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Abulela, Mohammed A. A.; Rios, Joseph A. – Applied Measurement in Education, 2022
When there are no personal consequences associated with test performance for examinees, rapid guessing (RG) is a concern and can differ between subgroups. To date, the impact of differential RG on item-level measurement invariance has received minimal attention. To that end, a simulation study was conducted to examine the robustness of the…
Descriptors: Comparative Analysis, Robustness (Statistics), Nonparametric Statistics, Item Analysis
Cui, Ying; Gierl, Mark; Guo, Qi – Educational Psychology, 2016
The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…
Descriptors: Cognitive Tests, Diagnostic Tests, Classification, Artificial Intelligence
Park, Jungkyu; Yu, Hsiu-Ting – Educational and Psychological Measurement, 2016
The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…
Descriptors: Hierarchical Linear Modeling, Nonparametric Statistics, Data Analysis, Simulation
Lathrop, Quinn N.; Cheng, Ying – Journal of Educational Measurement, 2014
When cut scores for classifications occur on the total score scale, popular methods for estimating classification accuracy (CA) and classification consistency (CC) require assumptions about a parametric form of the test scores or about a parametric response model, such as item response theory (IRT). This article develops an approach to estimate CA…
Descriptors: Cutting Scores, Classification, Computation, Nonparametric Statistics

Meijer, Rob R.; And Others – Applied Measurement in Education, 1996
Several existing group-based statistics to detect improbable item score patterns are discussed, along with the cut scores proposed in the literature to classify an item score pattern as aberrant. A simulation study and an empirical study are used to compare the statistics and their use and to investigate the practical use of cut scores. (SLD)
Descriptors: Achievement Tests, Classification, Cutting Scores, Identification
Meijer, Rob R. – 1994
In person-fit analysis, the object is to investigate whether an item score pattern is improbable given the item score patterns of the other persons in the group or given what is expected on the basis of a test model. In this study, several existing group-based statistics to detect such improbable score patterns were investigated, along with the…
Descriptors: Achievement Tests, Classification, College Students, Cutting Scores