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Reimers, Jennifer; Turner, Ronna C.; Tendeiro, Jorge N.; Lo, Wen-Juo; Keiffer, Elizabeth – Measurement: Interdisciplinary Research and Perspectives, 2023
Person-fit analyses are commonly used to detect aberrant responding in self-report data. Nonparametric person fit statistics do not require fitting a parametric test theory model and have performed well compared to other person-fit statistics. However, detection of aberrant responding has primarily focused on dominance response data, thus the…
Descriptors: Goodness of Fit, Nonparametric Statistics, Error of Measurement, Comparative Analysis
Pearl, Judea – Sociological Methods & Research, 2017
We address the task of determining, from statistical averages alone, whether a population under study consists of several subpopulations, unknown to the investigator, each responding to a given treatment markedly differently. We show that such determination is feasible in three cases: (1) randomized trials with binary treatments, (2) models where…
Descriptors: Statistical Analysis, Models, Social Science Research, Randomized Controlled Trials
Wind, Stefanie A. – Educational Measurement: Issues and Practice, 2017
Mokken scale analysis (MSA) is a probabilistic-nonparametric approach to item response theory (IRT) that can be used to evaluate fundamental measurement properties with less strict assumptions than parametric IRT models. This instructional module provides an introduction to MSA as a probabilistic-nonparametric framework in which to explore…
Descriptors: Probability, Nonparametric Statistics, Item Response Theory, Scaling
Hooper, Jay; Cowell, Ryan – Educational Assessment, 2014
There has been much research and discussion on the principles of standards-based grading, and there is a growing consensus of best practice. Even so, the actual process of implementing standards-based grading at a school or district level can be a significant challenge. There are very practical questions that remain unclear, such as how the grades…
Descriptors: True Scores, Grading, Academic Standards, Computation
Porter, Kristin E.; Balu, Rekha – MDRC, 2016
Education systems are increasingly creating rich, longitudinal data sets with frequent, and even real-time, data updates of many student measures, including daily attendance, homework submissions, and exam scores. These data sets provide an opportunity for district and school staff members to move beyond an indicators-based approach and instead…
Descriptors: Models, Prediction, Statistical Analysis, Elementary Secondary Education
Karabatsos, George; Walker, Stephen G. – Psychometrika, 2009
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…
Descriptors: Nonparametric Statistics, Item Response Theory, Models, Comparative Analysis
Lee, Young-Sun – Applied Psychological Measurement, 2007
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…
Descriptors: Nonparametric Statistics, Computation, Item Response Theory, Evaluation Methods

Junker, Brian; Sijtsma, Klaas – Applied Psychological Measurement, 2001
Discusses usability and interpretation issues for single-strategy cognitive assessment models that posit a stochastic, conjunctive relationship between a set of cognitive attributes to be assessed and performance on particular items/tasks of the assessment. Also discusses stochastic ordering and monotonicity properties that enhance the…
Descriptors: Cognitive Processes, Evaluation Methods, Item Response Theory, Models
Johnson, Matthew S. – Psychometrika, 2006
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…
Descriptors: Psychometrics, Nonparametric Statistics, Item Response Theory, Models
Xu, Xueli; Douglas, Jeff – Psychometrika, 2006
Nonparametric item response models have been developed as alternatives to the relatively inflexible parametric item response models. An open question is whether it is possible and practical to administer computerized adaptive testing with nonparametric models. This paper explores the possibility of computerized adaptive testing when using…
Descriptors: Simulation, Nonparametric Statistics, Item Analysis, Item Response Theory
Hessen, David J. – Psychometrika, 2005
In the present paper, a new family of item response theory (IRT) models for dichotomous item scores is proposed. Two basic assumptions define the most general model of this family. The first assumption is local independence of the item scores given a unidimensional latent trait. The second assumption is that the odds-ratios for all item-pairs are…
Descriptors: Item Response Theory, Scores, Test Items, Models
Roussos, Louis A.; Ozbek, Ozlem Yesim – Journal of Educational Measurement, 2006
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…
Descriptors: Evaluation Methods, Effect Size, Test Bias, Item Response Theory