Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 6 |
Descriptor
Item Response Theory | 20 |
Nonparametric Statistics | 20 |
Models | 11 |
Evaluation Methods | 4 |
Test Construction | 4 |
Test Items | 4 |
Probability | 3 |
Scaling | 3 |
Scores | 3 |
Statistical Analysis | 3 |
Adaptive Testing | 2 |
More ▼ |
Source
Applied Psychological… | 7 |
Psychometrika | 4 |
Educational Measurement:… | 2 |
Psychological Methods | 2 |
Grantee Submission | 1 |
Journal of Applied Measurement | 1 |
Journal of Educational and… | 1 |
Measurement:… | 1 |
Author
Publication Type
Reports - Descriptive | 20 |
Journal Articles | 18 |
Speeches/Meeting Papers | 2 |
Education Level
Elementary Secondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
Trends in International… | 1 |
What Works Clearinghouse Rating
Sengül Avsar, Asiye – Measurement: Interdisciplinary Research and Perspectives, 2020
In order to reach valid and reliable test scores, various test theories have been developed, and one of them is nonparametric item response theory (NIRT). Mokken Models are the most widely known NIRT models which are useful for small samples and short tests. Mokken Package is useful for Mokken Scale Analysis. An important issue about validity is…
Descriptors: Response Style (Tests), Nonparametric Statistics, Item Response Theory, Test Validity
Wind, Stefanie A. – Educational Measurement: Issues and Practice, 2018
In this digital ITEMS module, we introduce the framework of nonparametric item response theory (IRT), in particular Mokken scaling, which can be used to evaluate fundamental measurement properties with less strict assumptions than parametric IRT models. We walk through the key distinction between parametric and nonparametric models, introduce the…
Descriptors: Educational Assessment, Nonparametric Statistics, Item Response Theory, Scaling
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
Arenson, Ethan A.; Karabatsos, George – Grantee Submission, 2017
Item response models typically assume that the item characteristic (step) curves follow a logistic or normal cumulative distribution function, which are strictly monotone functions of person test ability. Such assumptions can be overly-restrictive for real item response data. We propose a simple and more flexible Bayesian nonparametric IRT model…
Descriptors: Bayesian Statistics, Item Response Theory, Nonparametric Statistics, Models
van der Ark, L. Andries; Bergsma, Wicher P. – Psychometrika, 2010
In contrast to dichotomous item response theory (IRT) models, most well-known polytomous IRT models do not imply stochastic ordering of the latent trait by the total test score (SOL). This has been thought to make the ordering of respondents on the latent trait using the total test score questionable and throws doubt on the justifiability of using…
Descriptors: Scores, Nonparametric Statistics, Item Response Theory, Models
Scheiblechner, Hartmann – Psychometrika, 2007
The (univariate) isotonic psychometric (ISOP) model (Scheiblechner, 1995) is a nonparametric IRT model for dichotomous and polytomous (rating scale) psychological test data. A weak subject independence axiom W1 postulates that the subjects are ordered in the same way except for ties (i.e., similarly or isotonically) by all items of a psychological…
Descriptors: Psychometrics, Intervals, Rating Scales, Psychological Testing

Habing, Brian – Applied Psychological Measurement, 2001
Discusses ideas underlying nonparametric regression and the parametric bootstrap with an overview of their application to item response theory and the assessment of local dependence. Illustrates the use of the method in assessing local dependence that varies with examinee trait levels. (SLD)
Descriptors: Item Response Theory, Nonparametric Statistics, Regression (Statistics)

Karabatsos, George – Journal of Applied Measurement, 2001
Describes similarities and differences between additive conjoint measurement and the Rasch model, and formalizes some new nonparametric item response models that are, in a sense, probabilistic measurement theory models. Applies these new models to published and simulated data. (SLD)
Descriptors: Item Response Theory, Measurement Techniques, Nonparametric Statistics, Probability

van der Ark, L. Andries – Applied Psychological Measurement, 2001
Describes relationships among 20 polytomous item response theory (IRT) models (parametric and nonparametric) and 8 measurement properties relevant to polytomous IRT. Provides three tables to assist in the choice of an appropriate model and demonstrates the use of the models in test construction. (SLD)
Descriptors: Item Response Theory, Models, Nonparametric Statistics, Test Construction

Scheiblechner, Hartmann – Psychometrika, 2003
Presented nonparametric tests for testing the validity of polytomous unidimensional ordinal probabilistic polytomous item response theory models along with procedures for testing the comonotonicity of two item sets and for item selection. Describes advantages of the new approach. (SLD)
Descriptors: Item Response Theory, Nonparametric Statistics, Selection, Test Items

Vermunt, Jeroen K. – Applied Psychological Measurement, 2001
Presents a general class of ordinal logit models that specifies equality and inequality constraints on sums of conditional response probabilities. Uses maximum likelihood to estimate these models, making their assumptions testable with likelihood-ratio statistics. Illustrates the proposed models with an example using reported adult crying…
Descriptors: Item Response Theory, Maximum Likelihood Statistics, Models, Nonparametric Statistics

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
Samejima, Fumiko – 1998
Item response theory (IRT) has been adapted as the theoretical foundation of computerized adaptive testing (CAT) for several decades. In applying IRT to CAT, there are certain considerations that are essential, and yet tend to be neglected. These essential issues are addressed in this paper, and then several ways of eliminating noise and bias in…
Descriptors: Ability, Adaptive Testing, Estimation (Mathematics), Item Response Theory

de Koning, Els; Sijtsma, Klaas; Hamers, Jo H. M. – Applied Psychological Measurement, 2002
Discusses the use of the nonparametric item response theory (IRT) Mokken models of monotone homogeneity and double monotonicity and the parametric Rasch and Verhelst models for the analysis of binary test data. Concludes that the simultaneous use of several IRT models for practical data analysis provides more insight into the structure of tests…
Descriptors: Comparative Analysis, Induction, Item Response Theory, Nonparametric Statistics

Bolt, Daniel M. – Applied Psychological Measurement, 2001
Presents a new nonparametric method for constructing a spatial representation of multidimensional test structure, the Conditional Covariance-based SCALing (CCSCAL) method. Describes an index to measure the accuracy of the representation. Uses simulation and real-life data analyses to show that the method provides a suitable approximation to…
Descriptors: Analysis of Covariance, Item Response Theory, Nonparametric Statistics, Scaling
Previous Page | Next Page »
Pages: 1 | 2