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Francesco Poli; Marlene Meyer; Rogier B. Mars; Sabine Hunnius – Child Development, 2025
Humans are driven by an intrinsic motivation to learn, but the developmental origins of curiosity-driven exploration remain unclear. We investigated the computational principles guiding 4-year-old children's exploration during a touchscreen game (N = 102, F = 49, M = 53, primarily white and middle-class, data collected in the Netherlands from…
Descriptors: Foreign Countries, Young Children, Learning Motivation, Discovery Learning
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Jorge N. Tendeiro; Rink Hoekstra; Tsz Keung Wong; Henk A. L. Kiers – Teaching Statistics: An International Journal for Teachers, 2025
Most researchers receive formal training in frequentist statistics during their undergraduate studies. In particular, hypothesis testing is usually rooted on the null hypothesis significance testing paradigm and its p-value. Null hypothesis Bayesian testing and its so-called Bayes factor are now becoming increasingly popular. Although the Bayes…
Descriptors: Statistics Education, Teaching Methods, Programming Languages, Bayesian Statistics
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Jeff Coon; Paulina N. Silva; Alexander Etz; Barbara W. Sarnecka – Journal of Cognition and Development, 2025
Bayesian methods offer many advantages when applied to psychological research, yet they may seem esoteric to researchers who are accustomed to traditional methods. This paper aims to lower the barrier of entry for developmental psychologists who are interested in using Bayesian methods. We provide worked examples of how to analyze common study…
Descriptors: Developmental Psychology, Bayesian Statistics, Research Methodology, Psychological Studies
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Jean-Paul Fox – Journal of Educational and Behavioral Statistics, 2025
Popular item response theory (IRT) models are considered complex, mainly due to the inclusion of a random factor variable (latent variable). The random factor variable represents the incidental parameter problem since the number of parameters increases when including data of new persons. Therefore, IRT models require a specific estimation method…
Descriptors: Sample Size, Item Response Theory, Accuracy, Bayesian Statistics
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John Ermisch – Sociological Methods & Research, 2025
Empirical analysis of variation in demographic events within the population is facilitated by using longitudinal survey data because of the richness of covariate measures in such data, but there is wave-on-wave dropout. When attrition is related to the event, it precludes consistent estimation of the impacts of covariates on the event and on event…
Descriptors: Attrition (Research Studies), Longitudinal Studies, Surveys, Statistical Analysis
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Edoardo Costantini; Kyle M. Lang; Tim Reeskens; Klaas Sijtsma – Sociological Methods & Research, 2025
Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant…
Descriptors: Statistical Analysis, Social Science Research, Predictor Variables, Sociology
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Qi, Hongchao; Rizopoulos, Dimitris; Rosmalen, Joost – Research Synthesis Methods, 2022
The meta-analytic-predictive (MAP) approach is a Bayesian meta-analytic method to synthesize and incorporate information from historical controls in the analysis of a new trial. Classically, only a single parameter, typically the intercept or rate, is assumed to vary across studies, which may not be realistic in more complex models. Analysis of…
Descriptors: Meta Analysis, Prediction, Correlation, Bayesian Statistics
McCluskey, Sydne – ProQuest LLC, 2023
Rater comparison analysis is commonly necessary in the social sciences. Conventional approaches to the problem generally focus on calculation of agreement statistics, which provide useful but incomplete information about rater agreement. Importantly, one-number agreement statistics give no indication regarding the nature of disagreements, nor do…
Descriptors: Bayesian Statistics, Structural Equation Models, Interrater Reliability, Beliefs
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Bartoš, František; Maier, Maximilian; Wagenmakers, Eric-Jan; Doucouliagos, Hristos; Stanley, T. D. – Research Synthesis Methods, 2023
Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods' performance to depend on the true data generating process, and no…
Descriptors: Robustness (Statistics), Bayesian Statistics, Meta Analysis, Publications
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Chen, Yinghan; Wang, Shiyu – Journal of Educational and Behavioral Statistics, 2023
Attribute hierarchy, the underlying prerequisite relationship among attributes, plays an important role in applying cognitive diagnosis models (CDM) for designing efficient cognitive diagnostic assessments. However, there are limited statistical tools to directly estimate attribute hierarchy from response data. In this study, we proposed a…
Descriptors: Cognitive Measurement, Models, Bayesian Statistics, Computation
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Milica Miocevic; Fayette Klaassen; Mariola Moeyaert; Gemma G. M. Geuke – Journal of Experimental Education, 2025
Mediation analysis in Single Case Experimental Designs (SCEDs) evaluates intervention mechanisms for individuals. Despite recent methodological developments, no clear guidelines exist for maximizing power to detect the indirect effect in SCEDs. This study compares frequentist and Bayesian methods, determining (1) minimum required sample size to…
Descriptors: Research Design, Mediation Theory, Statistical Analysis, Simulation
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Dongho Shin; Yongyun Shin; Nao Hagiwara – Grantee Submission, 2025
We consider Bayesian estimation of a hierarchical linear model (HLM) from partially observed data, assumed to be missing at random, and small sample sizes. A vector of continuous covariates C includes cluster-level partially observed covariates with interaction effects. Due to small sample sizes from 37 patient-physician encounters repeatedly…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, Multivariate Analysis, Data Analysis
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Adrian Quintero; Emmanuel Lesaffre; Geert Verbeke – Journal of Educational and Behavioral Statistics, 2024
Bayesian methods to infer model dimensionality in factor analysis generally assume a lower triangular structure for the factor loadings matrix. Consequently, the ordering of the outcomes influences the results. Therefore, we propose a method to infer model dimensionality without imposing any prior restriction on the loadings matrix. Our approach…
Descriptors: Bayesian Statistics, Factor Analysis, Factor Structure, Sampling
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Rosa W. Runhardt – Sociological Methods & Research, 2024
This article uses the interventionist theory of causation, a counterfactual theory taken from philosophy of science, to strengthen causal analysis in process tracing research. Causal claims from process tracing are re-expressed in terms of so-called hypothetical interventions, and concrete evidential tests are proposed which are shown to…
Descriptors: Causal Models, Statistical Inference, Intervention, Investigations
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Suzanne C. Freeman; Alex J. Sutton; Nicola J. Cooper; Alessandro Gasparini; Michael J. Crowther; Neil Hawkins – Research Synthesis Methods, 2024
Background: Traditionally, meta-analysis of time-to-event outcomes reports a single pooled hazard ratio assuming proportional hazards (PH). For health technology assessment evaluations, hazard ratios are frequently extrapolated across a lifetime horizon. However, when treatment effects vary over time, an assumption of PH is not always valid. The…
Descriptors: Cancer, Medical Research, Bayesian Statistics, Meta Analysis
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