Regression (Distance from School)

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Free download from www.hsrc p ress.ac.za Compiled by the Research Programme on Human Resources Development, Human Sciences Research Council (Executive Director: Dr Andre Kraak) Published by the Human Sciences Research Council Publishers Private Bag X9182, Cape Town, 8000, South Africa © Human Sciences Research Council 2002 First published 2002 All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. ISBN 0-7969-2005-2 Cover design by FUEL Design Produced by comPress Distributed in South Africa by Blue Weaver Marketing and Distribution, P.O. Box 30370, Tokai, Cape Town, South Africa, 7966. Tel/Fax: (021) 701-7302, email: blueweav@mweb.co.za Free download from www.hsrc p ress.ac.za This report is the culmination of a process in which a dedicated project team in the Research Programme on Human Resources Development (HRD) at the Human Sciences Research Council (HSRC) was involved. I should like to pay tribute to the following team members: • Jacques du Toit for research and instrument design; sampling; questionnaire piloting; fieldwork conceptualisation and fieldworker training; survey logistics management; data capture management and analysis. • Dr Andre Kraak for project conceptualisation; research and instrument design. • Lindi Basson for project, fieldwork, and financial administration; fieldwork conceptualisation; fieldworker recruitment and management; survey logistics management. • Mariette Visser for sampling; schools database management. • Dr Tom Magau for instrument design; fieldworker training; fieldwork administration of questionnaires; questionnaire tallying. • Mmamajoro Shilubane for instrument design; fieldworker training; questionnaire tallying. • Dr Isaac Ntshoe for questionnaire tallying. • Salim Akoojee for questionnaire tallying. In addition, I should like to thank: • Dr Jacques Pietersen, chief statistician in the Research Programme on Surveys, Analyses, Modelling and Mapping at the HSRC, for his assistance in designing and drawing the sample for the survey, weighting the data, conceptualising the CHAID and regression analyses, and assisting in the interpretation of the findings; • Dr Andre Kraak, Executive Director of the Research Programme on Human Resources Development, for his critical insights into key issues raised by the find- ings of the report; • Debbie Budlender, Principal Researcher at the Community Agency for Social Enquiry (CASE), for the very helpful advice on statistical interpretation and expression proffered in her critical reading of the report; and • Jane Hendry, Institutional Planner in the Institutional Planning Department, University of Cape Town, for her invaluable comments on the report from a higher education perspective. Finally, I should like to express my thanks to all those whose involvement in the project before and during the fieldwork stage made this study possible: • Prof Peter Maassen, Director of the Higher Education Development Association (Hedda) at the University of Oslo, for providing the initial impetus for the study; • Dr Nico Cloete, Director of the Centre for Higher Education Transformation (CHET), for facilitating the commissioning of the HSRC to undertake the research; • The Higher Education Branch of the Department of Education – in particular, its Deputy Director-General, Nasima Badsha, for her continuous support of the project; • The nine provincial education departments, for giving us access to schools; • The 288 schools nationwide that allowed us to survey their Grade 12 learners; and • The 12 204 respondents to the survey. Michael Cosser Project Manager Acknowledgements Free download from Regression (Distance from School) Regression (Distance from School) By: OpenStaxCollege Regression (Distance from School) Class Time: Names: Student Learning Outcomes • The student will calculate and construct the line of best fit between two variables • The student will evaluate the relationship between two variables to determine if that relationship is significant Collect the DataUse eight members of your class for the sample Collect bivariate data (distance an individual lives from school, the cost of supplies for the current term) Complete the table Distance from school Cost of supplies this term Which variable should be the dependent variable and which should be the independent variable? Why? Graph “distance” vs “cost.” Plot the points on the graph Label both axes with words Scale both axes 1/3 Regression (Distance from School) Analyze the DataEnter your data into your calculator or computer Write the linear equation, rounding to four decimal places Calculate the following: a = b = correlation = n = equation: ŷ = Is the correlation significant? Why or why not? (Answer in one to three complete sentences.) Supply an answer for the following senarios: For a person who lives eight miles from campus, predict the total cost of supplies this term: For a person who lives eighty miles from campus, predict the total cost of supplies this term: Obtain the graph on your calculator or computer Sketch the regression line Discussion Questions Answer each question in complete sentences Does the line seem to fit the data? Why? What does the correlation imply about the relationship between the distance and the cost? Are there any outliers? If so, which point is an outlier? 