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Hans Humenberger – Teaching Statistics: An International Journal for Teachers, 2025
In the last years special "ovals" appear increasingly often in diagrams and applets for discussing crucial items of statistical inference (when dealing with confidence intervals for an unknown probability p; approximation of the binomial distribution by the normal distribution; especially in German literature, see e.g. [Meyer,…
Descriptors: Computer Oriented Programs, Prediction, Intervals, Statistical Inference
Carpentras, Dino; Quayle, Michael – International Journal of Social Research Methodology, 2023
Agent-based models (ABMs) often rely on psychometric constructs such as 'opinions', 'stubbornness', 'happiness', etc. The measurement process for these constructs is quite different from the one used in physics as there is no standardized unit of measurement for opinion or happiness. Consequently, measurements are usually affected by 'psychometric…
Descriptors: Psychometrics, Error of Measurement, Models, Prediction
Migliavaca, Celina Borges; Stein, Cinara; Colpani, Verônica; Barker, Timothy Hugh; Ziegelmann, Patricia Klarmann; Munn, Zachary; Falavigna, Maicon – Research Synthesis Methods, 2022
Over the last decade, there has been a 10-fold increase in the number of published systematic reviews of prevalence. In meta-analyses of prevalence, the summary estimate represents an average prevalence from included studies. This estimate is truly informative only if there is no substantial heterogeneity among the different contexts being pooled.…
Descriptors: Incidence, Meta Analysis, Statistics, Statistical Distributions
Kjorte Harra; David Kaplan – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The present work focuses on the performance of two types of shrinkage priors--the horseshoe prior and the recently developed regularized horseshoe prior--in the context of inducing sparsity in path analysis and growth curve models. Prior research has shown that these horseshoe priors induce sparsity by at least as much as the "gold…
Descriptors: Structural Equation Models, Bayesian Statistics, Regression (Statistics), Statistical Inference
Ke-Hai Yuan; Yongfei Fang – Grantee Submission, 2023
Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and…
Descriptors: Structural Equation Models, Regression (Statistics), Weighted Scores, Comparative Analysis
Feinberg, Richard A.; von Davier, Matthias – Journal of Educational and Behavioral Statistics, 2020
The literature showing that subscores fail to add value is vast; yet despite their typical redundancy and the frequent presence of substantial statistical errors, many stakeholders remain convinced of their necessity. This article describes a method for identifying and reporting unexpectedly high or low subscores by comparing each examinee's…
Descriptors: Scores, Probability, Statistical Distributions, Ability
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
Wang, Chia-Chun; Lee, Wen-Chung – Research Synthesis Methods, 2019
A systematic review and meta-analysis is an important step in evidence synthesis. The current paradigm for meta-analyses requires a presentation of the means under a random-effects model; however, a mean with a confidence interval provides an incomplete summary of the underlying heterogeneity in meta-analysis. Prediction intervals show the range…
Descriptors: Meta Analysis, Computation, Statistical Analysis, Prediction
Yongyun Shin; Stephen W. Raudenbush – Grantee Submission, 2023
We consider two-level models where a continuous response R and continuous covariates C are assumed missing at random. Inferences based on maximum likelihood or Bayes are routinely made by estimating their joint normal distribution from observed data R[subscript obs] and C[subscript obs]. However, if the model for R given C includes random…
Descriptors: Maximum Likelihood Statistics, Hierarchical Linear Modeling, Error of Measurement, Statistical Distributions
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
Dorans, Neil J. – ETS Research Report Series, 2018
A distinction is made between scores as measures of a construct and predictions of a criterion or outcome variable. The interpretation attached to predictions of criteria, such as job performance or college grade point average (GPA), differs from that attached to scores that are measures of a construct, such as reading proficiency or knowledge…
Descriptors: Job Performance, Scores, Data Interpretation, Statistical Distributions
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
Boppuru, Prarthap Rudra; K, Ramesha – International Journal of Web-Based Learning and Teaching Technologies, 2019
Social media is the platforms where users communicate, interact, share ideas, career interest, pictures, video, etc. Social media gives an opportunity to analyze the human behavior. Crime analysis using data from social media such as Newsfeeds, Facebook, Twitter, etc., is becoming one of the emerging areas of research for law enforcement…
Descriptors: Social Media, Foreign Countries, Prediction, Law Enforcement
Campitelli, Guillermo; Macbeth, Guillermo; Ospina, Raydonal; Marmolejo-Ramos, Fernando – Educational and Psychological Measurement, 2017
We present three strategies to replace the null hypothesis statistical significance testing approach in psychological research: (1) visual representation of cognitive processes and predictions, (2) visual representation of data distributions and choice of the appropriate distribution for analysis, and (3) model comparison. The three strategies…
Descriptors: Research Methodology, Hypothesis Testing, Psychology, Social Science Research
Burrus, Robert T.; Jones, Adam T.; Schuhmann, Peter W. – Journal of Education for Business, 2016
University students' latent attitudes toward capitalism were quantified and used to predict self-reported cheating behaviors. Results suggest that the relationship between student academic dishonesty and attitudes toward capitalism are complex. Students indicating a strong degree of risk aversion are less likely to report cheating behaviors.…
Descriptors: Student Attitudes, College Students, Social Systems, Prediction