Measuring Program Outcome

The Progress Evaluation Scales (PES) provide an efficient measuring devicefor evaluating current functioning, setting treatment goals, and assessing change over time in clinically relevant aspects of personal, social, and community adjustment. The PES can be completed by patients, significant others, and therapists, making it possible to obtain various points of view of the outcome of mental health services. This article describes the seven domains measured by the PES and the underlying dimensions they were designed to tap, and presents the generalizability, validity, and usefulness of the scales as applied to an adult mental health center population.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/67322/2/10.1177_0193841X8100500402.pd

An Overview of Normal Theory Structural Measurement Error Models

This paper gives an introduction and overview to the often under-used measurement error model. The purpose is to provide a simple summary of problems that arise from measurement error and of the solutions that have been proposed. We start by describing how measurement error models occur in real-world situations. Then we proceed with defining the measurement error model, initially introducing the multivariate form of the model, and then, starting with the simplest form of the model thoroughly discuss its features and solutions to the problems introduced due to measurement error. We discuss higher-dimensional and more advanced forms of the model and give a brief numerical illustration. Copyright 2007 The Authors. Journal compilation (c) 2007 International Statistical Institute.

Least squares estimators in measurement error models under the balanced loss function

Balanced loss function, direct and reverse regression, ineasurement errors, ultrastructural model, 62J05,

Improved regression calibration

The likelihood for generalized linear models with covariate measurement error cannot in general be expressed in closed form which makes maximum likelihood estimation taxing. A popular alternative is regression calibration which is computationally efficient at the cost of inconsistent estimation. We propose an improved regression calibration approach, a general pseudo maximum likelihood estimation method based on a conveniently decomposed form of the likelihood. It is both consistent and computationally efficient, and produces point estimates and estimated standard errors which are practically identical to those obtained by maximum likelihood. Simulations suggest that improved regression calibration which is easy to implement in standard software, works well in a range of situations