scholarly journals Patterns of Education Financing and Debt: A Comparison of Two Cohorts of Canadian Post-Secondary Graduates

1996 ◽  
Vol 26 (2) ◽  
pp. 23-46
Author(s):  
Robert D. Hiscott
Keyword(s):  
Community College ◽  
Multiple Regression ◽  
Student Loans ◽  
Post Secondary ◽  
Explanatory Variables ◽  
College And University ◽  
Recent Graduates ◽  
Statistics Canada ◽  
Using Data ◽  
Recent Cohort

Using data from 1988 and 1992 National Graduates Surveys (conducted by Statistics Canada), this paper explores educational financing and debt patterns for recent graduates of Canadian community college and university programs. A majority of recent post-secondary graduates borrowed to finance their education at some point during their educational programs through the Canada Student Loans Program and/or other sources. The more recent cohort of post-secondary graduates (1990 graduates interviewed in 1992) reported markedly higher debt loads and significantly greater amounts owing two years after graduation, relative to the earlier cohort (of 1986 graduates surveyed in 1988). Multiple regression models are developed and tested to predict the amount of debt (in dollars) owed by graduates approximately two years after completion of their programs. Key explanatory variables of (1) total amount borrowed, (2) university or community college program graduate, (3) number of months not employed between graduation and time of interview, (4) current job temporary or not, and (5) current employment income were all found to be highly significant for the most recent cohort of post-secondary graduates. However, there are important differences in multiple regression results between the two cohorts which are discussed in detail in the paper.

The Kitchen Sink

2018 ◽  
Author(s):  
Gary Smith
Keyword(s):  
Multiple Regression ◽  
Historical Period ◽  
Scatter Plot ◽  
Consumer Spending ◽  
Explanatory Variables ◽  
The World ◽  
Scatter Plots ◽  
Using Data ◽  
The One ◽  
Over Time

Back in the 1980s, I talked to an economics professor who made forecasts for a large bank based on simple correlations like the one in Figure 1. If he wanted to forecast consumer spending, he made a scatter plot of income and spending and used a transparent ruler to draw a line that seemed to fit the data. If the scatter looked like Figure 1, then when income went up, he predicted that spending would go up. The problem with his simple scatter plots is that the world is not simple. Income affects spending, but so does wealth. What if this professor happened to draw his scatter plot using data from a historical period in which income rose (increasing spending) but the stock market crashed (reducing spending) and the wealth effect was more powerful than the income effect, so that spending declined, as in Figure 2? The professor’s scatter plot of spending and income will indicate that an increase in income reduces spending. Then, when he tries to forecast spending for a period when income and wealth both increase, his prediction of a decline in spending will be disastrously wrong. Multiple regression to the rescue. Multiple regression models have multiple explanatory variables. For example, a model of consumer spending might be: C = a + bY + cW where C is consumer spending, Y is household income, and W is wealth. The order in which the explanatory variables are listed does not matter. What does matter is which variables are included in the model and which are left out. A large part of the art of regression analysis is choosing explanatory variables that are important and ignoring those that are unimportant. The coefficient b measures the effect on spending of an increase in income, holding wealth constant, and c measures the effect on spending of an increase in wealth, holding income constant. The math for estimating these coefficients is complicated but the principle is simple: choose the estimates that give the best predictions of consumer spending for the data used to estimate the model. In Chapter 4, we saw that spurious correlations can appear when we compare variables like spending, income, and wealth that all tend to increase over time.

scholarly journals The Impact of Work Characteristics on Social Distancing: Implications at the Time of COVID-19

2021 ◽  
Vol 18 (10) ◽  
pp. 5074
Author(s):  
Keisuke Kokubun ◽  
Yoshinori Yamakawa
Keyword(s):  
Information Processing ◽  
Multiple Regression ◽  
Explanatory Power ◽  
Work Conditions ◽  
Multiple Regression Model ◽  
Social Characteristics ◽  
Work Characteristics ◽  
Social Distancing ◽  
Using Data ◽  
The Impact

The coronavirus disease (COVID-19) continues to spread globally. While social distancing has attracted attention as a measure to prevent the spread of infection, some occupations find it difficult to implement. Therefore, this study aims to investigate the relationship between work characteristics and social distancing using data available on O*NET, an occupational information site. A total of eight factors were extracted by performing an exploratory factor analysis: work conditions, supervisory work, information processing, response to aggression, specialization, autonomy, interaction outside the organization, and interdependence. A multiple regression analysis showed that interdependence, response to aggression, and interaction outside the organization, which are categorized as ”social characteristics,” and information processing and specialization, which are categorized as “knowledge characteristics,” were associated with physical proximity. Furthermore, we added customer, which represents contact with the customer, and remote working, which represents a small amount of outdoor activity, to our multiple regression model, and confirmed that they increased the explanatory power of the model. This suggests that those who work under interdependence, face aggression, and engage in outside activities, and/or have frequent contact with customers, little interaction outside the organization, and little information processing will have the most difficulty in maintaining social distancing.

