Vertical Equating for State Assessments: Issues and Solutions in Determination of Adequate Yearly Progress and School Accountability

This article describes AYP and some of the psychometric issues it raises. It examines scaling as a means to equate tests as part of a process to confirm educational gains. Vertically moderated standards are recommended over vertical equating of state assessments to measure annual progress and provide useful instructional information. Accessed 34,106 times on https://pareonline.net from April 30, 2003 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Evaluating Teachers and Schools Using Student Growth Models

Interest in Student Growth Modeling (SGM) and Value Added Modeling (VAM) arises from educators concerned with measuring the effectiveness of teaching and other school activities through changes in student performance as a companion and perhaps even an alternative to status. Several formal statistical models have been proposed for year-to-year growth and these fall into at least three clusters: simple change (e.g., differences on a vertical scale), residualized change (e.g., simple linear or quantile regression techniques), and value tables (varying salience of different achievement level outcomes across two years). Several of these methods have been implemented by states and districts. This paper reviews relevant literature and reports results of a data-based comparison of six basic SGM models that may permit aggregating across teachers or schools to provide evaluative information. Our investigation raises some issues that may compromise current efforts to implement VAM in teacher and school evaluations and makes suggestions for both practice and research based on the results. Accessed 9,761 times on https://pareonline.net from December 17, 2012 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

A solution to the weighted procrustes problem in which the transformation is in agreement with the loss function

This paper provides a generalization of the Procrustes problem in which the errors are weighted from the right, or the left, or both. The solution is achieved by having the orthogonality constraint on the transformation be in agreement with the norm of the least squares criterion. This general principle is discussed and illustrated by the mathematics of the weighted orthogonal Procrustes problem.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45737/1/11336_2005_Article_BF02296976.pd

Exploring the Full-Information Bifactor Model in Vertical ScalingWith Construct Shift

En: Applied Psychological Measurement, 36(1), pp. 3–20To address the lack of attention to construct shift in item response theory (IRT) vertical scaling, a multigroup, bifactor model was proposed to model the common dimension for all grades and the grade-specific dimensions. Bifactor model estimation accuracy was evaluated through a simulation study with manipulated factors of percentage of common items, sample size, and degree of construct shift. In addition, the unidimensional IRT (UIRT) model, which ignores construct shift, was also estimated to represent current practice. It was found that (a) bifactor models were well recovered overall, though the grade-specific dimensions were not as well recovered as the general dimension; (b) item discrimination parameter estimates were overestimated in UIRT models due to the effect of construct shift; (c) the person parameters of UIRT models were less accurately estimated than those of bifactor models; (d) group mean parameter estimates from UIRT models were less accurate than those of bifactor models; and (e) a large effect due to construct shift was found for the group mean parameter estimates of UIRT models. A real data analysis provided an illustration of how bifactor models can be applied to problems involving vertical scaling with construct shift. General procedures for testing practice were recommended and discussed

The approximation of a group stimulus space by averaging responses to selected subsets of the stimuli

Multi-dimensional scaling, average space, paired comparisons,