Teaching Statistics in a Critical Way: Historical, Philosophical and Political Aspects of Statistics
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GAISE II Pre-K–12 Report
Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II) A Framework for Statistics and Data Science Education Writing Committee Anna Bargagliotti (co-chair) Sheri Johnson Loyola Marymount University University of Georgia* Christine Franklin (co-chair) Leticia Perez American Statistical Association University of California Los Angeles Center X Pip Arnold Denise A. Spangler Karekare Education New Zealand University of Georgia Rob Gould University of California Los Angeles The Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II) is an official position of the National Council of Teachers of Mathematics as approved by the NCTM Board of Directors, February 2020. Endorsed by the American Statistical Association, November 2020. *Current affiliation -The Mount Vernon School Library of Congress Cataloging-in-Publication Data Names: Bargagliotti, Anna, author. Title: Pre-K–12 guidelines for assessment and instruction in statistics education II (GAISE II) / writing committee, Anna Bargagliotti (co-chair), Loyola Marymount University, Christine Franklin (co-chair), American Statistical Association, Pip Arnold, Karekare Education New Zealand, Rob Gould, University of California Los Angeles, Sheri Johnson, University of Georgia, Leticia Perez, University of California Los Angeles, Denise A. Spangler, University of Georgia. Other titles: Guidelines for assessment and instruction in statistics education (GAISE) report Description: Second edition. | Alexandria, VA : American Statistical Association, 2020. | Includes bibliographical references. | Summary: “This document lays out a curriculum framework for Pre-K–12 educational programs that is designed to help students achieve data literacy and become statistically literate. The framework and subsequent sections in this book recommend curriculum and implementation strategies covering Pre-K–12 statistics education”-- Provided by publisher. -
Introduction to Social Statistics
SOCY 3400: INTRODUCTION TO SOCIAL STATISTICS MWF 11-12:00; Lab PGH 492 (sec. 13748) M 2-4 or (sec. 13749)W 2-4 Professor: Jarron M. Saint Onge, Ph.D. Office: PGH 489 Phone: (713) 743-3962 Email: [email protected] Office Hours: MW 10-11 (Please email) or by appointment Teaching Assistant: TA Email: Office Hours: TTh 10-12 or by appointment Required Text: McLendon, M. K. 2004. Statistical Analysis in the Social Sciences. Additional materials will be distributed through WebCT COURSE DESCRIPTION: Sociological research relies on experience with both qualitative (e.g. interviews, participant observation) and quantitative methods (e.g., statistical analyses) to investigate social phenomena. This class focuses on learning quantitative methods for furthering our knowledge about the world around us. This class will help students in the social sciences to gain a basic understanding of statistics, whether to understand, critique, or conduct social research. The course is divided into three main sections: (1) Descriptive Statistics; (2) Inferential Statistics; and (3) Applied Techniques. Descriptive statistics will allow you to summarize and describe data. Inferential Statistics will allow you to make estimates about a population (e.g., this entire class) based on a sample (e.g., 10 or 12 students in the class). The third section of the course will help you understand and interpret commonly used social science techniques that will help you to understand sociological research. In this class, you will learn concepts associated with social statistics. You will learn to understand and grasp the concepts, rather than only focusing on getting the correct answers. -
The Condition of Education 2005 (NCES 2005–094)
U.S. Department of Education Institute of Education Sciences NCES 2005–094 The Condition of Education 2005 U.S. Department of Education Institute of Education Sciences NCES 2005–094 June 2005 John Wirt Project Director National Center for Education Statistics Susan Choy MPR Associates, Inc. Patrick Rooney William Hussar National Center for Education Statistics Stephen Provasnik Gillian Hampden-Thompson Education Statistics Services Institute Production: Barbara Kridl Managing Editor Andrea Livingston Senior Editor MPR Associates, Inc. U.S. Department of Education Margaret Spellings Secretary Institute of Education Sciences Grover J. Whitehurst Director National Center for Education Statistics Grover J. Whitehurst Acting Commissioner The National Center for Education Statistics (NCES) is the primary federal entity for collecting, analyzing, and reporting data related to education in the United States and other nations. It fulfi lls a congressional mandate to collect, collate, analyze, and report full and complete statistics on the condition of education in the United States; conduct and publish reports and specialized analyses of the meaning and signifi cance of such statistics; assist state and local education agencies in improving their statistical systems; and review and report on education activities in foreign countries. NCES activities are designed to address high priority education data needs; provide consistent, reliable, complete, and accurate indicators of education status and trends; and report timely, useful, and high quality data to the U.S. Department of Education, the Congress, the states, other education policymakers, practitioners, data users, and the general public. We strive to make our products available in a variety of formats and in language that is appropriate to a variety of audiences. -
