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Liu, Jin; Perera, Robert A.; Kang, Le; Sabo, Roy T.; Kirkpatrick, Robert M. – Journal of Educational and Behavioral Statistics, 2022
This study proposes transformation functions and matrices between coefficients in the original and reparameterized parameter spaces for an existing linear-linear piecewise model to derive the interpretable coefficients directly related to the underlying change pattern. Additionally, the study extends the existing model to allow individual…
Descriptors: Longitudinal Studies, Statistical Analysis, Matrices, Mathematics
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Tueller, Stephen J.; Drotar, Scott; Lubke, Gitta H. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
The discrimination between alternative models and the detection of latent classes in the context of latent variable mixture modeling depends on sample size, class separation, and other aspects that are related to power. Prior to a mixture analysis it is useful to investigate model performance in a simulation study that reflects the research…
Descriptors: Simulation, Structural Equation Models, Statistical Analysis, Mathematics
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Haberman, Shelby J. – ETS Research Report Series, 2013
A general program for item-response analysis is described that uses the stabilized Newton-Raphson algorithm. This program is written to be compliant with Fortran 2003 standards and is sufficiently general to handle independent variables, multidimensional ability parameters, and matrix sampling. The ability variables may be either polytomous or…
Descriptors: Predictor Variables, Mathematics, Item Response Theory, Probability
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DeCarlo, Lawrence T. – Applied Psychological Measurement, 2011
Cognitive diagnostic models (CDMs) attempt to uncover latent skills or attributes that examinees must possess in order to answer test items correctly. The DINA (deterministic input, noisy "and") model is a popular CDM that has been widely used. It is shown here that a logistic version of the model can easily be fit with standard software for…
Descriptors: Bayesian Statistics, Computation, Cognitive Tests, Diagnostic Tests
Statistical Reporting Service (USDA), Washington, DC. – 1974
Intended as a reference for the convenience of students in sampling, this monograph attempts to express relevant, introductory mathematics and probability in the context of sample surveys. Although some proofs are presented, the emphasis is more on exposition of mathematical language and concepts than on the mathematics per se and rigorous proofs.…
Descriptors: Agriculture, Algebra, Classification, Data Collection
Scheetz, James P.; Forsyth, Robert A. – 1977
Empirical evidence is presented related to the effects of using a stratified sampling of items in multiple matrix sampling on the accuracy of estimates of the population mean. Data were obtained from a sample of 600 high school students for a 36-item mathematics test and a 40-item vocabulary test, both subtests of the Iowa Tests of Educational…
Descriptors: Achievement Tests, Difficulty Level, Item Analysis, Item Sampling
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection