scholarly journals On the Performance of Semi- and Nonparametric Item Response Functions in Computer Adaptive Tests

2021 ◽  
pp. 001316442110142
Author(s):  
Carl F. Falk ◽  
Leah M. Feuerstahler
Keyword(s):  
Item Response ◽  
Large Scale ◽  
Kernel Smoothing ◽  
Logistic Function ◽  
Item Selection ◽  
Response Functions ◽  
Adaptive Tests ◽  
Analytical Derivatives ◽  
Selection Algorithms ◽  
Nonparametric Item Response

Large-scale assessments often use a computer adaptive test (CAT) for selection of items and for scoring respondents. Such tests often assume a parametric form for the relationship between item responses and the underlying construct. Although semi- and nonparametric response functions could be used, there is scant research on their performance in a CAT. In this work, we compare parametric response functions versus those estimated using kernel smoothing and a logistic function of a monotonic polynomial. Monotonic polynomial items can be used with traditional CAT item selection algorithms that use analytical derivatives. We compared these approaches in CAT simulations with a variety of item selection algorithms. Our simulations also varied the features of the calibration and item pool: sample size, the presence of missing data, and the percentage of nonstandard items. In general, the results support the use of semi- and nonparametric item response functions in a CAT.

scholarly journals Nonparametric Item Response Models: A Comparison on Recovering True Scores

2020 ◽  
Author(s):  
Víthor Rosa Franco ◽  
Marie Wiberg
Keyword(s):  
Item Response ◽  
Kernel Smoothing ◽  
Real Data ◽  
Response Models ◽  
Item Response Models ◽  
Future Studies ◽  
Sum Scores ◽  
True Scores ◽  
Practical Implications ◽  
Nonparametric Item Response

Nonparametric procedures are used to add flexibility to models. Three nonparametric item response models have been proposed, but not directly compared: the Kernel smoothing (KS-IRT); the Davidian-Curve (DC-IRT); and the Bayesian semiparametric Rasch model (SP-Rasch). The main aim of the present study is to compare the performance of these procedures in recovering simulated true scores, using sum scores as benchmarks. The secondary aim is to compare their performances in terms of practical equivalence with real data. Overall, the results show that, apart from the DC-IRT, which is the model that performs the worse, all the other models give results quite similar to those when sum scores are used. These results are followed by a discussion with practical implications and recommendations for future studies.

Item Response Theory and Music Testing

2019 ◽  
pp. 477-503
Author(s):  
Brian Wesolowski
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Test Scores ◽  
General Framework ◽  
Logistic Function ◽  
Response Theory ◽  
Measurement Models ◽  
Latent Constructs ◽  
Item Parameters ◽  
Introductory Overview

This chapter presents an introductory overview of concepts that underscore the general framework of item response theory. “Item response theory” is a broad umbrella term used to describe a family of mathematical measurement models that consider observed test scores to be a function of latent, unobservable constructs. Most musical constructs cannot be directly measured and are therefore unobservable. Musical constructs can therefore only be inferred based on secondary, observable behaviors. Item response theory uses observable behaviors as probabilistic distributions of responses as a logistic function of person and item parameters in order to define latent constructs. This chapter describes philosophical, theoretical, and applied perspectives of item response theory in the context of measuring musical behaviors.

Item Relational Structure Theory Based on Liu’s Improved Nonparametric IRT Theory

2013 ◽  
Vol 479-480 ◽  
pp. 1193-1196
Author(s):  
Hsiang Chuan Liu ◽  
Yen Kuei Yu ◽  
Hsien Chang Tsai
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Joint Probability ◽  
Relational Structure ◽  
Structure Theory ◽  
Response Theory ◽  
Nonparametric Item Response Theory ◽  
Relational Structures ◽  
Relational Structure Theory ◽  
Nonparametric Item Response

In this paper, an extensional item relational structure theory based on the improved nonparametric item response theory is proposed. Item relational structure theory (Takeya, 1991) was developed to detect item relational structures of a group of subjects. The differences of these structures and experts knowledge structures can provide more information for planning remedial instruction, developing instruction materials, or educational researches. In this study, Lius improved nonparametric item response theory ( Liu, 2000, 2013) without the local independence assumption is used to estimate the joint probability of two items, and construct personal item relational structures. A Mathematics example is also provided in this paper to illustrate the advantages of the proposed method

scholarly journals Reanalysis of an Allocentric Navigation Strategy Scale based on Item Response Theory

2020 ◽  
Vol 2 (1) ◽  
pp. 90-105
Author(s):  
Jimmy Y. Zhong
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Large Scale ◽  
Relative Magnitude ◽  
Latent Structure ◽  
Response Theory ◽  
Future Studies ◽  
Bidimensional Model ◽  
Navigation Strategy ◽  
Component Loading

AbstractFocusing on 12 allocentric/survey-based strategy items of the Navigation Strategy Questionnaire (Zhong & Kozhevnikov, 2016), the current study applied item response theory-based analysis to determine whether a bidimensional model could better describe the latent structure of the survey-based strategy. Results from item and model fit diagnostics, categorical response and item information curves showed that an item with the lowest rotated component loading (.27) [SURVEY12], could be considered for exclusion in future studies; and that a bidimensional model with three preference-related items constituting a content factor offered a better representation of the latent structure than a unidimensional model per se. Mean scores from these three items also correlated significantly with a pointing-to-landmarks task to the same relative magnitude as the mean scores from all items, and all items excluding SURVEY12. These findings gave early evidence suggesting that the three preference-related items could constitute a subscale for deriving quick estimates of large-scale allocentric spatial processing in healthy adults in both experimental and clinical settings. Potential cognitive and brain mechanisms were discussed, followed by calls for future studies to gather greater evidence confirming the predictive validity of the full and sub scales, along with the design of new items focusing on environmental familiarity.

Psychology of Computer Use: VIII. Utilizing a Nonparametric Item Response Model to Develop Unidimensional Scales: Mokscal

1989 ◽  
Vol 68 (3) ◽  
pp. 987-1000 ◽  
Author(s):  
Elisabeth Tenvergert ◽  
Johannes Kingma ◽  
Terry Taerum
Keyword(s):  
Item Response ◽  
Goodness Of Fit ◽  
Functional Form ◽  
Search Procedure ◽  
Response Model ◽  
Scale Analysis ◽  
Item Response Model ◽  
Goodness Of Fit Test ◽  
Item Coefficient ◽  
Nonparametric Item Response

MOKSCAL is a program for the Mokken (1971) scale analysis based on a nonparametric item response model that makes no assumptions about the functional form of the item trace lines. The only constraint the Mokken model puts on the trace lines is the assumption of double monotony; that is, the item trace lines must be nondecreasing and the lines are not allowed to cross. MOKSCAL provides three procedures of scaling: a search procedure, an evaluation of the whole set of items, and an extension of an existing scale. All procedures provide a coefficient of scalability for all items that meet the criteria of the Mokken model and an item coefficient of scalability of every item. A test of robustness of the found scale can be performed to analyze whether the scale is invariant across different subgroups or samples. This robustness test may serve as a goodness-of-fit test for the established scale. The program is written in FORTRAN 77 and is suitable for both mainframe and microcomputers.

scholarly journals AUTOMATED ITEM SELECTION USING ITEM RESPONSE THEORY*

1991 ◽  
Vol 1991 (1) ◽  
pp. i-31 ◽  
Author(s):  
Martha L. Stocking ◽  
Len Swanson ◽  
Mari Pearlman
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Item Selection ◽  
Response Theory
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