Results 1 - 10
of
25
The Impact of Family Income on Child Achievement: Evidence from the Earned Income Tax Credit
, 2009
"... ..."
(Show Context)
Assumptions of Value‐Added Models for Estimating School Effects
, 2008
"... Designs, and Analytic Methods. ” We thank Derek Neal for his thoughtful discussion of earlier stages of this work. All errors remain our own. 1 ..."
Abstract
-
Cited by 20 (0 self)
- Add to MetaCart
(Show Context)
Designs, and Analytic Methods. ” We thank Derek Neal for his thoughtful discussion of earlier stages of this work. All errors remain our own. 1
Measurement Matters: Perspectives on Education Policy from an Economist and
- School Board Member,Journal of Economic Perspectives, 24:3 (Summer 2010
"... ..."
(Show Context)
Exploring Student-Teacher Interactions in Longitudinal Achievement Data
, 2008
"... This article develops a model for longitudinal student achievement data designed to estimate heterogeneity in teacher effects across students of different achivement levels. The model specifies interactions between teacher effects and students’ predicted scores on a test, estimating both average eff ..."
Abstract
-
Cited by 8 (0 self)
- Add to MetaCart
This article develops a model for longitudinal student achievement data designed to estimate heterogeneity in teacher effects across students of different achivement levels. The model specifies interactions between teacher effects and students’ predicted scores on a test, estimating both average effects of individual teachers and interaction terms indicating whether individual teachers are differentially effective with students of different predicted scores. Using various longitudinal data sources, we find evidence of these interactions that are of relatively consistent but modest magnitude across different contexts, accounting for about 10 % of the total variation in teacher effects across all students. However, the amount that the interactions matter in practice depends on how different are the groups of students taught by different teachers. Using empirical estimates of the heterogeneity of students across teachers, we find that the interactions account for about 3%-4 % of total variation in teacher effects on different classes, with somewhat larger values in middle school mathematics. Our findings suggest that ignoring these interactions is not likely to introduce appreciable bias in estimated teacher effects for most teachers in most settings. The results of this study should be of interest to policymakers concerned about the validity of VAM teacher effect estimates.
The Sensitivity of Value-Added Modeling to the Creation of a Vertical Score Scale
, 2008
"... The research described in this paper was supported by a grant from the Carnegie Corporation. The purpose of this study was to evaluate the sensitivity of value-added modeling to the way an underlying vertical score scale has been created. Longitudinal item-level data was analyzed with both student a ..."
Abstract
-
Cited by 7 (1 self)
- Add to MetaCart
The research described in this paper was supported by a grant from the Carnegie Corporation. The purpose of this study was to evaluate the sensitivity of value-added modeling to the way an underlying vertical score scale has been created. Longitudinal item-level data was analyzed with both student and school-level identifiers for the entire state of Colorado between 2003 and 2006. Eight different vertical scales were established on the basis of choices made for three key variables: Item Response Theory modeling approach, calibration approach and student proficiency estimation approach. Each scale represented a methodological approach that was psychometrically defensible. Longitudinal values from each scale were used as the outcome in a commonly used value-added model (the layered model popularized by William Sanders) as a means of estimating school effects. Our findings suggest that while the ordering of estimating school effects is insensitive to the underlying vertical scale, the precision of such value-added estimates can be quite sensitive to the combinations of choices made in the creation of the scale. 2
GPvam: Maximum Likelihood Estimation of Multiple Membership Mixed Models Used in Value-Added Modeling, http://cran.r-project.org/web/packages/GPvam/index.html, R package version
, 2012
"... Abstract. The generalized persistence (GP) model, developed in the context of estimating “value added ” by individual teachers to their students ’ current and future test scores, is one of the most flexible value-added models in the literature. Although developed in the educational setting, the GP m ..."
