scholarly journals Latent-variable Approaches Utilizing Both Item Scores and Response Times To Detect Test Fraud

2021 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Sandip Sinharay
Keyword(s):  
Latent Variable ◽  
Response Times ◽  
Real Data ◽  
Latent Variable Models ◽  
Detailed Review ◽  
Item Scores ◽  
Educational Tests

Abstract There is a growing interest in approaches based on latent-variable models for detecting fraudulent behavior on educational tests. Wollack and Schoenig (2018) noted the presence of five types of statistical/psychometric approaches to detect the three broad types of test fraud that occur in educational tests. This paper includes a brief review of the five types of statistical/psychometric approaches mentioned by Wollack and Schoenig (2018). This paper then includes a more detailed review of the recent approaches for detecting test fraud using both item scores and response times—all of these approaches are based on latent-variable models. A real data example demonstrates the use of two of the approaches.

scholarly journals Classical latent variable models for medical research

2008 ◽  
Vol 17 (1) ◽  
pp. 5-32 ◽  
Author(s):  
Sophia Rabe-Hesketh ◽  
Anders Skrondal
Keyword(s):  
Medical Research ◽  
Latent Variable ◽  
Structural Equation ◽  
Latent Class ◽  
Structural Equation Models ◽  
Real Data ◽  
Latent Variable Models ◽  
Latent Class Models ◽  
Forced Expiratory Flow ◽  
Class Models

Latent variable models are commonly used in medical statistics, although often not referred to under this name. In this paper we describe classical latent variable models such as factor analysis, item response theory, latent class models and structural equation models. Their usefulness in medical research is demonstrated using real data. Examples include measurement of forced expiratory flow, measurement of physical disability, diagnosis of myocardial infarction and modelling the determinants of clients' satisfaction with counsellors' interviews.

scholarly journals Detection of Item Preknowledge Using Response Times

2020 ◽  
Vol 44 (5) ◽  
pp. 376-392
Author(s):  
Sandip Sinharay
Keyword(s):  
Type I Error ◽  
Response Times ◽  
Real Data ◽  
Standard Normal Distribution ◽  
Major Type ◽  
Type I ◽  
Standard Normal ◽  
The Difference ◽  
Item Scores ◽  
Item Preknowledge

Benefiting from item preknowledge is a major type of fraudulent behavior during educational assessments. This article suggests a new statistic that can be used for detecting the examinees who may have benefited from item preknowledge using their response times. The statistic quantifies the difference in speed between the compromised items and the non-compromised items of the examinees. The distribution of the statistic under the null hypothesis of no preknowledge is proved to be the standard normal distribution. A simulation study is used to evaluate the Type I error rate and power of the suggested statistic. A real data example demonstrates the usefulness of the new statistic that is found to provide information that is not provided by statistics based only on item scores.

Right-Wing Authoritarians Aren't Very Funny: RWA, Personality, and Creative Humor Production

2020 ◽  
Author(s):  
Paul Silvia ◽  
Alexander P. Christensen ◽  
Katherine N. Cotter
Keyword(s):  
Young Adults ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Openness To Experience ◽  
Rasch Models ◽  
Right Wing ◽  
Right Wing Authoritarianism ◽  
Humor Appreciation ◽  
Negative Effect

Right-wing authoritarianism (RWA) has well-known links with humor appreciation, such as enjoying jokes that target deviant groups, but less is known about RWA and creative humor production—coming up with funny ideas oneself. A sample of 186 young adults completed a measure of RWA, the HEXACO-100, and 3 humor production tasks that involved writing funny cartoon captions, creating humorous definitions for quirky concepts, and completing joke stems with punchlines. The humor responses were scored by 8 raters and analyzed with many-facet Rasch models. Latent variable models found that RWA had a large, significant effect on humor production (β = -.47 [-.65, -.30], p < .001): responses created by people high in RWA were rated as much less funny. RWA’s negative effect on humor was smaller but still significant (β = -.25 [-.49, -.01], p = .044) after controlling for Openness to Experience (β = .39 [.20, .59], p < .001) and Conscientiousness (β = -.21 [-.41, -.02], p = .029). Taken together, the findings suggest that people high in RWA just aren’t very funny.

