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Haiyan Liu; Wen Qu; Zhiyong Zhang; Hao Wu – Grantee Submission, 2022
Bayesian inference for structural equation models (SEMs) is increasingly popular in social and psychological sciences owing to its flexibility to adapt to more complex models and the ability to include prior information if available. However, there are two major hurdles in using the traditional Bayesian SEM in practice: (1) the information nested…
Descriptors: Bayesian Statistics, Structural Equation Models, Statistical Inference, Statistical Distributions
Huang, Hening – Research Synthesis Methods, 2023
Many statistical methods (estimators) are available for estimating the consensus value (or average effect) and heterogeneity variance in interlaboratory studies or meta-analyses. These estimators are all valid because they are developed from or supported by certain statistical principles. However, no estimator can be perfect and must have error or…
Descriptors: Statistical Analysis, Computation, Measurement Techniques, Meta Analysis
Erik-Jan van Kesteren; Daniel L. Oberski – Structural Equation Modeling: A Multidisciplinary Journal, 2022
Structural equation modeling (SEM) is being applied to ever more complex data types and questions, often requiring extensions such as regularization or novel fitting functions. To extend SEM, researchers currently need to completely reformulate SEM and its optimization algorithm -- a challenging and time-consuming task. In this paper, we introduce…
Descriptors: Structural Equation Models, Computation, Graphs, Algorithms
Gilraine, Michael; Gu, Jiaying; McMillan, Robert – National Bureau of Economic Research, 2020
This paper proposes a new methodology for estimating teacher value-added. Rather than imposing a normality assumption on unobserved teacher quality (as in the standard empirical Bayes approach), our nonparametric estimator permits the underlying distribution to be estimated directly and in a computationally feasible way. The resulting estimates…
Descriptors: Value Added Models, Teacher Effectiveness, Nonparametric Statistics, Computation
van der Linden, Wim J.; Ren, Hao – Journal of Educational and Behavioral Statistics, 2020
The Bayesian way of accounting for the effects of error in the ability and item parameters in adaptive testing is through the joint posterior distribution of all parameters. An optimized Markov chain Monte Carlo algorithm for adaptive testing is presented, which samples this distribution in real time to score the examinee's ability and optimally…
Descriptors: Bayesian Statistics, Adaptive Testing, Error of Measurement, Markov Processes
Wilcox, Rand R.; Serang, Sarfaraz – Educational and Psychological Measurement, 2017
The article provides perspectives on p values, null hypothesis testing, and alternative techniques in light of modern robust statistical methods. Null hypothesis testing and "p" values can provide useful information provided they are interpreted in a sound manner, which includes taking into account insights and advances that have…
Descriptors: Hypothesis Testing, Bayesian Statistics, Computation, Effect Size
Blokpoel, Mark; Wareham, Todd; Haselager, Pim; Toni, Ivan; van Rooij, Iris – Journal of Problem Solving, 2018
The ability to generate novel hypotheses is an important problem-solving capacity of humans. This ability is vital for making sense of the complex and unfamiliar world we live in. Often, this capacity is characterized as an inference to the best explanation--selecting the "best" explanation from a given set of candidate hypotheses.…
Descriptors: Hypothesis Testing, Logical Thinking, Inferences, Computation
Satake, Eiki; Vashlishan Murray, Amy – Teaching Statistics: An International Journal for Teachers, 2015
This paper presents a comparison of three approaches to the teaching of probability to demonstrate how the truth table of elementary mathematical logic can be used to teach the calculations of conditional probabilities. Students are typically introduced to the topic of conditional probabilities--especially the ones that involve Bayes' rule--with…
Descriptors: Teaching Methods, Probability, Bayesian Statistics, Mathematical Logic
García-Pérez, Miguel A. – Educational and Psychological Measurement, 2017
Null hypothesis significance testing (NHST) has been the subject of debate for decades and alternative approaches to data analysis have been proposed. This article addresses this debate from the perspective of scientific inquiry and inference. Inference is an inverse problem and application of statistical methods cannot reveal whether effects…
Descriptors: Hypothesis Testing, Statistical Inference, Effect Size, Bayesian Statistics
Pan, Yilin – Society for Research on Educational Effectiveness, 2016
Given the necessity to bridge the gap between what happened and what is likely to happen, this paper aims to explore how to apply Bayesian inference to cost-effectiveness analysis so as to capture the uncertainty of a ratio-type efficiency measure. The first part of the paper summarizes the characteristics of the evaluation data that are commonly…
Descriptors: Resource Allocation, Cost Effectiveness, Bayesian Statistics, Statistical Analysis
Guarino, Cassandra M.; Maxfield, Michelle; Reckase, Mark D.; Thompson, Paul N.; Wooldridge, Jeffrey M. – Journal of Educational and Behavioral Statistics, 2015
Empirical Bayes's (EB) estimation has become a popular procedure used to calculate teacher value added, often as a way to make imprecise estimates more reliable. In this article, we review the theory of EB estimation and use simulated and real student achievement data to study the ability of EB estimators to properly rank teachers. We compare the…
Descriptors: Bayesian Statistics, Computation, Teacher Evaluation, Teacher Effectiveness
McClelland, James L.; Mirman, Daniel; Bolger, Donald J.; Khaitan, Pranav – Cognitive Science, 2014
In a seminal 1977 article, Rumelhart argued that perception required the simultaneous use of multiple sources of information, allowing perceivers to optimally interpret sensory information at many levels of representation in real time as information arrives. Building on Rumelhart's arguments, we present the Interactive Activation…
Descriptors: Perception, Comprehension, Cognitive Processes, Alphabets
Chung, Yeojin; Rabe-Hesketh, Sophia; Gelman, Andrew; Dorie, Vincent; Liu, Jinchen – Society for Research on Educational Effectiveness, 2012
Hierarchical or multilevel linear models are widely used for longitudinal or cross-sectional data on students nested in classes and schools, and are particularly important for estimating treatment effects in cluster-randomized trials, multi-site trials, and meta-analyses. The models can allow for variation in treatment effects, as well as…
Descriptors: Statistical Analysis, Models, Computation, Maximum Likelihood Statistics
Edwards, Michael C. – Measurement: Interdisciplinary Research and Perspectives, 2013
This author has had the privilege of knowing Professor Maydeu-Olivares for almost a decade and although their paths cross only occasionally, such instances were always enjoyable and enlightening. Edwards states that Maydeu-Olivares' target article for this issue, ("Goodness-of-Fit Assessment of Item Response Theory Models") provides…
Descriptors: Goodness of Fit, Item Response Theory, Models, Factor Analysis
Miller, Jeff; Schwarz, Wolf – Psychological Methods, 2011
We study a model of the research process in which the true effect size, the replication jitter due to changes in experimental procedure, and the statistical error of effect size measurement are all normally distributed random variables. Within this model, we analyze the probability of successfully replicating an initial experimental result by…
Descriptors: Models, Research, Effect Size, Probability