2/3 Regression (Distance from School) Should the outlier, if it exists, be removed? Why or why not? 3/3 1 So, you want to go to grad school in Economics? A practical guide of the first years (for outsiders) from insiders Ceyhun Elgin Mario Solis-Garcia University of Minnesota April 2007 2 Introduction You may be wondering about our intentions in writing this document. A bit of background information may be useful here. We, along with many other young, bright students across the U.S. and other countries, were fascinated with the idea of enrolling in a Ph.D. program in economics, but were missing the big picture of the process, as well of the outcome! For our case, it is true that we are still working for the degree, but after one year and a half in the program (as of December 2006), we think that the experience may not be for everyone (but may be more exciting for some than for others!). This document is intended to provide, to the best of our knowledge, an accurate picture of the life before and during a Ph.D. program in economics. Our hope is that potentially interested students may benefit from our experience. As could be expected from a theme such as this one, a usual disclaimer arises. The conclusions expressed within are based from our experience in the economics program at the University of Minnesota; we expect many of the features discussed below to be fairly similar among schools. However, the experience might be different in other programs, within the U.S. or across the world! Our paper first will discuss what you should do before going to the Ph.D. Then we will try to give you some flavor of the life in the first year of the program, followed by our conclusion. So you’ve decided to go for a Ph.D. program Getting a Ph.D. degree is easier said than done. Your undergraduate or master’s advisor is right: It’s going to be difficult. In fact, we can agree and go even further: It’s going to be the most difficult experience of your life. However, should you decide to go for it, the rewards are easily greater than the costs. The first moral of the story: Think twice before you decide! 3 In our view, graduate work in economics (or in any other discipline, for that matter) is an exercise in discipline, endurance, hard work, and patience. It is not so easy having all 4 at the same time… but you’ll get used to it. The early work: tests So, suppose you are really decided to go on the adventure of a Ph.D. program. Congratulations! But now you have to get to work. You should have heard from everyone: “Take the GRE test.” Well, we’ll also say it: Take the GRE test. And take it early. Even though most universities suggest taking the test no later than the December before the year to enter the program, we suggest taking it no later than June or July of the year before entering the program. Why? For starters, bad luck happens. You can take special courses, study for a couple of months, score 800s on your practice tests, but it all comes down to one particular test. And here’s where the bad luck comes into play. If you don’t get the score you expect, you Best Practices in Social Media Summary of Findings from the Second Comprehensive Study of Social Media Use by Schools, Colleges and Universities April 13, 2011 In collaboration with the Council for Advancement and Support of Education (CASE) C A S E S o c i a l M e d i a S u r v e y • S u m m a r y T o p li n e Fin dings • A p r i l 1 3 , 2011 2. Overview of findings Background Slover Linett Strategies Inc. and mStoner partnered with CASE for the second year in a row in order to research educational institutions about their social media activities. Last year’s inaugural study was, from what we could tell, one of the first studies conducted of the institutions themselves about how they use social media. Our overarching goal of this research is to create knowledge and resources to assist education professionals as they assess and implement social media strategies for communication and engagement. Some of the questions we hoped it would answer include:  How do professionals in education incorporate social media into their marketing and communication strategies, initiatives, and campaigns?  What are their goals for social media?  Which social media sites and tools are in use in the education sector and which are most successful?  What are current best practices?  How do they staff their social media efforts?  How are schools measuring the ROI or impact of social media initiatives?  What contributes to the successful use of social media?  What are the barriers to the effective use of social media?  What does the near future look like for social media in education? We conducted an online survey among a random sample of approximately 18,000 CASE members, including contacts in admissions and enrollment departments – in the US and abroad. We received nearly 951 (on par with last year’s response) across all types of institutions – a testament to the interest in this topic. We are just beginning to mine the data from this second round of research and will be releasing a full white paper later in the year. If you’re interested in receiving this white paper, please email mstoner@mstoner.com. For any questions about the study please contact: Cheryl Slover-Linett President Slover Linett Strategies Inc. 773-348-9204 cheryl@sloverlinett.com Thanks for your interest. C A S E S o c i a l M e d i a S u r v e y • S u m m a r y T o p li n e Fin dings • A p r i l 1 3 , 2011 3. Initial findings Note that questions 1–7 are for profiling purposes to ensure the representativeness of the respondent base. 1. Are you affiliated with an institution in: North America 88% Europe 8% Asia Pacific 3% Other 2% 2. [INTERNATIONAL ONLY] What type of institution do you work at? Higher education/university 76% Elementary/primary/secondary/high school 11% Special focus institution (e.g. stand-alone business school, medical © 2008 Research Consortium on Educational Outcomes and Poverty WP08/15 RECOUP Working Paper No. 15 RECOUP Working Paper No. 15 Schooling, transitions and reproductive citizenship for poor people in urban and rural north India: Preliminary results from Alwar and Dewas Claire Noronha, Roger Jeffery and Patricia Jeffery with the RECOUP India Research Team Claire Noronha, Roger Jeffery and Patricia Jeffery with the RECOUP India Research Team 1 1 Abstract Abstract Exactly how schooling affects young women’s ‘autonomy’, especially