PPT-Examining Disproportionality and Quantitative Data

Examining Disproportionality and Quantitative Data New York University Metropolitan Center for Research on Equity and the Transformation of Schools Technical Assistance Center on Disproportionality ENTER DISTRICT NAME.  . If you have any questions, you can send them to the host using the Chat feature in the bottom right corner during the webinar.. . The webinar will start momentarily.. © 2013 Keenan & Associates. Your. Epistemology. Dr. Chase Young. Cunningham, J. W., & Fitzgerald, J. (1996). . Epistemology . and reading. . Reading . Research . Quarterly. , 31, 36-60. . e·pis·te·mol·o·gy. iˌpistəˈ. Conference,ChildWelfare:Disproportionality,Disparity,Discrimination:AssessingtheFacts,thePolicytookplaceJanuary2011,LawSchool,Cambridge,MA. 871(2009),SSRN http://www.law.harvard.edu/programs/about/cap Research in Education. Sohee Kang. Ph.D. , . l. ecturer . Math . and . Statistics . Learning . Centre. Outline. Analyzing Educational Research Data. Collecting data. Using R (R commander) for describing and testing hypotheses. Emilie O’Connor. oconnore@wisconsinpbisnetwork.org. Sara . Summ. summs@. wisconsinrticenter.org. Session Description. Creating a system that supports all students equitably is a priority for educators. In this session, participants will first explore how . New York University. Metropolitan Center for Research on Equity and the Transformation of Schools. Technical Assistance Center on Disproportionality. ENTER DISTRICT NAME. ENTER DATE. Internal and External Dialogue:. Part 1 . Pg. 43-53. When dealing with a large data set, it is best to:. summarize. . make . a picture. *. note - we do not use bar graphs or circle graphs for quantitative data. Histograms. The chapter example discusses earthquake magnitudes.. by Alyssa Webb. Data can come in many different types but it useless without it’s context.. Not all data represented by numbers is numerical. (ex: 1=boy, 2=girl). Who, What, When, Where, Why, and How? provides context for data.. Kim Unger – Fall 2017. About me. Senior Analytics Consultant, DataBrains. Former SSEF, ISEF, STS Finalist. Science Fair Judge (regional, SSEF, ISEF). Data Visualization is my day-to-day job. kunger@databrains.com. Histograms. Bins. – equal width “piles” that we use to divide up quantitative data. The bins and the counts in each bin give the . distribution. of the quantitative variable. Enron Corporation . Texas Disproportionality Summit. Kent McIntosh. University of Oregon. Handouts:. http://www.pbis.org. Information from this session comes from:. McIntosh, K., Girvan, E. J., Horner, R. H., & Smolkowski, K. . | Disproportionality . Jennifer . Strus. , Assistant Secretary. Update: 6/2015. Disproportionality| Children’s Administration. Background . (complete the following, note N/A if not applicable). DSHS Goal 3: Protection - Each individual who is vulnerable will be protected . : Supporting . State . Implementation. Moderator: Tom . Munk. Panelists: . Julie . Bollmer. Cesar D’Agord. Lisa Haley. Vicki . Niskioka. 1. Agenda. 2. History . of . Significant Disproportionality . Quantitative or Qualitative?. John’s reaction time with 10 hours’ sleep averaged0.21 seconds; with 6 hours’ sleep it was 0.28 seconds.. Jenny gave a detailed account of her relationship with each of her children or.