Value Added Modeling and Growth Modeling with Particular Application to Teacher and School Effectiveness
A volume in the series: The MARCES Book Series. Editor(s): Hong Jiao, University of Maryland. Robert W. Lissitz, University of Maryland.
Modeling student growth has been a federal policy requirement under No Child Left Behind (NCLB). In addition to tracking student growth, the latest Race To The Top (RTTP) federal education policy stipulates the evaluation of teacher effectiveness from the perspective of added value that teachers contribute to student learning and growth. Student growth modeling and teacher value-added modeling are complex. The complexity stems, in part, from issues due to non-random assignment of students into classes and schools, measurement error in students’ achievement scores that are utilized to evaluate the added value of teachers, multidimensionality of the measured construct across multiple grades, and the inclusion of covariates. National experts at the Twelfth Annual Maryland Assessment Research Center’s Conference on “Value Added Modeling and Growth Modeling with Particular Application to Teacher and School Effectiveness” present the latest developments and methods to tackle these issues. This book includes chapters based on these conference presentations. Further, the book provides some answers to questions such as what makes a good growth model? What criteria should be used in evaluating growth models? How should outputs from growth models be utilized? How auxiliary teacher information could be utilized to improve value added? How multiple sources of student information could be accumulated to estimate teacher effectiveness? Whether student-level and school-level covariates should be included? And what are the impacts of the potential heterogeneity of teacher effects across students of different aptitudes or other differing characteristics on growth modeling and teacher evaluation?
Overall, this book addresses reliability and validity issues in growth modeling and value added modeling and presents the latest development in this area. In addition, some persistent issues have been approached from a new perspective. This edited volume provides a very good source of information related to the current explorations in student growth and teacher effectiveness evaluation.
Preface, J. R. Lockwood and Daniel F. McCaffrey. Should Nonlinear Functions of Test Scores Be Used as Covariates in a Regression Model? J. R. Lockwood and Daniel F. McCaffrey. Value-Added to What? The Paradox of Multidimensionality, Derek C. Briggs and Ben Domingue. Accuracy, Transparency, and Incentives: Contrasting Criteria for Evaluating Growth Models, Andrew Ho. A Research-Based Response to Federal Non-Regulatory Guidance on Growth Models, Mark Ehlert, Cory Koedel, Eric Parsons, and Michael Podgursky. Borrowing the Strength of Unidimensional Scaling to Produce Multidimensional Educational Effectiveness Profiles, Joseph A. Martineau, and Ji Zeng. Value-Added Models and the Next Generation of Assessments, Robert H. Meyer and Emin Dokumaci. Using Auxiliary Teacher Data to Improve Value-Added: An Application of Small Area Estimation to Middle School Mathematics Teachers, Daniel F. McCaffrey, Bing Han, and J. R. Lockwood. The Evaluation of Teachers and Schools Using the Educator Response Function (ERF), Mark D. Reckase and Joseph A. Martineau. The Effective Use of Student and School Descriptive Indicators of Learning Progress: From the Conditional Growth Index to the Learning Productivity Measurement System, Y. M. Thum. Educational Value-Added Analysis of Covariance Models with Error in the Covariates, S. Paul Wright. Direct Modeling of Student Growth With Multilevel and Mixture Extensions, Hong Jiao and Robert Lissitz. Modeling Latent Growth Using Mixture Item Response Theory, Hong Jiao and Robert Lissitz. About the Authors.
Web price: $45.04 (Reg. 52.99)
Web price: $80.74 (Reg. 94.99)
- EDU000000 - EDUCATION: General
- EDU011000 - EDUCATION: Evaluation & Assessment
- EDU037000 - EDUCATION: Research
- Application of Artificial Intelligence to Assessment
- Data Analytics and Psychometrics Informing Assessment Practices
- Enhancing Effective Instruction and Learning Using Assessment Data
- Innovative Psychometric Modeling and Methods
- Technology Enhanced Innovative Assessment Development, Modeling, and Scoring From an Interdisciplinary Perspective
- Test Fairness in the New Generation of Large‐Scale Assessment
- The Next Generation of Testing Common Core Standards, Smarter‐Balanced, PARCC, and the Nationwide Testing Movement