The Policy Choices of Effective Principals David Figlio, Northwestern U/NBER Tim Sass, Florida State U July 2010.

Slides:



Advertisements
Similar presentations
Evaluating and Institutionalizing
Advertisements

Project VIABLE: Behavioral Specificity and Wording Impact on DBR Accuracy Teresa J. LeBel 1, Amy M. Briesch 1, Stephen P. Kilgus 1, T. Chris Riley-Tillman.
Dynamic panels and unit roots
Human Capital Policies in Education: Further Research on Teachers and Principals 5 rd Annual CALDER Conference January 27 th, 2012.
Teacher Training, Teacher Quality and Student Achievement Douglas Harris Tim R. Sass Dept. of Educational Dept. of Economics Policy Studies Florida State.
STUDENT GROWTH PART 3 10/16/14 ASSESSMENTS. REVIEW 1.Who are we going to target? 9 th grade 2.How much do they need to grow? Based on MAPS testing 1.5.
Brief introduction on Logistic Regression
Forecasting OPS 370.
School autonomy and student achievement. An international study with a focus on Italy Angelo Paletta Maria Magdalena Isac Daniele Vidoni.
A Guide to Education Research in the Era of NCLB Brian Jacob University of Michigan December 5, 2007.
Teacher Effectiveness in Urban Schools Richard Buddin & Gema Zamarro IES Research Conference, June 2010.
How College Makes a Difference: A Summary By E.T. Pascarella and P.T. Terenzini (1991). From How College Affects Students: Findings and Insights from Twenty.
Irwin/McGraw-Hill © Andrew F. Siegel, 1997 and l Chapter 12 l Multiple Regression: Predicting One Factor from Several Others.
Educator Evaluations Education Accountability Summit August 26-28,
Fall 2006 – Fundamentals of Business Statistics 1 Chapter 13 Introduction to Linear Regression and Correlation Analysis.
Using Growth Models for Accountability Pete Goldschmidt, Ph.D. Assistant Professor California State University Northridge Senior Researcher National Center.
Chapter 28 Design of Experiments (DOE). Objectives Define basic design of experiments (DOE) terminology. Apply DOE principles. Plan, organize, and evaluate.
Linear Regression and Correlation Analysis
Chapter 13 Introduction to Linear Regression and Correlation Analysis
Autocorrelation Lecture 18 Lecture 18.
What Makes For a Good Teacher and Who Can Tell? Douglas N. Harris Tim R. Sass Dept. of Ed. Policy Studies Dept. of Economics Univ. of Wisconsin Florida.
Special Data Opportunities in Florida David N. Figlio University of Florida and National Bureau of Economic Research.
Production Functions and Measuring the Effect of Teachers on Student Achievement With Value-Added HSE March 20, 2012.
Principal Performance Evaluation System
March, What does the new law require?  20% State student growth data (increases to 25% upon implementation of value0added growth model)  20%
-- Preliminary, Do Not Quote Without Permission -- VALUE-ADDED MODELS AND THE MEASUREMENT OF TEACHER QUALITY Douglas HarrisTim R. Sass Dept. of Ed. LeadershipDept.
Time Series “The Art of Forecasting”. What Is Forecasting? Process of predicting a future event Underlying basis of all business decisions –Production.
March 28, What does the new law require?  20% State student growth data (increases to 25% upon implementation of value0added growth model)  20%
1 Comments on: “New Research on Training, Growing and Evaluating Teachers” 6 th Annual CALDER Conference February 21, 2013.
S519: Evaluation of Information Systems Week 14: April 7, 2008.
Human Capital Policies in Education: Further Research on Teachers and Principals 5 rd Annual CALDER Conference January 27 th, 2012.
The Determinants of Student Achievement: Different Estimates for Different Measures Tim Sass Department of Economics Florida State University CALDER Conference.
BECTa ICT Research Conference – June 2002 Intro  Survey Details  Secondary Surveys conducted July 2000 and June/July 2001  Sponsored by Fischer Family.
