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Sanaz Nazari; Walter L. Leite; A. Corinne Huggins-Manley – Educational and Psychological Measurement, 2024
Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish…
Descriptors: Social Desirability, Bias, Artificial Intelligence, Identification
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Ilagan, Michael John; Falk, Carl F. – Educational and Psychological Measurement, 2023
Administering Likert-type questionnaires to online samples risks contamination of the data by malicious computer-generated random responses, also known as bots. Although nonresponsivity indices (NRIs) such as person-total correlations or Mahalanobis distance have shown great promise to detect bots, universal cutoff values are elusive. An initial…
Descriptors: Likert Scales, Questionnaires, Artificial Intelligence, Identification
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Sedat Sen; Allan S. Cohen – Educational and Psychological Measurement, 2024
A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike's information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper's…
Descriptors: Goodness of Fit, Item Response Theory, Sample Size, Classification
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Yang, Xiangdong – Educational and Psychological Measurement, 2007
This article investigates several methods of identifying individual guessers from their response data. Both the posterior probability method and the likelihood ratio method are based on the two-state mixture modeling approach to response times. The accuracy method is based on response accuracy data. Results from the simulation study showed that…
Descriptors: Probability, Simulation, Test Items, Models