scholarly journals Training for policy capacity: A practitioner’s reflection on an in-house intervention for civil servants, students, and post-secondary graduates in Canada

2021 ◽  
pp. 014473942110194
Author(s):  
Bobby Thomas Cameron
Keyword(s):  
Prince Edward Island ◽  
Provincial Government ◽  
Civil Servants ◽  
Mentorship Program ◽  
Post Secondary ◽  
Policy Capacity ◽  
Public Servant ◽  
Capacity Theory ◽  
Integrated Policy ◽  
Recent Graduates

A substantial amount of scholarly work focuses on conceptualizing, theorizing and studying the policy capacity of governments. Yet, guidance for practitioners on developing policy capacity training programs is lacking. In this article, I reflect on my experience as a public servant in the provincial government of Prince Edward Island where I designed and implemented the Policy Capacity Development and Mentorship Program for civil servants, recent graduates and students. In this article, I offer a descriptive overview of the framework and logic of the program and discuss how I integrated policy capacity theory. This article may serve other practitioners who seek to implement similar programs in their respective organizations and provides a base for future interventions. The article also offers thoughts on practitioner-led collaboration with academics and recommendations for those who would like to establish similar programs in their organizations.

scholarly journals Mothers of children with food allergies report poorer perceived life status which may be explained by limited career choices

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Tara Lynn Mary Frykas ◽  
Michael Golding ◽  
Elissa M. Abrams ◽  
Elinor Simons ◽  
Jennifer Lisa Penner Protudjer
Keyword(s):  
Food Allergy ◽  
Low Income ◽  
Career Choices ◽  
Food Allergies ◽  
Allergic Child ◽  
Intangible Costs ◽  
Statistics Canada ◽  
Using Data

AbstractPediatric food allergy is associated with direct, indirect and intangible costs. However, it remains unclear if intangible costs of pediatric food allergy influence parental career choices. Using data from 63 parents whose children had been diagnosed by a pediatric allergist with food allergy, we sought to (a) establish perceived life status of families with a food allergic child, and (b) to describe any career limitations viewed as attributable to food allergy. Compared to responding parents whose children had one to two food allergies, those with three or more food allergies had significantly poorer perceived life status (ß − 0.74; 95%CI − 1.41; − 0.07; p < 0.05). Overall, 14.3% of parents (all mothers) reported career limitations due to food allergy. Two of the 7 mothers (28.6%) who reported career limitations due to their child's food allergy fell below Statistics Canada cut-off for low-income, after tax dollars (LIM-AT). One of the three mothers who had changed jobs because of their child's food allergy was below the LIM-AT. No fathers reported food allergy-related career limitations. In conclusion, mothers of children with multiple food allergies reported worse perceived life status that may be partly explained by food allergy-related career limitations.

The Effects of the Expansion of For-Profit Colleges on Student Enrollments and Outcomes at Community Colleges

2018 ◽  
Vol 40 (4) ◽  
pp. 631-652 ◽  
Author(s):  
Adela Soliz
Keyword(s):  
Community College ◽  
Postsecondary Education ◽  
Large Scale ◽  
Higher Education System ◽  
For Profit ◽  
Education Data ◽  
Education Levels ◽  
Short And Long Term ◽  
Using Data ◽  
The Impact

This study is the first large-scale examination of the impact of for-profit colleges on the enrollment and outcomes of students at other postsecondary institutions. Using data primarily from the Integrated Postsecondary Education Data System (IPEDS) and a differences-in-differences approach, I estimate the effect of a new for-profit college opening on community college enrollments and degree completions, as well as county education levels. My results suggest that community college enrollments and degree completions do not decline when a new degree-granting for-profit college opens nearby. Furthermore, I find evidence that the county-level production of short- and long-term certificates increases after a new for-profit college opens, though the number of associate’s degrees does not increase. This evidence should serve to broaden conversations about the role of for-profit colleges in the larger landscape of the American higher education system.

scholarly journals Analisis Nisbah dan Angsuran Terhadap Pembiayaan Kredit Usaha Rakyat (KUR) di BriSyariah KCP Genteng Banyuwangi

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Nur Anim Jauhariyah ◽  
Ahmad Saiful Amin
Keyword(s):  
Data Collection ◽  
Small Business ◽  
Multiple Regression ◽  
Quantitative Approach ◽  
Analysis Tool ◽  
Independent Variables ◽  
Using Data

This study uses a quantitative approach by determining the research sample using data collection techniques obtained from the number of respondents 24 customers. The independent variables of this study are Nisbah (X1) and Installments (X2), while the dependent variable (Y) is Financing using micro small business credit products (KUR) iB. The analysis tool uses multiple regression with the help of SPSS software.The results of the study 1) Significantly the ratio (X1) has an effect on the financing (Y) of the people's business credit (KUR) micro and small iB products; 2) Significantly the installment (X2) has no effect on the financing (Y) of the people's business credit (KUR), micro and small iB; 3) Simultaneously, the ratio (X1) and installments (X2) have a significant effect on the financing (Y) of the iB micro and small business credit (KUR) product at BRISyariah KCP Genteng, Banyuwangi Regency.