Statistics Education and the Role of Technology
Statistics Education and the Role of Technology Gail Burrill Mathematical Sciences Education Board, National Research Council, Washington, D.C. The world is changing dramatically. CD’s, the web, caller ID, grocery store debit machines, MRI’s: life today is very different from life thirty years ago. Computer assisted design, spreadsheet cost analysis, simulations, instant communication across cities and nations: the business world is very different from the business world thirty years ago. Information technology is transforming the way we do business and the way we live, and all indications are that even more change is imminent. In education just as in society and in the workforce, technology has the potential to make a profound impact. It can change the nature of the content we teach and the way we teach that content. In statistics, that potential is well on its way to becoming a reality. Although a Danish astronomer, Tyco Brahe, (1546-1601) was one of the first scientists to confront the problem of measurement error (Freedman et al, 1996), even in the early 1700's the nature of statistics was primarily qualitative probably due to the fact that there was very little quantitative data available (Ottaviani, 1989). Quantitative information became a reality in Hungary in 1784 with the first census; the first U.S. census was in 1790. This data was very difficult to manage, and by the late 1880's, a crisis had developed in the attempt to organize and process the large amounts of data collected through the U.S. census. While mathematicians and statisticians had continued over the years to develop statistics as a field, Herman Hollerith in response to this crisis produced a machine that would handle tabulations mechanically (Wallechinsky & Wallace, 1975). -
The Interpretation of Probability: Still an Open Issue? 1
philosophies Article The Interpretation of Probability: Still an Open Issue? 1 Maria Carla Galavotti Department of Philosophy and Communication, University of Bologna, Via Zamboni 38, 40126 Bologna, Italy; [email protected] Received: 19 July 2017; Accepted: 19 August 2017; Published: 29 August 2017 Abstract: Probability as understood today, namely as a quantitative notion expressible by means of a function ranging in the interval between 0–1, took shape in the mid-17th century, and presents both a mathematical and a philosophical aspect. Of these two sides, the second is by far the most controversial, and fuels a heated debate, still ongoing. After a short historical sketch of the birth and developments of probability, its major interpretations are outlined, by referring to the work of their most prominent representatives. The final section addresses the question of whether any of such interpretations can presently be considered predominant, which is answered in the negative. Keywords: probability; classical theory; frequentism; logicism; subjectivism; propensity 1. A Long Story Made Short Probability, taken as a quantitative notion whose value ranges in the interval between 0 and 1, emerged around the middle of the 17th century thanks to the work of two leading French mathematicians: Blaise Pascal and Pierre Fermat. According to a well-known anecdote: “a problem about games of chance proposed to an austere Jansenist by a man of the world was the origin of the calculus of probabilities”2. The ‘man of the world’ was the French gentleman Chevalier de Méré, a conspicuous figure at the court of Louis XIV, who asked Pascal—the ‘austere Jansenist’—the solution to some questions regarding gambling, such as how many dice tosses are needed to have a fair chance to obtain a double-six, or how the players should divide the stakes if a game is interrupted. -
Research Notes Research Notes
Research Notes Office of Research and PsychometricsDevelopment RN-23,RN-38, November February 20052009 Advanced Placement® Statistics Students’ Education Choices After High School Brian F. Patterson Introduction were more likely than non-AP examinees to take courses in the discipline of the exam. While Morgan and Maneckshana The College Board’s Advanced Placement® (AP®) Program did not analyze the relationship of AP Statistics participation was founded in 1955 as a national testing program with the and subsequent course taking, they did report a very robust goal of providing high school students opportunities to take finding across 25 exams and 21 colleges and universities. college-level courses. As of the 2009 administration, the AP One study that analyzed the more general relation- Program will consist of 37 courses in 20 different subject ship of high school mathematics and science course work areas. Despite the tremendous growth of the program, the with choice of a math or science major in college was College Board has not been able to systematically track what Trusty (2002). Trusty estimated separate models for men happens to the exam-taking population after they are exam- and women, given previous findings of significant gender ined, nor has it any information on students who take an AP interaction effects. He found that even after controlling course, but not the corresponding AP Exam. for racial/ethnic group, socioeconomic status, eighth-grade There is the expectation that students participating mathematics and science exam scores, and high school edu- in the Advanced Placement Program tend to be more likely cational attitudes and behaviors, course-taking patterns in than nonparticipants to choose postsecondary course work high school do have a significant relationship with choice of that is related to the discipline in which they are examined, a math or science major. -