Sensitivity of Teacher Value-Added Estimates to Student and Peer Control Variables October 2013 Matthew Johnson Stephen Lipscomb Brian Gill.
School Accountability and the Distribution of Student Achievement Randall Reback Barnard College Economics Department and Teachers College, Columbia University.
The Inter-temporal Stability of Teacher Effect Estimates J. R. Lockwood Daniel F. McCaffrey Tim R. Sass The RAND Corporation The RAND Corporation Florida.
Issues in Assessment Design, Vertical Alignment, and Data Management : Working with Growth Models Pete Goldschmidt UCLA Graduate School of Education &
Julian Betts, Department of Economics, UCSD and NBER.
Research on teacher pay-for-performance Patrick McEwan Wellesley College (Also see Victor Lavy, “Using performance-based pay to improve.
Operations Fall 2015 Bruce Duggan Providence University College.
CHAPTER 9: Producing Data: Experiments. Chapter 9 Concepts 2  Observation vs. Experiment  Subjects, Factors, Treatments  How to Experiment Badly 
“A Truthful Evaluation Of Yourself Gives Feedback For Growth and Success” Brenda Johnson Padgett Brenda Johnson Padgett.
Lesson Overview Lesson Overview What Is Science? Lesson Overview 1.1 What Is Science?
CHAPTER 9: Producing Data Experiments ESSENTIAL STATISTICS Second Edition David S. Moore, William I. Notz, and Michael A. Fligner Lecture Presentation.
Public Finance Seminar Spring 2015, Professor Yinger Public Production Functions.
Impediments to the estimation of teacher value added Steven Rivkin Jun Ishii April 2008.
Lesson Overview Lesson Overview What Is Science? Lesson Overview 1.1 What Is Science?
School-level Correlates of Achievement: Linking NAEP, State Assessments, and SASS NAEP State Analysis Project Sami Kitmitto CCSSO National Conference on.
Changes in Professional licensure Teacher evaluation system Training at Coastal Carolina University.
Release of Preliminary Value-Added Data Webinar August 13, 2012 Florida Department of Education.
Financial Exclusion Topic Report: a presentation to the Scottish Household Survey User Day Keith Hayton GEN 26 th November 2007.
CHAPTER 9: Producing Data Experiments ESSENTIAL STATISTICS Second Edition David S. Moore, William I. Notz, and Michael A. Fligner Lecture Presentation.
CURRICULUM Simply put: “What is taught to students.”
Internal Evaluation of MMP Cindy M. Walker Jacqueline Gosz Razia Azen University of Wisconsin Milwaukee.
CHAPTER 9: Producing Data Experiments ESSENTIAL STATISTICS Second Edition David S. Moore, William I. Notz, and Michael A. Fligner Lecture Presentation.
Lecture 7: Bivariate Statistics. 2 Properties of Standard Deviation Variance is just the square of the S.D. If a constant is added to all scores, it has.
Assessing the Impact of Informality on Wages in Tanzania: Is There a Penalty for Women? Pablo Suárez Robles (University Paris-Est Créteil) 1.
Florida Algebra I EOC Value-Added Model June 2013.
Forecasting. Model with indicator variables The choice of a forecasting technique depends on the components identified in the time series. The techniques.
The Question of Causation 4.2:Establishing Causation AP Statistics.
TCAI: Lessons from first Endline TCAI Development Partners Feb 27, 2013.
School Quality and the Black-White Achievement Gap
“The Art of Forecasting”
Java Programming Loops
Techniques for Data Analysis Event Study
The Question of Causation
Ch. 13. Pooled Cross Sections Across Time: Simple Panel Data.
Why We Should be Skeptical about the Common Core
Ch. 13. Pooled Cross Sections Across Time: Simple Panel Data.
Reminder for next week CUELT Conference.
Presentation transcript:

The Policy Choices of Effective Principals David Figlio, Northwestern U/NBER Tim Sass, Florida State U July 2010

