Statistics, Department of

 

Date of this Version

8-2010

Comments

A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Statistics, Under the Supervision of Professor Erin E. Blankenship. Lincoln, Nebraska: August, 2010
Copyright 2010 Jennifer L. Green

Abstract

Value-added modeling is an alternative approach to test-based accountability systems based on the proportions of students scoring at or above pre-determined proficiency levels. Value-added modeling techniques provide opportunities to estimate an individual teacher’s effect on student learning, while allowing for the possibility to control for the effect of non-educational factors beyond a school system’s control, such as socioeconomic status. However, numerous considerations exist when using value-added models to estimate teacher effects and defining what the teacher effects really describe.

Chapter 2 provides an introduction to value-added methodology by describing several value-added models available for estimating teacher effects and their respective advantages and disadvantages. Modeling variations and their impact on estimated teacher effects are also discussed in addition to the various statistical and psychometric issues associated with estimating value-added teacher effects.

Because value-added analyses require high-quality longitudinal data that are often not available, Chapters 3 and 4 propose methodology for analyzing less-than-ideal assessment data. Chapter 3 proposes value-added methodology for analyzing longitudinal student achievement data not on a single developmental scale and addresses issues arising when using a layered, longitudinal mixed model to analyze gains in standardized scores. The chapter also discusses methods for estimating teacher effects on student learning before and after entering professional development programs and applies these methods of analysis to achievement data.

Chapter 4 describes the use of curve-of-factors methodology to analyze longitudinal achievement data collected from two differently scaled assessments in a single year and subject, such as mathematics. Assuming data come from a curve-of-factors model structure, a simulation study evaluates the performance of the proposed curve-of-factors model in its ability to accurately rank teachers in the presence of either complete or missing test data and compares it to the performance of the Z-score methodology proposed in Chapter 3.



Share

COinS