighting mean temperature (Tm) for Ground-based GPS precipitable water vapor (PWV) retrieval. These 9 models, together with Bevis model, are used to retrieve the GPS PWV at station BGTY. In comparison with the correlations between the ground-based GPS PWV and radiosonde PWV at this station, the difference between these 10 different models of Tm is analyzed. The results show that the Bevis model of Tm can be used to retrieve the GPS PWV of the regions mentioned above. At the same time, the Tm model computed from the radiosonde measurements of specific regions and seasons can provide more accurate GPS PWV than the Bevis model.

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