Conventional advice discourages controlling for postoutcome variables in regression analysis. By contrast, we show that controlling for commonly available postoutcome (i.e., future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved…

When cut scores for classifications occur on the total score scale, popular methods for estimating classification accuracy (CA) and classification consistency (CC) require assumptions about a parametric form of the test scores or about a parametric response model, such as item response theory (IRT). This article develops an approach to estimate CA…

A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…

The OECD Program for International Student Assessment (PISA) and the OECD Teaching and Learning International Survey (TALIS) constitute two of the largest ongoing international student and teacher surveys presently underway. Data generated from these surveys offer researchers and policy-makers opportunities to identify particular educational…

This study compares the performance of three nonparametric item characteristic curve (ICC) estimation procedures: isotonic regression, smoothed isotonic regression, and kernel smoothing. Smoothed isotonic regression, employed along with an appropriate kernel function, provides better estimates and also satisfies the assumption of strict…

Examined the Type I error rate and the power of the Stout T procedure (DIMTEST) (W. Stout, 19987, 1990) and the Holland-Rosenbaum procedure (P. Holland and P. Rosenbaum, 1986) for normal and nonnormal data sets through a Monte Carlo study. Both procedures performed adequately under some conditions, but the Stout T procedure showed adequate power…

The primary purpose of this study was to compare the performance of three procedures to assess dimensionality that were investigated by R. Nandakumar (1994) at different test conditions to reflect the characteristics of language test data. Procedures investigated were nonlinear factor analysis, the procedure of P. Holland and P. Rosenbaum, and W.…

The Type I error and power properties of the parametric F test and three nonparametric competitors were compared in terms of 3 x 4 factorial analysis of covariance layout. The focus of the study was on the test for interaction either in the presence or absence of main effects. A variety of conditional distributions, sample sizes, levels of variate…

Unlike their monotone counterparts, nonparametric unfolding response models, which assume the item response function is unimodal, have seen little attention in the psychometric literature. This paper studies the nonparametric behavior of unfolding models by building on the work of Post (1992). The paper provides rigorous justification for a class…

Nonparametric and robust statistics (those using trimmed means and Winsorized variances) were compared for their ability to detect treatment effects in the two-sample case. The use of two specialized tests, designed to be sensitive to treatment effects when data distributions are skewed to the right, is not supported by the analyses. (SLD)

The development of the DETECT procedure marked an important advancement in nonparametric dimensionality analysis. DETECT is the first nonparametric technique to estimate the number of dimensions in a data set, estimate an effect size for multidimensionality, and identify which dimension is predominantly measured by each item. The efficacy of…