scholarly journals Analysis of model fit and item parameter of work and energy test using item response theory

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Yustiandi Yustiandi ◽  
Duden Saepuzaman
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Model Fit ◽  
Item Parameter ◽  
Response Theory ◽  
Work And Energy

Interval Estimation of Latent Variable Scores in Item Response Theory

2017 ◽  
Vol 43 (3) ◽  
pp. 259-285 ◽  
Author(s):  
Yang Liu ◽  
Ji Seung Yang
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Latent Variable ◽  
Interval Estimation ◽  
Random Variable ◽  
Item Parameter ◽  
Response Theory ◽  
Inference Problem ◽  
Irt Model ◽  
Item Parameter Estimation

The uncertainty arising from item parameter estimation is often not negligible and must be accounted for when calculating latent variable (LV) scores in item response theory (IRT). It is particularly so when the calibration sample size is limited and/or the calibration IRT model is complex. In the current work, we treat two-stage IRT scoring as a predictive inference problem: The target of prediction is a random variable that follows the true posterior of the LV conditional on the response pattern being scored. Various Bayesian, fiducial, and frequentist prediction intervals of LV scores, which can be obtained from a simple yet generic Monte Carlo recipe, are evaluated and contrasted via simulations based on several measures of prediction quality. An empirical data example is also presented to illustrate the use of candidate methods.

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.

scholarly journals Estimating the Political Center from Aggregate Data: An Item Response Theory Alternative to the Stimson Dyad Ratios Algorithm

2014 ◽  
Vol 22 (1) ◽  
pp. 115-129 ◽  
Author(s):  
Anthony J. McGann
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Median Voter ◽  
Theory Model ◽  
Free Software ◽  
Model Fit ◽  
The Political ◽  
Response Theory ◽  
The United Kingdom ◽  
Political Center

This article provides an algorithm to produce a time-series estimate of the political center (or median voter) from aggregate survey data, even when the same questions are not asked in most years. This is compared to the existing Stimson dyad ratios approach, which has been applied to various questions in political science. Unlike the dyad ratios approach, the model developed here is derived from an explicit model of individual behavior—the widely used item response theory model. I compare the results of both techniques using the data on public opinion from the United Kingdom from 1947 to 2005 from Bartle, Dellepiane-Avellaneda, and Stimson. Measures of overall model fit are provided, as well as techniques for testing model's assumptions and the fit of individual items. Full code is provided for estimation with free software WinBUGS and JAGS.

Postexamination Analysis of Objective Tests Using the Three-Parameter Item Response Theory

2014 ◽  
Vol 22 (1) ◽  
pp. 94-105
Author(s):  
Mohsen Tavakol ◽  
Mohammad Rahimi-Madiseh ◽  
Reg Dennick
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Item Parameter ◽  
Response Theory ◽  
Characteristic Curves ◽  
Test Characteristic ◽  
Test Items ◽  
Test Information ◽  
Objective Tests

Background and Purpose: Although the importance of item response theory (IRT) has been emphasized in health and medical education, in practice, few psychometricians in nurse education have used these methods to create tests that discriminate well at any level of student ability. The purpose of this study is to evaluate the psychometric properties of a real objective test using three-parameter IRT. Methods: Three-parameter IRT was used to monitor and improve the quality of the test items. Results: Item parameter indices, item characteristic curves (ICCs), test information functions, and test characteristic curves reveal aberrant items which do not assess the construct being measured. Conclusions: The results of this study provide useful information for educators to improve the quality of assessment, teaching strategies, and curricula.

scholarly journals Sample Size and Test Length for Item Parameter Estimate and Exam Parameter Estimate

2021 ◽  
Vol 9 (1) ◽  
pp. 69-78
Author(s):  
Riswan Riswan
Keyword(s):  
Item Response Theory ◽  
Sample Size ◽  
Item Response ◽  
Parameter Estimate ◽  
Test Theory ◽  
Item Parameter ◽  
Parameter Estimates ◽  
Test Length ◽  
Response Theory ◽  
The Stability

The Item Response Theory (IRT) model contains one or more parameters in the model. These parameters are unknown, so it is necessary to predict them. This paper aims (1) to determine the sample size (N) on the stability of the item parameter (2) to determine the length (n) test on the stability of the estimate parameter examinee (3) to determine the effect of the model on the stability of the item and the parameter to examine (4) to find out Effect of sample size and test length on item stability and examinee parameter estimates (5) Effect of sample size, test length, and model on item stability and examinee parameter estimates. This paper is a simulation study in which the latent trait (q) sample simulation is derived from a standard normal population of ~ N (0.1), with a specific Sample Size (N) and test length (n) with the 1PL, 2PL and 3PL models using Wingen. Item analysis was carried out using the classical theory test approach and modern test theory. Item Response Theory and data were analyzed through software R with the ltm package. The results showed that the larger the sample size (N), the more stable the estimated parameter. For the length test, which is the greater the test length (n), the more stable the estimated parameter (q).

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