scholarly journals Looking behind the choice of organic: A cross-country analysis applying Integrated Choice and Latent Variable Models

Appetite ◽  
2021 ◽  
pp. 105591
Author(s):  
Ching-Hua Yeh ◽  
Monika Hartmann ◽  
Matthew Gorton ◽  
Barbara Tocco ◽  
Virginie Amilien ◽  
...  
Keyword(s):  
Latent Variable ◽  
Latent Variable Models ◽  
Country Analysis ◽  
Cross Country

scholarly journals Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4290
Author(s):  
Dongmei Zhang ◽  
Yuyang Zhang ◽  
Bohou Jiang ◽  
Xinwei Jiang ◽  
Zhijiang Kang
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Processes ◽  
Latent Variable ◽  
History Matching ◽  
Latent Variable Models ◽  
High Dimensional ◽  
Model Parameters ◽  
Variable Model ◽  
Assisted History Matching ◽  
Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

Using a Response Time–Based Expected A Posteriori Estimator to Control for Differential Speededness in Computerized Adaptive Test

2021 ◽  
pp. 014662162110146
Author(s):  
Justin L. Kern ◽  
Edison Choe
Keyword(s):  
Response Times ◽  
Real Data ◽  
Joint Estimation ◽  
Item Selection ◽  
A Posteriori ◽  
Computerized Adaptive Test ◽  
Maximum Information ◽  
Adaptive Test ◽  
Estimation And Control ◽  
And Control

This study investigates using response times (RTs) with item responses in a computerized adaptive test (CAT) setting to enhance item selection and ability estimation and control for differential speededness. Using van der Linden’s hierarchical framework, an extended procedure for joint estimation of ability and speed parameters for use in CAT is developed following van der Linden; this is called the joint expected a posteriori estimator (J-EAP). It is shown that the J-EAP estimate of ability and speededness outperforms the standard maximum likelihood estimator (MLE) of ability and speededness in terms of correlation, root mean square error, and bias. It is further shown that under the maximum information per time unit item selection method (MICT)—a method which uses estimates for ability and speededness directly—using the J-EAP further reduces average examinee time spent and variability in test times between examinees above the resulting gains of this selection algorithm with the MLE while maintaining estimation efficiency. Simulated test results are further corroborated with test parameters derived from a real data example.

scholarly journals Longitudinal Analysis of Patient-Reported Outcomes in Clinical Trials: Applications of Multilevel and Multidimensional Item Response Theory

Psychometrika ◽  
2021 ◽  
Author(s):  
Li Cai ◽  
Carrie R. Houts
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Latent Variable ◽  
Patient Reported Outcomes ◽  
Multilevel Models ◽  
Latent Variable Models ◽  
Trial Data ◽  
Regulatory Science ◽  
Measurement Information ◽  
Patient Reported

AbstractWith decades of advance research and recent developments in the drug and medical device regulatory approval process, patient-reported outcomes (PROs) are becoming increasingly important in clinical trials. While clinical trial analyses typically treat scores from PROs as observed variables, the potential to use latent variable models when analyzing patient responses in clinical trial data presents novel opportunities for both psychometrics and regulatory science. An accessible overview of analyses commonly used to analyze longitudinal trial data and statistical models familiar in both psychometrics and biometrics, such as growth models, multilevel models, and latent variable models, is provided to call attention to connections and common themes among these models that have found use across many research areas. Additionally, examples using empirical data from a randomized clinical trial provide concrete demonstrations of the implementation of these models. The increasing availability of high-quality, psychometrically rigorous assessment instruments in clinical trials, of which the Patient-Reported Outcomes Measurement Information System (PROMIS®) is a prominent example, provides rare possibilities for psychometrics to help improve the statistical tools used in regulatory science.

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