THE HISTORY and DEVELOPMENT of STATISTICS in BELGIUM by Dr
THE HISTORY AND DEVELOPMENT OF STATISTICS IN BELGIUM By Dr. Armand Julin Director-General of the Belgian Labor Bureau, Member of the International Statistical Institute Chapter I. Historical Survey A vigorous interest in statistical researches has been both created and facilitated in Belgium by her restricted terri- tory, very dense population, prosperous agriculture, and the variety and vitality of her manufacturing interests. Nor need it surprise us that the successive governments of Bel- gium have given statistics a prominent place in their affairs. Baron de Reiffenberg, who published a bibliography of the ancient statistics of Belgium,* has given a long list of docu- ments relating to the population, agriculture, industry, commerce, transportation facilities, finance, army, etc. It was, however, chiefly the Austrian government which in- creased the number of such investigations and reports. The royal archives are filled to overflowing with documents from that period of our history and their very over-abun- dance forms even for the historian a most diflScult task.f With the French domination (1794-1814), the interest for statistics did not diminish. Lucien Bonaparte, Minister of the Interior from 1799-1800, organized in France the first Bureau of Statistics, while his successor, Chaptal, undertook to compile the statistics of the departments. As far as Belgium is concerned, there were published in Paris seven statistical memoirs prepared under the direction of the prefects. An eighth issue was not finished and a ninth one * Nouveaux mimoires de I'Acadimie royale des sciences et belles lettres de Bruxelles, t. VII. t The Archives of the kingdom and the catalogue of the van Hulthem library, preserved in the Biblioth^que Royale at Brussells, offer valuable information on this head. -
Precept 8: Some Review, Heteroskedasticity, and Causal Inference Soc 500: Applied Social Statistics
Precept 8: Some review, heteroskedasticity, and causal inference Soc 500: Applied Social Statistics Alex Kindel Princeton University November 15, 2018 Alex Kindel (Princeton) Precept 8 November 15, 2018 1 / 27 Learning Objectives 1 Review 1 Calculating error variance 2 Interaction terms (common support, main effects) 3 Model interpretation ("increase", "intuitively") 4 Heteroskedasticity 2 Causal inference with potential outcomes 0Thanks to Ian Lundberg and Xinyi Duan for material and ideas. Alex Kindel (Princeton) Precept 8 November 15, 2018 2 / 27 Calculating error variance We have some data: Y, X, Z. We think the correct model is Y = X + Z + u. We estimate this conditional expectation using OLS: Y = β0 + β1X + β2Z We want to know the standard error of β1. Standard error of β1 r ^2 ^ 1 σu 2 2 SE(βj ) = 2 Pn 2 , where Rj is the R of a regression of 1−Rj i=1(xij −x¯j ) variable j on all others. ^2 Question: What is σu? Alex Kindel (Princeton) Precept 8 November 15, 2018 3 / 27 Calculating error variance P 2 ^2 i u^i σu = DFresid You can adjust this in finite samples by u¯^ (why?) Alex Kindel (Princeton) Precept 8 November 15, 2018 4 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Assume X ∼ N (?; ?) and Z 2 f0; 1g Alex Kindel (Princeton) Precept 8 November 15, 2018 5 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Scenario 1 When Z = 0, X ∼ N (3; 4) When Z = 1, X ∼ N (−3; 2) Do you think an interaction term is justified here? Alex Kindel (Princeton) Precept 8 November 15, 2018 6 / 27 Interaction terms Y = β0 + β1X + β2Z + β3XZ Scenario -
A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research JRTE | Vol
A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research JRTE | Vol. 43, No. 3, pp. 253–278 | ©2011 ISTE | iste.org A Meta-Analysis Examining the Impact of Computer-Assisted Instruction on Postsecondary Statistics Education: 40 Years of Research Karen Larwin Youngstown State University David Larwin Kent State University at Salem Abstract The present meta-analysis is a comprehensive investigation of the effectiveness of computer-assisted instruction (CAI) on student achievement in postsec- ondary statistics education across a forty year period of time. The researchers calculated an overall effect size of 0.566 from 70 studies, for a total of 219 effect-size measures from a sample of n = 40,125 participants. These results suggest that the typical student moved from the 50th percentile to the 73rd percentile when technology was used as part of the curriculum. This study demonstrates that subcategories can further the understanding of how the use of CAI in statistics education might be maximized. The study discusses im- plications and limitations. (Keywords: statistics education, computer-assisted instruction, meta-analysis) iscovering how students learn most effectively is one of the major goals of research in education. During the last 30 years, many re- Dsearchers and educators have called for reform in the area of statistics education in an effort to more successfully reach the growing population of students, across an expansive variety of disciplines, who are required to complete coursework in statistics (e.g., Cobb, 1993, 2007; Garfield, 1993, 1995, 2002; Giraud, 1997; Hogg, 1991; Lindsay, Kettering, & Siegmund, 2004; Moore, 1997; Roiter, & Petocz, 1996; Snee, 1993;Yilmaz, 1996). -
