PPT-MATCHING TECHNIQUES

Technical Track Session VI This material constitutes supporting material for the Impact Evaluation in Practice book This additional material is made freely but please acknowledge its use as follows . Individual Matching Controls are matched to cases on one or more attributes (i.e. age, gender, smoking status, etc). Each case/control pair then has identical values on the matching factors. Requi in Non-Bipartite Graphs. Alicia . Thilani. Singham Goodwin. 18.304 • 3/22/2013. Our Goal. Make. an . algorithm. to . find. the . largest. . cardinality. . matching. (. most. sets of . partners. Rochelle McCrea. Team Leader. and. Towana DeShazo. Grants and Agreements Specialist. Awards Management Branch. Office of Extramural Programs. Topics Covered. Matching/Cost Sharing Fact Sheet. Requirements. Jessica Comfort. Business . Systems . Analyst. Goals for today’s presentation. Review Banner forms used in the set up of a tape load. Review Banner forms used in the matching process. Match on Null . Based on work by: . Francisco Campos, Aidan . Coville. , Ana . Fernandes. , Markus Goldstein and David McKenzie. Are matching grants an effective tool to crowd in business investment and spur business growth?. Sam . Marden. s.h.marden@lse.ac.uk. Introduction. Describe the intuition behind . matching estimators. . Be concise. .. Suppose you have a sample of 100,000 prospective voters, with data on age, gender, party affiliation, county of residence, and whether or not an individual voted in the last elections. Ten thousand of these individuals were reached by telephone and heard a short message from a non-partisan agency regarding the importance of voting. The aim of the message was to improve voter turn out. Explain in no more than three sentences how one would use a matching estimator to estimate the effect of the calls. Note, you do not need to provide technical details (that comes next week), but a clear and intuitive explanation of how you would construct the matching estimator.. A Practical Demonstration Looking at Results from the Promise Pathways Initiative at Long Beach City College. Andrew Fuenmayor, Research Analyst. John Hetts, . Director of Institutional Research. Long Beach City College . NRS & RSS Edinburgh. , . October. 2012. AGENDA. . Context: 2011 Census quality assurance and the role of administrative data. Data matching challenges and solutions. Data to be matched. Matching methods and interpretation . Philip A. Bernstein Microsoft Corp.. Jayant . Madhavan. Google. Erhard Rahm Univ. of Leipzig. Copyright © 2011 Microsoft Corp.. The . problem of generating . correspondences between . Stratification,. Regression. Heejung Bang, PhD. UC-Davis. 1. Rimm. & . Bortin. (1978). Clinical trials as religion. 2. Motivating episode. A surgeon came to me – his aim/hypothesis is clear. Excel dataset has small N & few variables.. MOTIVES OF IMPERIALISM. (E.M.P.I.R.E). Image Matching Activity. For the following images, decide which imperialism “motivation” it represents (ECONOMY, MILITARY, POLITICS, IDEOLOGIES, RELIGION, or EXPLORATORY). Acceleration Data . Pramod. . Vemulapalli. . Outline . 50 % Tutorial and 50 % Research Results . Basics . Literature Survey . Acceleration Data . Preliminary Results . Conclusions . What is A Time-Series Subsequence ?. Module . 9. Experimental . psychology . guided-inquiry learning. Module 9: Matching/Matched Pairs Design. ©2012, . Dr. A. Geliebter & Dr. B. Rumain, Touro College & University System. Let’s now get back to our depression study. Suppose we have 3 treatment groups with each group receiving a different dose of Elate. Let’s say the doses are 750mg, 1200mg, and 0 mg. And, suppose also we know that our subjects are not roughly equal in their level of depression; some are more severely depressed while others are only mildly or moderately depressed. . Network to Compare Image Patches. Jure . Zbontar. , Yann . LeCun. Background. Motivation. Problem Formulation. Methodology. Training Data. Suggested Net Architectures. Sequential Steps. Results. Conclusion.