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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
Liu, Yixing; Thompson, Marilyn S. – Journal of Experimental Education, 2022
A simulation study was conducted to explore the impact of differential item functioning (DIF) on general factor difference estimation for bifactor, ordinal data. Common analysis misspecifications in which the generated bifactor data with DIF were fitted using models with equality constraints on noninvariant item parameters were compared under data…
Descriptors: Comparative Analysis, Item Analysis, Sample Size, Error of Measurement
Rutkowski, Leslie – Applied Measurement in Education, 2014
Large-scale assessment programs such as the National Assessment of Educational Progress (NAEP), Trends in International Mathematics and Science Study (TIMSS), and Programme for International Student Assessment (PISA) use a sophisticated assessment administration design called matrix sampling that minimizes the testing burden on individual…
Descriptors: Measurement, Testing, Item Sampling, Computation
Jiao, Hong; Kamata, Akihito; Wang, Shudong; Jin, Ying – Journal of Educational Measurement, 2012
The applications of item response theory (IRT) models assume local item independence and that examinees are independent of each other. When a representative sample for psychometric analysis is selected using a cluster sampling method in a testlet-based assessment, both local item dependence and local person dependence are likely to be induced.…
Descriptors: Item Response Theory, Test Items, Markov Processes, Monte Carlo Methods

Hanges, Paul J.; And Others – Educational and Psychological Measurement, 1991
Whether it is possible to develop a classification function that identifies the underlying range restriction from sample information alone was investigated in a simulation. Results indicate that such a function is possible. The procedure was found to be relatively accurate, robust, and powerful. (SLD)
Descriptors: Classification, Computer Simulation, Equations (Mathematics), Mathematical Models