scholarly journals Comparison Among New Residual-based Person-Fit Indices and Wright's Indices for Dichotomous Three-Parameter IRT Model with Standardized Tests

2010 ◽  
Vol 4 (2) ◽  
pp. 14
Author(s):  
Rashid Al-Mehrzi
Keyword(s):  
Standardized Tests ◽  
Rasch Model ◽  
Simulated Data ◽  
Statistical Properties ◽  
Fit Indices ◽  
Person Fit ◽  
Irt Model ◽  
Test Conditions ◽  
Person Fit Index ◽  
Fit Index

Wright's residual-based person fit indices were the first person fit indices with dichotomous IRT model and commonly used with Rasch model software. Although there were number of studies which suggested modifications to improve the statistical properties of the Wright's indices, they remained to lack good statistical properties.The study presented a new person fit index and how it can be interpreted and applied for detecting person misfit. Moreover, through a simulated data, the study investigated the statistical properties and the power rates of the new index and compared it with Wright's indices. Results showed that the new index had superior statistical properties under different test conditions and overcome the Wright's index. 

Detecting Random Responses in a Personality Scale Using IRT-Based Person-Fit Indices

2019 ◽  
Vol 35 (1) ◽  
pp. 126-136 ◽  
Author(s):  
Tour Liu ◽  
Tian Lan ◽  
Tao Xin
Keyword(s):  
Experimental System ◽  
Real Data ◽  
Fit Indices ◽  
Random Response ◽  
Person Fit ◽  
Index Method ◽  
Single Person ◽  
Person Fit Index ◽  
Random Responses ◽  
Response Behaviors

Abstract. Random response is a very common aberrant response behavior in personality tests and may negatively affect the reliability, validity, or other analytical aspects of psychological assessment. Typically, researchers use a single person-fit index to identify random responses. This study recommends a three-step person-fit analysis procedure. Unlike the typical single person-fit methods, the three-step procedure identifies both global misfit and local misfit individuals using different person-fit indices. This procedure was able to identify more local misfit individuals than single-index method, and a graphical method was used to visualize those particular items in which random response behaviors appear. This method may be useful to researchers in that it will provide them with more information about response behaviors, allowing better evaluation of scale administration and development of more plausible explanations. Real data were used in this study instead of simulation data. In order to create real random responses, an experimental test administration was designed. Four different random response samples were produced using this experimental system.

Adjusting Person Fit Index for Skewness in Cognitive Diagnosis Modeling

2019 ◽  
Vol 37 (2) ◽  
pp. 399-420
Author(s):  
Kevin Carl P. Santos ◽  
Jimmy de la Torre ◽  
Matthias von Davier
Keyword(s):  
Cognitive Diagnosis ◽  
Person Fit ◽  
Person Fit Index ◽  
Fit Index ◽  
Cognitive Diagnosis Modeling

Identifying and Classifying Aberrant Response Patterns Through Functional Data Analysis

2020 ◽  
Vol 45 (6) ◽  
pp. 719-749
Author(s):  
Eduardo Doval ◽  
Pedro Delicado
Keyword(s):  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Simulated Data ◽  
Real Data ◽  
Response Patterns ◽  
Fit Indices ◽  
Person Fit ◽  
Data Set ◽  
Aberrant Response Patterns

We propose new methods for identifying and classifying aberrant response patterns (ARPs) by means of functional data analysis. These methods take the person response function (PRF) of an individual and compare it with the pattern that would correspond to a generic individual of the same ability according to the item-person response surface. ARPs correspond to atypical difference functions. The ARP classification is done with functional data clustering applied to the PRFs identified as ARP. We apply these methods to two sets of simulated data (the first is used to illustrate the ARP identification methods and the second demonstrates classification of the response patterns flagged as ARP) and a real data set (a Grade 12 science assessment test, SAT, with 32 items answered by 600 examinees). For comparative purposes, ARPs are also identified with three nonparametric person-fit indices (Ht, Modified Caution Index, and ZU3). Our results indicate that the ARP detection ability of one of our proposed methods is comparable to that of person-fit indices. Moreover, the proposed classification methods enable ARP associated with either spuriously low or spuriously high scores to be distinguished.

