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Showing 1 to 15 of 33 results Save | Export
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Roderick J. Little; James R. Carpenter; Katherine J. Lee – Sociological Methods & Research, 2024
Missing data are a pervasive problem in data analysis. Three common methods for addressing the problem are (a) complete-case analysis, where only units that are complete on the variables in an analysis are included; (b) weighting, where the complete cases are weighted by the inverse of an estimate of the probability of being complete; and (c)…
Descriptors: Foreign Countries, Probability, Robustness (Statistics), Responses
Yao, Yuling; Vehtari, Aki; Gelman, Andrew – Grantee Submission, 2022
When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty. And, even if the most important modes can be found, it is difficult to evaluate their relative weights in…
Descriptors: Bayesian Statistics, Computation, Markov Processes, Monte Carlo Methods
David Kaplan; Kjorte Harra – OECD Publishing, 2023
This report aims to showcase the value of implementing a Bayesian framework to analyse and report results from international large-scale surveys and provide guidance to users who want to analyse the data using this approach. The motivation for this report stems from the recognition that Bayesian statistical inference is fast becoming a popular…
Descriptors: Bayesian Statistics, Statistical Inference, Data Analysis, Educational Research
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Fielding, Jill; Makar, Katie – Instructional Science: An International Journal of the Learning Sciences, 2022
Conceptual challenge is often considered a necessary ingredient for promoting deep learning in an inquiry-based environment. However, challenge alone does not support conceptual development. In this paper, we draw on complexity theory as a theoretical lens to explore how a primary teacher facilitated students' conceptual change through repeated…
Descriptors: Elementary School Students, Statistics Education, Mathematics Education, Elementary School Mathematics
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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
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Makar, Katie; Allmond, Sue – ZDM: The International Journal on Mathematics Education, 2018
Children have limited exposure to statistical concepts and processes, yet researchers have highlighted multiple benefits of experiences in which they design and/or engage informally with statistical modelling. A study was conducted with a classroom in which students developed and utilised data-based models to respond to the inquiry question,…
Descriptors: Statistics, Mathematical Models, Prediction, Statistical Distributions
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Sánchez Sánchez, Ernesto; García Rios, Víctor N.; Silvestre Castro, Eleazar; Licea, Guadalupe Carrasco – North American Chapter of the International Group for the Psychology of Mathematics Education, 2020
In this paper, we address the following questions: What misconceptions do high school students exhibit in their first encounter with significance test problems through a repeated sampling approach? Which theory or framework could explain the presence and features of such patterns? With brief prior instruction on the use of Fathom software to…
Descriptors: High School Students, Misconceptions, Statistical Significance, Testing
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Cai, Tianji; Xia, Yiwei; Zhou, Yisu – Sociological Methods & Research, 2021
Analysts of discrete data often face the challenge of managing the tendency of inflation on certain values. When treated improperly, such phenomenon may lead to biased estimates and incorrect inferences. This study extends the existing literature on single-value inflated models and develops a general framework to handle variables with more than…
Descriptors: Statistical Distributions, Probability, Statistical Analysis, Statistical Bias
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Ranger, Jochen; Kuhn, Jörg Tobias; Ortner, Tuulia M. – Educational and Psychological Measurement, 2020
The hierarchical model of van der Linden is the most popular model for responses and response times in tests. It is composed of two separate submodels--one for the responses and one for the response times--that are joined at a higher level. The submodel for the response times is based on the lognormal distribution. The lognormal distribution is a…
Descriptors: Reaction Time, Tests, Statistical Distributions, Models
Dorie, Vincent; Hill, Jennifer; Shalit, Uri; Scott, Marc; Cervone, Daniel – Grantee Submission, 2018
Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend.…
Descriptors: Statistical Inference, Simulation, Causal Models, Research Methodology
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Fergusson, Anna; Pfannkuch, Maxine – Journal of Statistics Education, 2020
Informally testing the fit of a probability distribution model is educationally a desirable precursor to formal methods for senior secondary school students. Limited research on how to teach such an informal approach, lack of statistically sound criteria to enable drawing of conclusions, as well as New Zealand assessment requirements led to this…
Descriptors: Foreign Countries, Statistics Education, Probability, Goodness of Fit
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van Dijke-Droogers, Marianne; Drijvers, Paul; Bakker, Arthur – Mathematical Thinking and Learning: An International Journal, 2020
While various studies suggest that informal statistical inference (ISI) can be developed by young students, more research is needed to translate this claim into a well-founded learning trajectory (LT). As a contribution, this paper presents the results of a cycle of design research that focuses on the design, implementation, and evaluation of the…
Descriptors: Statistical Inference, Grade 9, Sampling, Statistical Distributions
Setyani, Geovani Debby; Kristanto, Yosep Dwi – Online Submission, 2020
Drawing inference from data is an important skill for students to understand their everyday life, so that the sampling distribution as a central topic in statistical inference is necessary to be learned by the students. However, little is known about how to teach the topic for high school students, especially in Indonesian context. Therefore, the…
Descriptors: High School Students, Grade 11, Private Schools, Foreign Countries
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Ames, Allison J. – Measurement: Interdisciplinary Research and Perspectives, 2018
Bayesian item response theory (IRT) modeling stages include (a) specifying the IRT likelihood model, (b) specifying the parameter prior distributions, (c) obtaining the posterior distribution, and (d) making appropriate inferences. The latter stage, and the focus of this research, includes model criticism. Choice of priors with the posterior…
Descriptors: Bayesian Statistics, Item Response Theory, Statistical Inference, Prediction
Ding, Peng; Dasgupta, Tirthankar – Grantee Submission, 2017
Fisher randomization tests for Neyman's null hypothesis of no average treatment effects are considered in a finite population setting associated with completely randomized experiments with more than two treatments. The consequences of using the F statistic to conduct such a test are examined both theoretically and computationally, and it is argued…
Descriptors: Statistical Analysis, Statistical Inference, Causal Models, Error Patterns
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