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Tihomir Asparouhov; Bengt Muthén – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Penalized structural equation models (PSEM) is a new powerful estimation technique that can be used to tackle a variety of difficult structural estimation problems that can not be handled with previously developed methods. In this paper we describe the PSEM framework and illustrate the quality of the method with simulation studies.…
Descriptors: Structural Equation Models, Computation, Factor Analysis, Measurement Techniques
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Brendan A. Schuetze – Educational Psychology Review, 2024
The computational model of school achievement represents a novel approach to theorizing school achievement, conceptualizing educational interventions as modifications to students' learning curves. By modeling the process and products of educational achievement simultaneously, this tool addresses several unresolved questions in educational…
Descriptors: Computation, Growth Models, Academic Achievement, Student Evaluation
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Monroe, Scott; Cai, Li – Educational Measurement: Issues and Practice, 2015
Student growth percentiles (SGPs, Betebenner, 2009) are used to locate a student's current score in a conditional distribution based on the student's past scores. Currently, following Betebenner (2009), quantile regression (QR) is most often used operationally to estimate the SGPs. Alternatively, multidimensional item response theory (MIRT) may…
Descriptors: Item Response Theory, Reliability, Growth Models, Computation
Monroe, Scott; Cai, Li – Grantee Submission, 2015
Student Growth Percentiles (SGP, Betebenner, 2009) are used to locate a student's current score in a conditional distribution based on the student's past scores. Currently, following Betebenner (2009), quantile regression is most often used operationally to estimate the SGPs. Alternatively, multidimensional item response theory (MIRT) may also be…
Descriptors: Item Response Theory, Reliability, Growth Models, Computation
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McNeish, Daniel; Harring, Jeffrey R. – Educational and Psychological Measurement, 2017
To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor…
Descriptors: Growth Models, Goodness of Fit, Error Correction, Sampling