scholarly journals Income differentials on regional labour markets in Southwest Germany

2009 ◽  
Vol 56 (3) ◽  
pp. 379-396
Author(s):  
Alice Guyot ◽  
Stefan Berwing ◽  
Maria Lauxen-Ulbrich
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Descriptive Statistics ◽  
Pay Gap ◽  
Explanatory Variables ◽  
Income Differentials ◽  
Income Disparities ◽  
Regional Labour Markets ◽  
Using Data ◽  
Regional Types

The aim of our paper is to identify explanatory variables for income disparities between women and men across different regional types. Using data from the BA Employment Panel (BEP) descriptive statistics show that the gender pay gap grows wider from core regions to periphery. The main explanatory variables for the income differentials are vocational education in the men's case and size of enterprise in the women's case. Whereas in the case of women the importance of vocational status increases and the importance of size of enterprise decreases from rural areas to urban areas.

scholarly journals Effects of Touch on Students’ Stress, Happiness, and Well-Being During Animal-Assisted Activities

2021 ◽  
Vol 5 (1) ◽  
pp. 111
Author(s):  
Laura Sokal ◽  
Brianne Bartel ◽  
Taylor Martin
Keyword(s):  
Mental Health ◽  
North America ◽  
Secondary Students ◽  
Repeated Measures ◽  
Research Study ◽  
Well Being ◽  
Post Secondary ◽  
College And University ◽  
Positive Effects ◽  
Repeated Measures Design

Post-secondary institutions across North America have adopted animal-assisted activities as a way to promote better mental health in their students. The current research study of 242 Canadian college and university students sought to contribute to our collective understanding of the aspects of the programs and characteristics of students that are related to promotion of better mental health in post-secondary students including decreased stress, and increased happiness and well-being. Results of a repeated measures design showed that students demonstrated greater positive effects on stress, happiness, and well-being when they touched dogs as compared to when they observed them. Furthermore, positive mental health outcomes were correlated with greater durations of contact as well as with higher levels of animal affiliation in students. Implications for post-secondary institutions are discussed. 

scholarly journals Monitoring antimicrobial resistance trends in commensal Escherichia coli from livestock, the Netherlands, 1998 to 2016

2019 ◽  
Vol 24 (25) ◽  
Author(s):  
Ayla Hesp ◽  
Kees Veldman ◽  
Jeanet van der Goot ◽  
Dik Mevius ◽  
Gerdien van Schaik
Keyword(s):  
Escherichia Coli ◽  
Antimicrobial Resistance ◽  
Monitoring Data ◽  
Resistant Isolate ◽  
Similar Data ◽  
Explanatory Variables ◽  
E Coli ◽  
Veal Calves ◽  
Before And After ◽  
Using Data

Background Monitoring of antimicrobial resistance (AMR) in animals is essential for public health surveillance. To enhance interpretation of monitoring data, evaluation and optimisation of AMR trend analysis is needed. Aims To quantify and evaluate trends in AMR in commensal Escherichia coli, using data from the Dutch national AMR monitoring programme in livestock (1998–2016). Methods Faecal samples were collected at slaughter from broilers, pigs and veal calves. Minimum inhibitory concentration values were obtained by broth microdilution for E. coli for 15 antimicrobials of eight antimicrobial classes. A Poisson regression model was applied to resistant isolate counts, with explanatory variables representing time before and after 2009 (reference year); for veal calves, sampling changed from 2012 represented by an extra explanatory variable. Results Resistant counts increased significantly from 1998-2009 in broilers and pigs, except for tetracyclines and sulfamethoxazole in broilers and chloramphenicol and aminoglycosides in pigs. Since 2009, resistant counts decreased for all antimicrobials in broilers and for all but the phenicols in pigs. In veal calves, for most antimicrobials no significant decrease in resistant counts could be determined for 2009–16, except for sulfamethoxazole and nalidixic acid. Within animal species, antimicrobial-specific trends were similar. Conclusions Using Dutch monitoring data from 1998-2016, this study quantified AMR trends in broilers and slaughter pigs and showed significant trend changes in the reference year 2009. We showed that monitoring in commensal E. coli is useful to quantify trends and detect trend changes in AMR. This model is applicable to similar data from other European countries.

scholarly journals A Review on Linear Regression Comprehensive in Machine Learning

2020 ◽  
Vol 1 (4) ◽  
pp. 140-147
Author(s):  
Dastan Maulud ◽  
Adnan M. Abdulazeez
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Linear Relationship ◽  
Multiple Regression ◽  
Polynomial Regression ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Explanatory Variables ◽  
Almost All ◽  
Simple Regression

Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

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