PPT-Using Prior Scores to Evaluate Bias in Value-Added Models

Raj Chetty Stanford University and NBER John N Friedman Brown University and NBER Jonah Rockoff Columbia University and NBER January 2016 Outcomebased value added VA models increasingly used to measure the productivity of many agents. Teacher Value-Added and Students’ Outcomes in Adulthood . Raj . Chetty. , Harvard. John N. Friedman, Harvard. Jonah . Rockoff. , Columbia. March 2014. . How can we measure and improve the quality of teaching in elementary schools?. Keeping It Simple. Polk County Schools – November 2015. The Statute: 1012.34. (3)(a)1. . Performance of students.—At least . one-third. of a performance evaluation must be based upon data and indicators of . Focus Vol. 29, No. 2, Fall/Winter 2012–13 Next, the researchers assessed whether teachers who raise test scores also improve their students’ outcomes in adulthood. They analyze the effects o Administrative Stuff:. Anna’s Office Hours. Tuesday after class: in the Co-lab. Friday 10-11am: . rm. 107. Making Sense of Overwhelming Data. “. Today companies like Google, which have grown up in an era of massively abundant data, don't have to settle for wrong models. Indeed, they don't have to settle for models at all. . Simon Kirby*, David Li* and Christy Chuang-Stein^,. PSI Conference 2017,. Grange Tower Bridge Hotel,. London. *Pfizer, ^Chuang-Stein Consulting. Layout. The problem. Some examples. Some possible bias adjustment methods. Xuhua Xia. xxia@uottawa.ca. http://. dambe.bio.uottawa.ca. Xuhua Xia. Transition bias refers to the degree by which the s/v ratio deviates from the expected 1/2. The observed s/v ratio is almost always much larger than 1/2.. CAS Ratemaking and Product Management. Spring 2012 – March 20. 1. 2. What are Credit-based Insurance Scores?. . A numeric representation of relative insurance claim risk based on consumer credit details. ©2015 AAMC.  May be reproduced and distributed with attribution and without alteration by and within AAMC member organizations only.. Learning objectives. By the end of this session, you will be able to:. Human and Machine Learning. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Flipping A Biased Coin. Suppose you have a coin with an unknown bias, . Human and Machine Learning. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Flipping A Biased Coin. Suppose you have a coin with an unknown bias, . Intelligence . and IQ . Testing. Controversy and Consensus. . Learning Objectives. LO 9.1. Identify different models and types of intelligence.. LO 9.2. Describe the connection between intelligence and brain size and efficiency.. . Henrik Singmann. David Kellen. Eda . Mızrak. Ilke. . Ö. ztekin. CogSci. : Theory, Data, and Models. Goals:. Develop accurate characterizations of observed behavior in terms of latent cognitive processes. . BiasIn this exercise participants are instructed to write down in a column on the lefthand side of a blank piece of paper the initials of six to ten people whom they trust the most who are not family A Look Across Maryland Hospitals. . . Diane . Alejo. ,. . Jennifer Bobbitt, . Filiz. . Costantini. , Diane Sender, Mary Brogan, Karen . Getson. , Angela Toro, Heather Romine,. Gail Hanna, Martha .