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Ushangi Goginava; Humberto Rafeiro – International Journal of Mathematical Education in Science and Technology, 2025
We demonstrate the Stirling formula, approximating the factorial, utilising accessible and elementary methods in an engaging manner.
Descriptors: Mathematical Formulas, Mathematical Logic, Computation
Francis Huang; Brian Keller – Large-scale Assessments in Education, 2025
Missing data are common with large scale assessments (LSAs). A typical approach to handling missing data with LSAs is the use of listwise deletion, despite decades of research showing that approach can be a suboptimal strategy resulting in biased estimates. In order to help researchers account for missing data, we provide a tutorial using R and…
Descriptors: Research Problems, Data Analysis, Statistical Bias, International Assessment
V. I. Romanenko; A. V. Romanenko – International Journal of Mathematical Education in Science and Technology, 2025
We present a method to compute the power series expansions of e[superscript x] ln (1 + x), sin x, and cos x without relying on mathematical analysis. Using the properties of elementary functions, we determine the coefficients of each series through the method of undetermined coefficients. We have validated our formulae through the use of…
Descriptors: Mathematics, Computation, Mathematical Formulas, Numbers
Paul T. von Hippel – Educational Evaluation and Policy Analysis, 2025
Educational researchers often report effect sizes in standard deviation units (SD), but SD effects are hard to interpret. Effects are easier to interpret in percentile points, but converting SDs to percentile points involves a calculation that is not transparent to educational stakeholders. We show that if the outcome variable is normally…
Descriptors: Effect Size, Computation, Mathematical Concepts, Statistical Distributions
David Voas; Laura Watt – Teaching Statistics: An International Journal for Teachers, 2025
Binary logistic regression is one of the most widely used statistical tools. The method uses odds, log odds, and odds ratios, which are difficult to understand and interpret. Understanding of logistic regression tends to fall down in one of three ways: (1) Many students and researchers come to believe that an odds ratio translates directly into…
Descriptors: Statistics, Statistics Education, Regression (Statistics), Misconceptions
Wörner, C. H. – Physics Teacher, 2023
Bounded by the statements of Feynman and Galileo, I describe certain tricks that can be useful for the teaching of physics. In particular, I describe the calculation of the center of mass (centroid) of an arc of circumference and a circular sector. For this purpose, I also use Pappus's theorems. An Appendix is available with Archimedes' method to…
Descriptors: Physics, Computation, Scientific Concepts, Science Instruction
López-Barrientos, José Daniel; Silva, Eliud; Lemus-Rodríguez, Enrique – Teaching Statistics: An International Journal for Teachers, 2023
We take advantage of a combinatorial misconception and the famous paradox of the Chevalier de Méré to present the multiplication rule for independent events; the principle of inclusion and exclusion in the presence of disjoint events; the median of a discrete-type random variable, and a confidence interval for a large sample. Moreover, we pay…
Descriptors: Statistics Education, Mathematical Concepts, Multiplication, Misconceptions
Kozinc, Žiga; Pleša, Jernej – Measurement in Physical Education and Exercise Science, 2023
Vertical jump height is a widely used variable in sport science and physical education. This narrative review summarizes various numerical methods used to calculate vertical jump height. In force plate technology, the jump height is most commonly calculated using the flight time (FT) or the takeoff velocity (TOV), which provide only the distance…
Descriptors: Physical Activities, Scientific Concepts, Measurement Techniques, Computation
Piantadosi, Steven T. – Child Development, 2023
The study of how children learn numbers has yielded one of the most productive research programs in cognitive development, spanning empirical and computational methods, as well as nativist and empiricist philosophies. This paper provides a tutorial on how to think computationally about learning models in a domain like number, where learners take…
Descriptors: Cognitive Development, Child Development, Computation, Models
John W. Keller; Arianna L. Demmerly – Journal of Chemical Education, 2025
This computational exercise gives students an in-depth look at the reactants, transition state, and product of the crystal violet alkaline hydrolysis reaction. The exercise, which is aimed at second semester general chemistry students, involves DFT energy calculations on five structures: crystal violet cation, crystal violet alcohol, and 4-water…
Descriptors: Chemistry, Science Education, Computation, Scientific Concepts
Michael C. Robbins; Zhuping Li – Field Methods, 2025
The Nolan Index (NI) is a normed, quantitative measure for comparing the degree of resemblance (similarity or dissimilarity) between free listings with an Excel program for calculating it. This article enhances that effort with the addition of an R program and additional applications. Free-list resemblance measures have been used to investigate…
Descriptors: Computation, Norm Referenced Tests, Comparative Analysis, Spreadsheets
Radek Pelánek – ACM Transactions on Computing Education, 2025
Learning environments for programming education need a comprehensive task set that guides students from basic programming concepts to complex challenges. For creating such a task set, it is beneficial to utilize the concept of a design space--a systematic mapping of design dimensions and choices along these dimensions. We propose an iterative…
Descriptors: Computer Science Education, Programming, Design, Task Analysis
Sarah Narvaiz; Qinyun Lin; Joshua M. Rosenberg; Kenneth A. Frank; Spiro J. Maroulis; Wei Wang; Ran Xu – Grantee Submission, 2024
Sensitivity analysis, a statistical method crucial for validating inferences across disciplines, quantifies the conditions that could alter conclusions (Razavi et al., 2021). One line of work is rooted in linear models and foregrounds the sensitivity of inferences to the strength of omitted variables (Cinelli & Hazlett, 2019; Frank, 2000). A…
Descriptors: Statistical Analysis, Computer Software, Robustness (Statistics), Statistical Inference
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
Cairns, Maxwell; Prendergast, Luke A. – Research Synthesis Methods, 2022
As a measure of heterogeneity in meta-analysis, the coefficient of variation (CV) has been recently considered, providing researchers with a complement to the very popular I[superscript 2] measure. While I[superscript 2] measures the proportion of total variance that is due to variance of the random effects, the CV is the ratio of the standard…
Descriptors: Meta Analysis, Statistical Analysis, Intervals, Computation