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Showing 1 to 15 of 21 results Save | Export
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Mark W. Isken – INFORMS Transactions on Education, 2025
A staple of many spreadsheet-based management science courses is the use of Excel for activities such as model building, sensitivity analysis, goal seeking, and Monte-Carlo simulation. What might those things look like if carried out using Python? We describe a teaching module in which Python is used to do typical Excel-based modeling and…
Descriptors: Spreadsheets, Models, Programming Languages, Monte Carlo Methods
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Yigiter, Mahmut Sami; Dogan, Nuri – Measurement: Interdisciplinary Research and Perspectives, 2023
In recent years, Computerized Multistage Testing (MST), with their versatile benefits, have found themselves a wide application in large scale assessments and have increased their popularity. The fact that forms can be made ready before the exam application, such as a linear test, and that they can be adapted according to the test taker's ability…
Descriptors: Programming Languages, Monte Carlo Methods, Computer Assisted Testing, Test Format
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Kilic, Abdullah Faruk; Uysal, Ibrahim – International Journal of Assessment Tools in Education, 2022
Most researchers investigate the corrected item-total correlation of items when analyzing item discrimination in multi-dimensional structures under the Classical Test Theory, which might lead to underestimating item discrimination, thereby removing items from the test. Researchers might investigate the corrected item-total correlation with the…
Descriptors: Item Analysis, Correlation, Item Response Theory, Test Items
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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
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Erdem-Kara, Basak – International Journal of Assessment Tools in Education, 2019
Computer adaptive testing is an important research field in educational measurement, and simulation studies play a critically important role in CAT development and evaluation. Both Monte Carlo and Post Hoc simulations are frequently used in CAT studies in order to investigate the effects of different factors on test efficiency and to compare…
Descriptors: Computer Assisted Testing, Adaptive Testing, Programming Languages, Monte Carlo Methods
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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
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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
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Albert, Jim; Hu, Jingchen – Journal of Statistics Education, 2020
Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more accessible to applied statisticians and, in turn, have potentially transformed Bayesian education at the undergraduate level. This article provides an…
Descriptors: Bayesian Statistics, Computation, Statistics Education, Undergraduate Students
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Seebut, Supot; Wongsason, Patcharee; Kim, Dojin; Putjuso, Thanin; Boonpok, Chawalit – EURASIA Journal of Mathematics, Science and Technology Education, 2022
Simulation modeling is an effective tool for solving problems that cannot be explained analytically or when data cannot be collected. This is done by simulating the observed behavior of a problem under study using a computer program. In math education, this can develop knowledge and fundamental competencies of simulation modeling at a higher level…
Descriptors: Programming Languages, Mathematics Instruction, Grade 12, Secondary School Students
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Lockwood, J. R.; Castellano, Katherine E.; McCaffrey, Daniel F. – Journal of Educational and Behavioral Statistics, 2022
Many states and school districts in the United States use standardized test scores to compute annual measures of student achievement progress and then use school-level averages of these growth measures for various reporting and diagnostic purposes. These aggregate growth measures can vary consequentially from year to year for the same school,…
Descriptors: Accuracy, Prediction, Programming Languages, Standardized Tests
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Hu, Jingchen – Journal of Statistics Education, 2020
We propose a semester-long Bayesian statistics course for undergraduate students with calculus and probability background. We cultivate students' Bayesian thinking with Bayesian methods applied to real data problems. We leverage modern Bayesian computing techniques not only for implementing Bayesian methods, but also to deepen students'…
Descriptors: Bayesian Statistics, Statistics Education, Undergraduate Students, Computation
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Ames, Allison J.; Au, Chi Hang – Measurement: Interdisciplinary Research and Perspectives, 2018
Stan is a flexible probabilistic programming language providing full Bayesian inference through Hamiltonian Monte Carlo algorithms. The benefits of Hamiltonian Monte Carlo include improved efficiency and faster inference, when compared to other MCMC software implementations. Users can interface with Stan through a variety of computing…
Descriptors: Item Response Theory, Computer Software Evaluation, Computer Software, Programming Languages
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
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Silva, R. M.; Guan, Y.; Swartz, T. B. – Journal on Efficiency and Responsibility in Education and Science, 2017
This paper attempts to bridge the gap between classical test theory and item response theory. It is demonstrated that the familiar and popular statistics used in classical test theory can be translated into a Bayesian framework where all of the advantages of the Bayesian paradigm can be realized. In particular, prior opinion can be introduced and…
Descriptors: Item Response Theory, Bayesian Statistics, Test Construction, Markov Processes
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Sanou Gozalo, Eduard; Hernández-Fernández, Antoni; Arias, Marta; Ferrer-i-Cancho, Ramon – Journal of Technology and Science Education, 2017
In a course of the degree of computer science, the programming project has changed from individual to teamed work, tentatively in couples (pair programming). Students have full freedom to team up with minimum intervention from teachers. The analysis of the working groups made indicates that students do not tend to associate with students with a…
Descriptors: Group Activities, Group Dynamics, Computer Science, Programming
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