Abstract
-
Cited by 4 (4 self)
- Add to MetaCart
(Show Context)
Abstract. The generalized persistence (GP) model, developed in the context of estimating “value added ” by individual teachers to their students ’ current and future test scores, is one of the most flexible value-added models in the literature. Although developed in the educational setting, the GP model can potentially be applied to any structure where each sequential response of a lower-level unit may be associated with a different higher-level unit, and the effects of the higher-level units may persist over time. The flexibility of the GP model, however, and its multiple membership random effects structure lead to computational challenges that have limited the model’s availability. We de-velop an EM algorithm to compute maximum likelihood estimates efficiently for the GP model, making use of the sparse structure of the random effects and error covariance matrices. The algorithm is implemented in the pack-age GPvam in R statistical software. We give examples of the computations and illustrate the gains in computational efficiency achieved by our estimation procedure. NOTICE This is the author’s version of a work that was accepted for publication in Compu-tational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, [VOL59, March, (2013)] DOI:10.1016/j.csda.2012.10.004 1.
A Response to Amrein-Beardsley (2008) “Methodological Concerns About the Education Value-Added Assessment System. A SAS White paper
, 2008
"... The Amrein-Beardsley paper attempts to give a summary of various issues associated with value-added assessment models both in general and with special focus on the methodology used in the Tennessee Value-Added Assessment System (TVAAS). SAS Institute Inc. now provides comparable analytical services ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
(Show Context)
The Amrein-Beardsley paper attempts to give a summary of various issues associated with value-added assessment models both in general and with special focus on the methodology used in the Tennessee Value-Added Assessment System (TVAAS). SAS Institute Inc. now provides comparable analytical services to various schools,
2012a), “A Correlated Random-Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects
"... Value-added models have been widely used to assess the contributions of indi-vidual teachers and schools to students ’ academic growth based on longitudinal student achievement outcomes. There is concern, however, that ignoring the presence of missing values, which are common in longitudinal studies ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
(Show Context)
Value-added models have been widely used to assess the contributions of indi-vidual teachers and schools to students ’ academic growth based on longitudinal student achievement outcomes. There is concern, however, that ignoring the presence of missing values, which are common in longitudinal studies, can bias teachers ’ value-added scores. In this article, a flexible correlated random effects model is developed that jointly models the student responses and the student missing data indicators. Both the student responses and the missing data mechanism depend on latent teacher effects as well as latent student effects, and the correlation between the sets of random effects adjusts teachers ’ value-added scores for informative missing data. The methods are illustrated with data from calculus classes at a large public university and with data from an elementary school district.
Controlling for student heterogeneity in longitudinal achievement models
, 2007
"... Education working paper series. RAND working papers are intended to share researchers ’ latest findings and to solicit informal peer review. They have been approved for circulation by RAND Education but have not been formally edited or peer reviewed. Unless otherwise indicated, working papers can be ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
Education working paper series. RAND working papers are intended to share researchers ’ latest findings and to solicit informal peer review. They have been approved for circulation by RAND Education but have not been formally edited or peer reviewed. Unless otherwise indicated, working papers can be quoted and cited without permission of the author, provided the source is clearly referred to as a working paper. RAND’s publications do not necessarily reflect the opinions of its research clients and sponsors. is a registered trademark.
Profile-Likelihood Approach for Estimating Generalized Linear Mixed Models With Factor Structures
"... In this article, the authors suggest a profile-likelihood approach for estimating complex models by maximum likelihood (ML) using standard software and minimal programming. The method works whenever setting some of the parameters of the model to known constants turns the model into a standard model ..."
Abstract
- Add to MetaCart
(Show Context)
In this article, the authors suggest a profile-likelihood approach for estimating complex models by maximum likelihood (ML) using standard software and minimal programming. The method works whenever setting some of the parameters of the model to known constants turns the model into a standard model. An important class of models that can be estimated this way is generalized linear mixed models with factor structures. Such models are useful in educational research, for example, for estimation of value-added teacher or school effects with persistence parameters and for analysis of large-scale assessment data using multilevel item response models with discrimination parameters. The authors describe the profile-likelihood approach, implement it in the R software, and apply the method to longitudinal data and binary item response data. Simulation studies and comparison with gllamm show that the profile-likelihood method performs well in both types of applications. The authors also briefly discuss other types of models that can be estimated using the profile-likelihood idea.