Introduction  Considerable research energy on measuring teacher (and now, principal) value added  Increasing evidence of large variation in principal effectiveness; unsurprisingly, easily measured characteristics aren’t particularly explanatory  Very little knowledge of what effective principals actually do, and whether these actions might be transferable

This Project  Merge measures of principal value added with rich survey data on instructional policies and practices in Florida  Survey data collected on three occasions for 80% of all Florida public schools; today’s first pass: and surveys of elementary school principals  Panel nature of survey allows us to observe policy changes that come about with principal changes at a school

Research Questions  Do school policies change when the identity of the principal changes? What are the policy choices of effective principals?  Do high value added principals “bring with them” different policy/practice choices from their prior schools?  Are the policy/practice choices of new principals in a school different for principals with high value added?

Defining Principal Value Added  Estimate “value-added” model of student achievement, including principal-by-year fixed effects Time period = 2000/ /05 school years Estimate four models which vary by:  Extent of persistence in prior inputs  Complete persistence (dependent variable is achievement gain) vs. partial persistence (dependent variable is current achievement level, lagged score as an explanatory variable)  Inclusion vs. exclusion of controls for teacher characteristics  If exclude teacher characteristics, principal value-added measure includes average teacher quality

Correlation of Principal Value-Added Measures Teacher Characteristics, Partial Persistence Teacher Characteristics, Complete Persistence No Teacher Characteristics, Partial Persistence No Teacher Characteristics, Complete Persistence Teacher Characteristics, Partial Persistence Teacher Characteristics, Complete Persistence No Teacher Characteristics, Partial Persistence No Teacher Characteristics, Complete Persistence

School Policies and Practices  Survey asked 65 questions about instructional policies and practices  Because of a school budget constraint, and the fact that these are often variations of a theme, we combine these questions into domains  Domains are weighted averages of individual policy responses, weighted by the variation in the question response

Policy and Practice Domains  Policies to improve low-performing students  Lengthening instructional time  Reduced class size for specific subjects  Narrowing of curriculum  Systems of scheduling and class organization  Policies to improve low-performing teachers  Teacher resources  Teacher incentives  Teacher autonomy

Example: Scheduling Systems  Component questions: Block scheduling Common prep periods Subject matter specialist teachers Organizing teachers into teams Looping Multi-age structure Other scheduling systems

Example: Policies to Improve Low- Performing Students  Component questions: Require grade retention Require summer school Require before/after school tutoring Require in-school supplemental instruction Require tutoring Require Saturday school Require other policy

Do New Principals Affect School Policy?  For each of nine policy domains (Z), regress absolute value of difference in policy in 2003/04 and 2001/02 at school k on a constant and an indicator for change in identity of principal (P):

Estimated Effect of Principal Change on Normalized Absolute Value of Policy Change

A Simple Model of School Policy  Assume current policy in school k with principal j is related to policy at school k in prior period, whether a change in school leadership has occurred (P), and policy preferences of the new principal (P x C):

Do Principals Bring their old Policies to New Schools?  Dependent variable: school policy domain in 2003/04  Right hand size variables: School policy domain in 2001/02 New principal since 2001/02 Interaction between new principal and new principal’s 2001/02 policy domain in their prior school

Do Principals Bring their Old Policies to New Schools?

Does New Principal Value Added Relate to School Policy Adoption?  Dependent variable: school policy domain in 2003/04  Right hand size variables: School policy domain in 2001/02 New principal since 2001/02 Interaction between new principal and new principal’s 2001/02 value added  Graph shows value-added from model with partial persistence and controls for teacher characteristics

Does New Principal Value Added Relate to School Policy Adoption?

Summary  Too early to draw any firm conclusions  Early evidence suggests that: New principals do have an impact on school policies even in the short run (2 years or less) In most cases principals do not simply “port” their policies from one school to another High value added principals implement different new policies when they arrive at a new school  Tend to favor policies directed toward improving low- performing students and low-performing teachers