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Per Nilsson; Andreas Eckert – Mathematical Thinking and Learning: An International Journal, 2024
This study contributes to the call for influencing practice by increasing attention to how learning environments can be designed to support learning in statistical inference. We report on a design experiment in secondary school (students 14-16 years old), that resulted in a set of lessons with the learning goal of teaching students how to apply…
Descriptors: Mathematics Instruction, Teaching Methods, Hypothesis Testing, Secondary School Students
Ernesto Sánchez; Victor Nozair García-Ríos; Francisco Sepúlveda – Educational Studies in Mathematics, 2024
Sampling distributions are fundamental for statistical inference, yet their abstract nature poses challenges for students. This research investigates the development of high school students' conceptions of sampling distribution through informal significance tests with the aid of digital technology. The study focuses on how technological tools…
Descriptors: High School Students, Concept Formation, Thinking Skills, Skill Development
Duxbury, Scott W. – Sociological Methods & Research, 2023
This study shows that residual variation can cause problems related to scaling in exponential random graph models (ERGM). Residual variation is likely to exist when there are unmeasured variables in a model--even those uncorrelated with other predictors--or when the logistic form of the model is inappropriate. As a consequence, coefficients cannot…
Descriptors: Graphs, Scaling, Research Problems, Models
Calzada, Maria E.; Gardner, Holly – Mathematics and Computer Education, 2011
The results of a simulation conducted by a research team involving undergraduate and high school students indicate that when data is symmetric the student's "t" confidence interval for a mean is superior to the studied non-parametric bootstrap confidence intervals. When data is skewed and for sample sizes n greater than or equal to 10,…
Descriptors: Intervals, Effect Size, Simulation, Undergraduate Students
Zhang, Zhiyong; Lai, Keke; Lu, Zhenqiu; Tong, Xin – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Despite the widespread popularity of growth curve analysis, few studies have investigated robust growth curve models. In this article, the "t" distribution is applied to model heavy-tailed data and contaminated normal data with outliers for growth curve analysis. The derived robust growth curve models are estimated through Bayesian…
Descriptors: Structural Equation Models, Bayesian Statistics, Statistical Inference, Statistical Distributions
Mulekar, Madhuri S.; Siegel, Murray H. – Mathematics Teacher, 2009
If students are to understand inferential statistics successfully, they must have a profound understanding of the nature of the sampling distribution. Specifically, they must comprehend the determination of the expected value and standard error of a sampling distribution as well as the meaning of the central limit theorem. Many students in a high…
Descriptors: Statistical Inference, Statistics, Sample Size, Error of Measurement