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Dynamic: An R Package for Deriving Dynamic Fit Index Cutoffs for Factor Analysis
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Melissa G. Wolf; Daniel McNeish – Grantee Submission, 2023
To evaluate the fit of a confirmatory factor analysis model, researchers often rely on fit indices such as SRMR, RMSEA, and CFI. These indices are frequently compared to benchmark values of 0.08, 0.06, and 0.96, respectively, established by Hu and Bentler (1999). However, these indices are affected by model characteristics and their sensitivity to…
Descriptors: Programming Languages, Cutting Scores, Benchmarking, Factor Analysis
Teaching Monte Carlo Simulation with Python
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Holman, Justin O.; Hacherl, Allie – Journal of Statistics and Data Science Education, 2023
It has become increasingly important for future business professionals to understand statistical computing methods as data science has gained widespread use in contemporary organizational decision processes in recent years. Used by scores of academics and practitioners in a variety of fields, Monte Carlo simulation is one of the most broadly…
Descriptors: Teaching Methods, Monte Carlo Methods, Programming Languages, Statistics Education
Bayesian Survival Analysis in STAN for Improved Measuring of Uncertainty in Parameter Estimates
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Kelter, Riko – Measurement: Interdisciplinary Research and Perspectives, 2020
Survival analysis is an important analytic method in the social and medical sciences. Also known under the name time-to-event analysis, this method provides parameter estimation and model fitting commonly conducted via maximum-likelihood. Bayesian survival analysis offers multiple advantages over the frequentist approach for measurement…
Descriptors: Bayesian Statistics, Maximum Likelihood Statistics, Programming Languages, Statistical Inference
Stan: A Probabilistic Programming Language
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Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.; Lee, Daniel; Goodrich, Ben; Betancourt, Michael; Brubaker, Marcus A.; Guo, Jiqiang; Li, Peter; Riddell, Allen – Grantee Submission, 2017
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the…
Descriptors: Programming Languages, Probability, Bayesian Statistics, Monte Carlo Methods
The MATH--Open Source Application for Easier Learning of Numerical Mathematics
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Glaser-Opitz, Henrich; Budajová, Kristina – Acta Didactica Napocensia, 2016
The article introduces a software application (MATH) supporting an education of Applied Mathematics, with focus on Numerical Mathematics. The MATH is an easy to use tool supporting various numerical methods calculations with graphical user interface and integrated plotting tool for graphical representation written in Qt with extensive use of Qwt…
Descriptors: Mathematics Education, Computer Software, Computer Assisted Instruction, College Mathematics
Numerical Problems and Agent-Based Models for a Mass Transfer Course
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Murthi, Manohar; Shea, Lonnie D.; Snurr, Randall Q. – Chemical Engineering Education, 2009
Problems requiring numerical solutions of differential equations or the use of agent-based modeling are presented for use in a course on mass transfer. These problems were solved using the popular technical computing language MATLABTM. Students were introduced to MATLAB via a problem with an analytical solution. A more complex problem to which no…
Descriptors: Scientific Concepts, Chemical Engineering, Engineering Education, Calculus