scholarly journals irtplay: An R Package for Online Item Calibration, Scoring, Evaluation of Model Fit, and Useful Functions for Unidimensional IRT

2020 ◽  
Vol 44 (7-8) ◽  
pp. 563-565
Author(s):  
Hwanggyu Lim ◽  
Craig S. Wells
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
R Package ◽  
Model Fit ◽  
Parameter Estimates ◽  
Model Data ◽  
Polytomous Items ◽  
Item Calibration ◽  
Irt Model ◽  
Irt Models

The R package irtplay provides practical tools for unidimensional item response theory (IRT) models that conveniently enable users to conduct many analyses related to IRT. For example, the irtplay includes functions for calibrating online items, scoring test-takers’ proficiencies, evaluating IRT model-data fit, and importing item and/or proficiency parameter estimates from the output of popular IRT software. In addition, the irtplay package supports mixed-item formats consisting of dichotomous and polytomous items.

scholarly journals GGUM: An R Package for Fitting the Generalized Graded Unfolding Model

2018 ◽  
Vol 43 (2) ◽  
pp. 172-173 ◽  
Author(s):  
Jorge N. Tendeiro ◽  
Sebastian Castro-Alvarez
Keyword(s):  
Operating System ◽  
Item Response Theory ◽  
Item Response ◽  
Ideal Point ◽  
R Package ◽  
Model Fit ◽  
Data Matrix ◽  
Unfolding Model ◽  
Irt Model ◽  
Generalized Graded Unfolding Model

In this article, the newly created GGUM R package is presented. This package finally brings the generalized graded unfolding model (GGUM) to the front stage for practitioners and researchers. It expands the possibilities of fitting this type of item response theory (IRT) model to settings that, up to now, were not possible (thus, beyond the limitations imposed by the widespread GGUM2004 software). The outcome is therefore a unique software, not limited by the dimensions of the data matrix or the operating system used. It includes various routines that allow fitting the model, checking model fit, plotting the results, and also interacting with GGUM2004 for those interested. The software should be of interest to all those who are interested in IRT in general or to ideal point models in particular.

Clustering students according to their proficiency: a comparison between different approaches based on item response theory models

2021 ◽  
pp. 43-48
Author(s):  
Rosa Fabbricatore ◽  
Francesco Palumbo
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Clustering Algorithm ◽  
Latent Class ◽  
Latent Class Models ◽  
Response Theory ◽  
Homogeneous Groups ◽  
Final Grade ◽  
Irt Model ◽  
Irt Models

Evaluating learners' competencies is a crucial concern in education, and home and classroom structured tests represent an effective assessment tool. Structured tests consist of sets of items that can refer to several abilities or more than one topic. Several statistical approaches allow evaluating students considering the items in a multidimensional way, accounting for their structure. According to the evaluation's ending aim, the assessment process assigns a final grade to each student or clusters students in homogeneous groups according to their level of mastery and ability. The latter represents a helpful tool for developing tailored recommendations and remediations for each group. At this aim, latent class models represent a reference. In the item response theory (IRT) paradigm, the multidimensional latent class IRT models, releasing both the traditional constraints of unidimensionality and continuous nature of the latent trait, allow to detect sub-populations of homogeneous students according to their proficiency level also accounting for the multidimensional nature of their ability. Moreover, the semi-parametric formulation leads to several advantages in practice: It avoids normality assumptions that may not hold and reduces the computation demanding. This study compares the results of the multidimensional latent class IRT models with those obtained by a two-step procedure, which consists of firstly modeling a multidimensional IRT model to estimate students' ability and then applying a clustering algorithm to classify students accordingly. Regarding the latter, parametric and non-parametric approaches were considered. Data refer to the admission test for the degree course in psychology exploited in 2014 at the University of Naples Federico II. Students involved were N=944, and their ability dimensions were defined according to the domains assessed by the entrance exam, namely Humanities, Reading and Comprehension, Mathematics, Science, and English. In particular, a multidimensional two-parameter logistic IRT model for dichotomously-scored items was considered for students' ability estimation.

scholarly journals IRTBEMM: An R Package for Estimating IRT Models With Guessing or Slipping Parameters

2020 ◽  
Vol 44 (7-8) ◽  
pp. 566-567
Author(s):  
Shaoyang Guo ◽  
Chanjin Zheng ◽  
Justin L. Kern
Keyword(s):  
Maximum Likelihood ◽  
Item Response Theory ◽  
Item Response ◽  
R Package ◽  
Response Theory ◽  
Estimation Algorithms ◽  
Irt Models ◽  
Irt Estimation