Report on Exact and Statistical Matching Techniques
Statistical Policy Working Papers are a series of technical documents prepared under the auspices of the Office of Federal Statistical Policy and Standards. These documents are the product of working groups or task forces, as noted in the Preface to each report. These Statistical Policy Working Papers are published for the purpose of encouraging further discussion of the technical issues and to stimulate policy actions which flow from the technical findings and recommendations. Readers of Statistical Policy Working Papers are encouraged to communicate directly with the Office of Federal Statistical Policy and Standards with additional views, suggestions, or technical concerns. Office of Joseph W. Duncan Federal Statistical Director Policy Standards For sale by the Superintendent of Documents, U.S. Government Printing Office Washington, D.C. 20402 Statistical Policy Working Paper 5 Report on Exact and Statistical Matching Techniques Prepared by Subcommittee on Matching Techniques Federal Committee on Statistical Methodology DEPARTMENT OF COMMERCE UNITED STATES OF AMERICA U.S. DEPARTMENT OF COMMERCE Philip M. Klutznick Courtenay M. Slater, Chief Economist Office of Federal Statistical Policy and Standards Joseph W. Duncan, Director Issued: June 1980 Office of Federal Statistical Policy and Standards Joseph W. Duncan, Director Katherine K. Wallman, Deputy Director, Social Statistics Gaylord E. Worden, Deputy Director, Economic Statistics Maria E. Gonzalez, Chairperson, Federal Committee on Statistical Methodology Preface This working paper was prepared by the Subcommittee on Matching Techniques, Federal Committee on Statistical Methodology. The Subcommittee was chaired by Daniel B. Radner, Office of Research and Statistics, Social Security Administration, Department of Health and Human Services. Members of the Subcommittee include Rich Allen, Economics, Statistics, and Cooperatives Service (USDA); Thomas B. -
Committee on National Statistics
January 20, 2010 News from the Committee on National Statistics PEOPLE NEWS >We note with great sadness the untimely death on December 17, 2010, from complications of cancer, of Dr. Phyllis Kaniss, executive director of the American Academy of Political and Social Science (AAPSS) and a longtime teaching faculty member at the Annenberg School for Communication at the University of Pennsylvania (see http://www.asc.upenn.edu/News/NewsDetail.aspx?nid=816&ntype=faculty). She was the author of Making Local News (University of Chicago Press, 1991) and The Media and the Mayor’s Race: The Failure of Urban Political Reporting (Indiana University Press, 1995), which won the 1995 Bart Richards Award for media criticism. In 1999, she created the Student Voices Project, a youth civic engagement initiative of the Annenberg Public Policy Center that worked with school systems in cities throughout the country. Dr. Kaniss received a B.A. degree from the University of Pennsylvania and a Ph.D. in regional science from Cornell University. Her connection with CNSTAT is that she worked tirelessly and enthusiastically with our staff and members to organize the very successful joint CNSTAT-AAPSS Symposium on the Federal Statistical System—Recognizing Its Contributions; Moving It Forward that was held at the National Academies on May 8, 2009, and resulted in a special volume of the Annals of the AAPSS, edited by Ken Prewitt, ―The Federal Statistical System: Its Vulnerability Matters More than You Think‖ (http://www.sagepub.com/books/Book235999). We will miss Phyllis’s good cheer and extraordinary skills in furthering the use of social science to address important social problems. -
FDPS PRICE DOCUMENT RESUME AC 006 502 Continuing
DOCUMENT RESUME ED 036 722 AC 006 502 AUTHOR Royce, Marion TITLE Continuing Education for Women in Canada; Trends and Opportunities. Monographs in Adult Education, 4. INSTITUTION Ontario Inst. for Studies in Education, Toronto. Dept. of Adult Education. PUB DATF Sep 69 NCTE 174p. FDPS PRICE EDRS Price MF-$0.75 HC-$8.80 DESCRIPTORS Correspondence Study, Discussion Groups, Educational Television, English (Second Language), Immigrants, Manpower Development, Nursing, *Professional Continuing Education, Public Affairs Education, *Public School Adult Education, Teacher Education, *University Extension, Volunteers, *Womens Education IDENTIFIERS *Canada ABSTRACT This report describes a number of innovative continuing education programs for Canadianwomen under the auspices of universities, local educational authorities, and other organizations. It covers daytime (largely part time)classes at Mount St. Vincent University, the Thomas More Institute, and the Universities of British Columbia, Calgary, Guelph,and Manitoba; offerings by extension departments and continuingeducation centers at McGill, the University of Toronto, and the Universitiesof Alberta, British Columbia, Calgary, Guelphand Manitoba; and activities of the Adult Education Division of theCalgary School Board, including those in cooperation with the Universityof Calgary. It also deals with a public affairs educationprogram in Toronto, training of volunteers by and for the National Councilof Jewish women, discussion groups sponsored by the Young Women's Christian Association, career