Algorithm for Developing Job-Person Fit Index H-JFIT: An Application to TOCT and GRIT Tests

2018 ◽  
Vol 19 (4) ◽  
pp. 75-118
Author(s):  
Sehee Hong ◽  
Yoona Jang ◽  
Joohan Kim ◽  
Young-san Yoo
Keyword(s):  
Person Fit ◽  
Person Fit Index ◽  
Fit Index

scholarly journals Calculating level-specific SEM fit indices for multilevel mediation analyses

2021 ◽  
Vol 21 (1) ◽  
pp. 195-205
Author(s):  
W. Scott Comulada
Keyword(s):  
Methadone Maintenance Treatment ◽  
Maintenance Treatment ◽  
Multilevel Models ◽  
Mean Squared Error ◽  
Methadone Maintenance ◽  
Fit Indices ◽  
Mediation Analyses ◽  
Multilevel Mediation ◽  
Squared Error ◽  
Fit Index

Stata’s gsem command provides the ability to fit multilevel structural equation models (SEM) and related multilevel models. A motivating example is provided by multilevel mediation analyses (MA) conducted on patient data from Methadone Maintenance Treatment clinics in China. Multilevel MA conducted through the gsem command examined the mediating effects of patients’ treatment progression and rapport with counselors on their treatment satisfaction. Multilevel models accounted for the clustering of patient observations within clinics. SEM fit indices, such as the comparative fit index and the root mean squared error of approximation, are commonly used in the SEM model selection process. Multilevel models present challenges in constructing fit indices because there are multiple levels of hierarchy to account for in establishing goodness of fit. Level-specific fit indices have been proposed in the literature but have not been incorporated into the gsem command. I created the gsemgof command to fill this role. Model results from the gsem command are used to calculate the level-specific comparative fit index and root mean squared error of approximation fit indices. I illustrate the gsemgof command through multilevel MA applied to two-level Methadone Maintenance Treatment data.

scholarly journals A multidimensional generalized many-facet Rasch model for rubric-based performance assessment

2021 ◽  
Author(s):  
Masaki Uto
Keyword(s):  
Performance Assessment ◽  
Rasch Model ◽  
Measurement Accuracy ◽  
Estimation Method ◽  
Real Data ◽  
Monte Carlo Algorithm ◽  
Irt Model ◽  
Irt Models ◽  
Proposed Model ◽  
Problem Item

AbstractPerformance assessment, in which human raters assess examinee performance in a practical task, often involves the use of a scoring rubric consisting of multiple evaluation items to increase the objectivity of evaluation. However, even when using a rubric, assigned scores are known to depend on characteristics of the rubric’s evaluation items and the raters, thus decreasing ability measurement accuracy. To resolve this problem, item response theory (IRT) models that can estimate examinee ability while considering the effects of these characteristics have been proposed. These IRT models assume unidimensionality, meaning that a rubric measures one latent ability. In practice, however, this assumption might not be satisfied because a rubric’s evaluation items are often designed to measure multiple sub-abilities that constitute a targeted ability. To address this issue, this study proposes a multidimensional IRT model for rubric-based performance assessment. Specifically, the proposed model is formulated as a multidimensional extension of a generalized many-facet Rasch model. Moreover, a No-U-Turn variant of the Hamiltonian Markov chain Monte Carlo algorithm is adopted as a parameter estimation method for the proposed model. The proposed model is useful not only for improving the ability measurement accuracy, but also for detailed analysis of rubric quality and rubric construct validity. The study demonstrates the effectiveness of the proposed model through simulation experiments and application to real data.

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