A recently released R package IRTBEMM is presented in this article. This package puts together several new estimation algorithms (Bayesian EMM, Bayesian E3M, and their maximum likelihood versions) for the Item Response Theory (IRT) models with guessing and slipping parameters (e.g., 3PL, 4PL, 1PL-G, and 1PL-AG models). IRTBEMM should be of interest to the researchers in IRT estimation and applying IRT models with the guessing and slipping effects to real datasets.

scholarly journals Entropy-Based Measures for Person Fit in Item Response Theory

2017 ◽  
Vol 41 (7) ◽  
pp. 512-529 ◽  
Author(s):  
William R. Dardick ◽  
Brandi A. Weiss
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Preliminary Evidence ◽  
Model Fit ◽  
Response Patterns ◽  
Person Fit ◽  
Response Theory ◽  
Logistic Regression Models ◽  
Irt Models

This article introduces three new variants of entropy to detect person misfit ( Ei, EMi, and EMRi), and provides preliminary evidence that these measures are worthy of further investigation. Previously, entropy has been used as a measure of approximate data–model fit to quantify how well individuals are classified into latent classes, and to quantify the quality of classification and separation between groups in logistic regression models. In the current study, entropy is explored through conceptual examples and Monte Carlo simulation comparing entropy with established measures of person fit in item response theory (IRT) such as lz, lz*, U, and W. Simulation results indicated that EMi and EMRi were successfully able to detect aberrant response patterns when comparing contaminated and uncontaminated subgroups of persons. In addition, EMi and EMRi performed similarly in showing separation between the contaminated and uncontaminated subgroups. However, EMRi may be advantageous over other measures when subtests include a small number of items. EMi and EMRi are recommended for use as approximate person-fit measures for IRT models. These measures of approximate person fit may be useful in making relative judgments about potential persons whose response patterns do not fit the theoretical model.

On Longitudinal Item Response Theory Models: A Didactic

2019 ◽  
Vol 45 (3) ◽  
pp. 339-368 ◽  
Author(s):  
Chun Wang ◽  
Steven W. Nydick
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Real Data ◽  
Measurement Model ◽  
Higher Order ◽  
Response Theory ◽  
Latent Growth ◽  
Multidimensional Irt ◽  
Irt Model ◽  
Irt Models

Recent work on measuring growth with categorical outcome variables has combined the item response theory (IRT) measurement model with the latent growth curve model and extended the assessment of growth to multidimensional IRT models and higher order IRT models. However, there is a lack of synthetic studies that clearly evaluate the strength and limitations of different multilevel IRT models for measuring growth. This study aims to introduce the various longitudinal IRT models, including the longitudinal unidimensional IRT model, longitudinal multidimensional IRT model, and longitudinal higher order IRT model, which cover a broad range of applications in education and social science. Following a comparison of the parameterizations, identification constraints, strengths, and weaknesses of the different models, a real data example is provided to illustrate the application of different longitudinal IRT models to model students’ growth trajectories on multiple latent abilities.

scholarly journals A study of alternative approaches to non-normal latent trait distributions in item response theory models used for health outcome measurement

2020 ◽  
Vol 29 (4) ◽  
pp. 1030-1048
Author(s):  
Niels Smits ◽  
Oğuzhan Öğreden ◽  
Mauricio Garnier-Villarreal ◽  
Caroline B Terwee ◽  
R Philip Chalmers
Keyword(s):  
Health Outcome ◽  
Item Response Theory ◽  
Health Outcomes ◽  
Item Response ◽  
Outcome Measurement ◽  
Latent Trait ◽  
Parameter Estimates ◽  
Response Theory ◽  
Zero Inflation ◽  
Irt Models

It is often unrealistic to assume normally distributed latent traits in the measurement of health outcomes. If normality is violated, the item response theory (IRT) models that are used to calibrate questionnaires may yield parameter estimates that are biased. Recently, IRT models were developed for dealing with specific deviations from normality, such as zero-inflation (“excess zeros”) and skewness. However, these models have not yet been evaluated under conditions representative of item bank development for health outcomes, characterized by a large number of polytomous items. A simulation study was performed to compare the bias in parameter estimates of the graded response model (GRM), polytomous extensions of the zero-inflated mixture IRT (ZIM-GRM), and Davidian Curve IRT (DC-GRM). In the case of zero-inflation, the GRM showed high bias overestimating discrimination parameters and yielding estimates of threshold parameters that were too high and too close to one another, while ZIM-GRM showed no bias. In the case of skewness, the GRM and DC-GRM showed little bias with the GRM showing slightly better results. Consequences for the development of health outcome measures are discussed.

scholarly journals About item response theory models and how they work

2018 ◽  
Vol 29 (1) ◽  
pp. 35-44
Author(s):  
Nell Sedransk
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Scoring System ◽  
Response Theory ◽  
Irt Model ◽  
Its Analysis ◽  
Irt Models ◽  
Actual Model ◽  
Item Response Theory Models ◽  
Better Than

