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Vinay Kumar Yadav; Shakti Prasad – Measurement: Interdisciplinary Research and Perspectives, 2024
In sample survey analysis, accurate population mean estimation is an important task, but traditional approaches frequently ignore the intricacies of real-world data, leading to biassed results. In order to handle uncertainties, indeterminacies, and ambiguity, this work presents an innovative approach based on neutrosophic statistics. We proposed…
Descriptors: Sampling, Statistical Bias, Predictor Variables, Predictive Measurement
Donoghue, John R.; McClellan, Catherine A.; Hess, Melinda R. – ETS Research Report Series, 2022
When constructed-response items are administered for a second time, it is necessary to evaluate whether the current Time B administration's raters have drifted from the scoring of the original administration at Time A. To study this, Time A papers are sampled and rescored by Time B scorers. Commonly the scores are compared using the proportion of…
Descriptors: Item Response Theory, Test Construction, Scoring, Testing
Xiao, Leifeng; Hau, Kit-Tai – Educational and Psychological Measurement, 2023
We examined the performance of coefficient alpha and its potential competitors (ordinal alpha, omega total, Revelle's omega total [omega RT], omega hierarchical [omega h], greatest lower bound [GLB], and coefficient "H") with continuous and discrete data having different types of non-normality. Results showed the estimation bias was…
Descriptors: Statistical Bias, Statistical Analysis, Likert Scales, Statistical Distributions
Giada Spaccapanico Proietti; Mariagiulia Matteucci; Stefania Mignani; Bernard P. Veldkamp – Journal of Educational and Behavioral Statistics, 2024
Classical automated test assembly (ATA) methods assume fixed and known coefficients for the constraints and the objective function. This hypothesis is not true for the estimates of item response theory parameters, which are crucial elements in test assembly classical models. To account for uncertainty in ATA, we propose a chance-constrained…
Descriptors: Automation, Computer Assisted Testing, Ambiguity (Context), Item Response Theory