with respect to her fertility and the life-chances of her children, is a contested issue. We draw on semi-structured interviews with young married women with at least one child under the age of six, in urban and rural areas of Rajasthan and Madhya Pradesh, north India, to elaborate differences in attitudes and experiences in early married life between young married women with at least eight years of schooling and those with little or no formal schooling. All the women in our sample come from India’s most disadvantaged social groups—Scheduled or Other Backward Castes—and live in disadvantaged communities. Tentative conclusions include that women with 10 years or more schooling have very different aspirations about their life partner and married life, and are better able to negotiate relationships with their mother-in-law than do the women with little or no formal schooling experience. Keywords: female autonomy, fertility, education, India Acknowledgements: An earlier version of this paper was presented at the UKFIET conference ‘Going for Growth? School, Community, Economy, Nation’, 11 – 13 September 2007, Oxford. We are grateful for comments on earlier drafts of this paper from Sara Ruto, Feyza Bhatti and Shailaja Fennell. This paper forms part of the Research Consortium on Educational Outcomes and Poverty (RECOUP). CORD is the Indian partner for RECOUP research. Neither DFID nor any of the partner institutions are responsible for any of the views expressed here. JEL Classification: J13, I29, N35 Correspondence: CORD, G-18/1 Nizamuddin West, New Delhi 110 013, India, Tel: +91 11 24356085. Email: cordrpc@gmail.com 1 The data used in this paper was collected by the CORD team: Sharmishta Basu, Rashi Bhargava, Aanchal Jain, Srimanti Mukherjee, Anubha Prakash, Claire Noronha and Manjula Sharma. Rashi Bhargava, Aanchal Jain, Srimanti Mukherjee and Anubha Prakash did the coding and summaries on Atlas ti. Sharmishta Basu provided useful input including analysis of quantitative data from NFHS 2 and 3 and RCH data on the sample districts. Roger and Patricia Jeffery provided the research design and the latter gave helpful technical inputs at the outset of the fieldwork, documentation and coding. 1 Introduction Since the early 1980s, much time and effort has been expended in trying to understand the relative contributions of different social factors in mortality decline (especially declines in infant and child mortality) and in reductions in fertility. In these debates, the schooling level (number of years attended) of a young mother has often been portrayed as the single most powerful correlate of reductions in the infant and child mortality of her children, and (to a lesser but still very considerable degree) to reduction in her completed family size. These correlations have been observed in almost every country and region at some point in time, using a variety of macro-economic and macro- demographic What are the lessons learned from school life? In your opinion, what are the lessons learned from school life? Whether one’s school-days are happy or the reverse, depends a good deal on the character of the school in which one’s lot in cast; for schools are of all kinds good, bad and indifferent. But most schools nowadays are respectable, and some are very good; and most of us look back upon our school life as, on the whole, a happy time. Of course, a small boy who first goes to school (especially if it is a boarding-school) does not at the beginning feel very happy. He is surrounded by unfamiliar faces and the life is very different from what he was used to at home, and he feels lost and homesick, and badly wants his mother. But he soon settles down; and when he gets used to the new conditions, he will feel comfortable and happy enough. Of course, there are draw backs, and even dangers in school life. There are always back sheep in every flock. A boy sometimes gets led away into bad habits by vicious companions. And when the discipline is over-strict and the masters unsympathetic, a sensitive boy may suffer much. But on the whole the influences of school life are healthy and good. For one thing, the strict discipline of a school has a healthy effect upon growing boys. At home a boy is too often coddled and allowed too much of his own way; but at school he has to obey. And there he learns the good old useful virtues of punctuality, regular method, the best use of time, diligence, and prompt obedience. Boys may not like these; but they are thankful for the lessons afterwards, when they become men. Then a boy cannot live in a community of boys without, as we say, getting the rough angles knocked off. Boys in their social life out of class and in the hostels discipline each other. A schoolboy soon gets such faults as conceit, priggishness, cowardice, meanness, and unsportsmanlike behavior, knocked out of him by the ridicule, criticism, and even rough treatment of his companions. It is often at school that life-friendship are made. Boys soon get to know one another, and form fast friendship, that in after life are a great joy and source of strength to them. The school games also develop a lad’s manly qualities, and not only make him physically strong, but also teach him “esprit-de-corps” and the true spirit of sportsmanship. So when school-days are over, a boy feels miserable and really regrets as he has to leave the school he joined. .. .Regression (Distance from School) Analyze the DataEnter your data into your calculator or computer Write the... distance and the cost? Are there any outliers? If so, which point is an outlier? 2/3 Regression (Distance from School) Should the outlier, if it exists, be removed? Why or why not? 3/3 ... following senarios: For a person who lives eight miles from campus, predict the total cost of supplies this term: For a person who lives eighty miles from campus, predict the total cost of supplies