This article is about FMCSA data and its analysis. The article responds to the two-part question: How does an Item Response Theory (IRT) model work differently . . . or better than any other model? The response to the first part is a careful, completely non-technical exposition of the fundamentals for IRT models. It differentiates IRT models from other models by providing the rationale underlying IRT modeling and by using graphs to illustrate two key properties for data items. The response to the second part of the question about superiority of an IRT model is, “it depends.” For FMCSA data, serious challenges arise from complexity of the data and from heterogeneity of the carrier industry. Questions are posed that will need to be addressed to determine the success of the actual model developed and of the scoring system.

scholarly journals Parameter Estimation Accuracy of the Effort-Moderated Item Response Theory Model Under Multiple Assumption Violations

2020 ◽  
pp. 001316442094989
Author(s):  
Joseph A. Rios ◽  
James Soland
Keyword(s):  
Parameter Estimation ◽  
Item Response Theory ◽  
Item Response ◽  
Item Parameter ◽  
Estimation Accuracy ◽  
Parameter Estimates ◽  
Response Theory ◽  
Irt Model ◽  
Ability Estimates ◽  
Ability Parameter

As low-stakes testing contexts increase, low test-taking effort may serve as a serious validity threat. One common solution to this problem is to identify noneffortful responses and treat them as missing during parameter estimation via the effort-moderated item response theory (EM-IRT) model. Although this model has been shown to outperform traditional IRT models (e.g., two-parameter logistic [2PL]) in parameter estimation under simulated conditions, prior research has failed to examine its performance under violations to the model’s assumptions. Therefore, the objective of this simulation study was to examine item and mean ability parameter recovery when violating the assumptions that noneffortful responding occurs randomly (Assumption 1) and is unrelated to the underlying ability of examinees (Assumption 2). Results demonstrated that, across conditions, the EM-IRT model provided robust item parameter estimates to violations of Assumption 1. However, bias values greater than 0.20 SDs were observed for the EM-IRT model when violating Assumption 2; nonetheless, these values were still lower than the 2PL model. In terms of mean ability estimates, model results indicated equal performance between the EM-IRT and 2PL models across conditions. Across both models, mean ability estimates were found to be biased by more than 0.25 SDs when violating Assumption 2. However, our accompanying empirical study suggested that this biasing occurred under extreme conditions that may not be present in some operational settings. Overall, these results suggest that the EM-IRT model provides superior item and equal mean ability parameter estimates in the presence of model violations under realistic conditions when compared with the 2PL model.

scholarly journals Using the Stan Program for Bayesian Item Response Theory

2017 ◽  
Vol 78 (3) ◽  
pp. 384-408 ◽  
Author(s):  
Yong Luo ◽  
Hong Jiao
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Model Comparison ◽  
Response Model ◽  
Graded Response Model ◽  
Response Theory ◽  
Irt Model ◽  
Irt Models ◽  
Nominal Response Model ◽  
Graded Response

Stan is a new Bayesian statistical software program that implements the powerful and efficient Hamiltonian Monte Carlo (HMC) algorithm. To date there is not a source that systematically provides Stan code for various item response theory (IRT) models. This article provides Stan code for three representative IRT models, including the three-parameter logistic IRT model, the graded response model, and the nominal response model. We demonstrate how IRT model comparison can be conducted with Stan and how the provided Stan code for simple IRT models can be easily extended to their multidimensional and multilevel cases.

Advancing and Evaluating IRT Model Data Fit Indices in Organizational Research

2019 ◽  
Vol 23 (3) ◽  
pp. 457-486 ◽  
Author(s):  
Christopher D. Nye ◽  
Seang-Hwane Joo ◽  
Bo Zhang ◽  
Stephen Stark
Keyword(s):  
Ideal Point ◽  
Past Research ◽  
Model Fit ◽  
Test Model ◽  
Fit Indices ◽  
Model Data ◽  
Organizational Research ◽  
Irt Model ◽  
Irt Models ◽  
Wide Range

Item response theory (IRT) models have a number of advantages for developing and evaluating scales in organizational research. However, these advantages can be obtained only when the IRT model used to estimate the parameters fits the data well. Therefore, examining IRT model fit is important before drawing conclusions from the data. To test model fit, a wide range of indices are available in the IRT literature and have demonstrated utility in past research. Nevertheless, the performance of many of these indices for detecting misfit has not been directly compared in simulations. The current study evaluates a number of these indices to determine their utility for detecting various types of misfit in both dominance and ideal point IRT models. Results indicate that some indices are more effective than others but that none of the indices accurately detected misfit due to multidimensionality in the data. The implications of these results for future organizational research are discussed.

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