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Direct linkA. M. Sadek; Fahad Al-Muhlaki – Measurement: Interdisciplinary Research and Perspectives, 2024
In this study, the accuracy of the artificial neural network (ANN) was assessed considering the uncertainties associated with the randomness of the data and the lack of learning. The Monte-Carlo algorithm was applied to simulate the randomness of the input variables and evaluate the output distribution. It has been shown that under certain…
Descriptors: Monte Carlo Methods, Accuracy, Artificial Intelligence, Guidelines
Najera, Hector – Measurement: Interdisciplinary Research and Perspectives, 2023
Measurement error affects the quality of population orderings of an index and, hence, increases the misclassification of the poor and the non-poor groups and affects statistical inferences from binary regression models. Hence, the conclusions about the extent, profile, and distribution of poverty are likely to be misleading. However, the size and…
Descriptors: Poverty, Error of Measurement, Classification, Statistical Inference
Kalkan, Ömür Kaya – Measurement: Interdisciplinary Research and Perspectives, 2022
The four-parameter logistic (4PL) Item Response Theory (IRT) model has recently been reconsidered in the literature due to the advances in the statistical modeling software and the recent developments in the estimation of the 4PL IRT model parameters. The current simulation study evaluated the performance of expectation-maximization (EM),…
Descriptors: Comparative Analysis, Sample Size, Test Length, Algorithms
Luo, Yong; Liang, Xinya – Measurement: Interdisciplinary Research and Perspectives, 2019
Current methods that simultaneously model differential testlet functioning (DTLF) and differential item functioning (DIF) constrain the variances of latent ability and testlet effects to be equal between the focal and the reference groups. Such a constraint can be stringent and unrealistic with real data. In this study, we propose a multigroup…
Descriptors: Test Items, Item Response Theory, Test Bias, Models
Leventhal, Brian C.; Stone, Clement A. – Measurement: Interdisciplinary Research and Perspectives, 2018
Interest in Bayesian analysis of item response theory (IRT) models has grown tremendously due to the appeal of the paradigm among psychometricians, advantages of these methods when analyzing complex models, and availability of general-purpose software. Possible models include models which reflect multidimensionality due to designed test structure,…
Descriptors: Bayesian Statistics, Item Response Theory, Models, Psychometrics
Guyon, Hervé; Tensaout, Mouloud – Measurement: Interdisciplinary Research and Perspectives, 2016
In this article, the authors extend the results of Aguirre-Urreta, Rönkkö, and Marakas (2016) concerning the omission of a relevant causal indicator by testing the validity of the assumption that causal indicators are entirely superfluous to the measurement model and discuss the implications for measurement theory. Contrary to common wisdom…
Descriptors: Causal Models, Structural Equation Models, Formative Evaluation, Measurement
