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.

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.

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. 

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

Is It Worthy to Take Account of the “Guessing” in the Performance of the Raven Test? Calling for the Principle of Parsimony for Test Validation

2020 ◽  
pp. 073428292093092 ◽  
Author(s):  
Patrícia Silva Lúcio ◽  
Joachim Vandekerckhove ◽  
Guilherme V. Polanczyk ◽  
Hugo Cogo-Moreira
Keyword(s):  
Preschool Children ◽  
Item Response Theory ◽  
Item Response ◽  
Real Data ◽  
Model Fit ◽  
Test Validation ◽  
Fit Indices ◽  
Test Information ◽  
Model Fit Indices ◽  
Principle Of Parsimony

The present study compares the fit of two- and three-parameter logistic (2PL and 3PL) models of item response theory in the performance of preschool children on the Raven’s Colored Progressive Matrices. The test of Raven is widely used for evaluating nonverbal intelligence of factor g. Studies comparing models with real data are scarce on the literature and this is the first to compare models of two and three parameters for the test of Raven, evaluating the informational gain of considering guessing probability. Participants were 582 Brazilian’s preschool children ( Mage = 57 months; SD = 7 months; 46% female) who responded individually to the instrument. The model fit indices suggested that the 2PL fit better to the data. The difficulty and ability parameters were similar between the models, with almost perfect correlations. Differences were observed in terms of discrimination and test information. The principle of parsimony must be called for comparing models.

The many null distributions of person fit indices

Psychometrika ◽  
1990 ◽  
Vol 55 (1) ◽  
pp. 75-106 ◽  
Author(s):  
Ivo W. Molenaar ◽  
Herbert Hoijtink
Keyword(s):  
Fit Indices ◽  
Person Fit ◽  
Null Distributions ◽  
The Many

Random Response Analysis for Nonlinear Systems of Composite Laminates

2011 ◽  
Vol 415-417 ◽  
pp. 56-61
Author(s):  
Feng Xiang You ◽  
Fei Zhang ◽  
Buo Lei Zuo
Keyword(s):  
Nonlinear Systems ◽  
Composite Laminates ◽  
Response Analysis ◽  
Random Response ◽  
Random Parameters ◽  
Engineering Structures ◽  
Central Difference ◽  
Central Difference Method ◽  
Random Responses ◽  
Random Nature

Geometric parameters of composite materials often have a random nature in engineering structures. How to study random response and statistical properties of nonlinear systems with random parameters has a very important significance for reliability and optimization of structural design. In this paper, perturbation method and random central difference method are explored to establish composite nonlinear vibration equations and computational model to study random responses of nonlinear systems with random parameters under deterministic loading of the composite laminates, numerical examples illustrate the correctness of the algorithm.

scholarly journals Robustness of BW aberrance indices against test length

2011 ◽  
pp. 310-318
Keyword(s):  
Model Fit ◽  
Data Matrix ◽  
Fit Indices ◽  
Test Length ◽  
Person Fit ◽  
Detection Rates ◽  
Error Ratio ◽  
Study Test ◽  
Item Data ◽  
Sem Analyses

Many research had shown person fit indices might be influenced by the factor of test length on their detection rates of aberrant responses. The purpose of this study was to examine test length effects on the BW aberrance indices. Three conditions were designed in this study: test length (K, including 25, 50,100, and 200 items), ability ratio (T/K, defined as the total person score divided by test length K), and error ratio (E/K, defined as the number of errors within ability level divided by test length). Four 100-person times varying-item data matrices (100x25, 100x50, 100x100, and 100x200) were randomly generated and permuted 500 times for each data matrix through 20 repeats. Results showed that after partialling out the factors of E/K and T/K, the effect of test length on the association between the two indices was very slight. In nonlinear regression analyses, E/K and T/K can predict more than 76 and 73 percent of the variances of the B index and that of the W index, respectively, but test length with both very slight contributions on them. Furthermore, a very good model fit generated from SEM analyses also showed the effect of test length on the B and W indices were very tiny. All these pieces of evidence endorsed the B and W indices were robust with test length.

Efficiency and Travel Agencies

2016 ◽  
pp. 211-235
Author(s):  
Dejan Dragan ◽  
Tomaž Kramberger ◽  
Darja Topolšek
Keyword(s):  
Structural Equation ◽  
Estimation Method ◽  
Real Data ◽  
Likelihood Method ◽  
Equation Modeling ◽  
Fit Indices ◽  
Structural Part ◽  
Positive Effects ◽  
Travel Agencies ◽  
Bayesian Structural Equation Modeling

The chapter deals with Bayesian structural equation modeling (SEM) for the case of travel agencies. The focus of research is the investigation of possible impacts of external integration with transport suppliers on the efficiency of travel agencies. In order to calculate the efficiency, the data envelopment analysis was used. For the construction of the measurement part of the model, the confirmatory factor analysis (CFA) was conducted, while its structural part was developed by the means of SEM procedure. When conducting the CFA and SEM procedures, the Bayesian estimation method was employed. Its performance was also compared with the maximum likelihood method and the fit indices of both methods were inspected. The results show that the derived model fits well to the real data. The study confirms certain positive effects of the external integration on the efficiency. This finding could represent an important guideline for the managers of the travel agencies.

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