Given the importance of education and the growing public demand for improving education quality under tight budget constraints, there has been an emerging movement to call for research-informed decisions in educational resource allocation. Despite the abundance of rigorous studies on the effectiveness, cost, and implementation of educational…

Although ownership is acknowledged to exert a potent influence on various aspects of information processing, the origin of these effects remains largely unknown. Based on the demonstration that self-relevance facilitates perceptual judgments (i.e., the self-prioritization effect), here we explored the possibility that ownership enhances object…

Bayesian statistical methods have become more feasible to implement with advances in computing but are not commonly used in educational research. In contrast to frequentist approaches that take hypotheses (and the associated parameters) as fixed, Bayesian methods take data as fixed and hypotheses as random. This difference means that Bayesian…

In learning systems and environment research, intelligent tutoring and personalisation are considered the two most important factors. An Intelligent Tutoring System can serve as an effective tool to improve problem-solving skills by simulating a human tutor's actions in implementing one-to-one adaptive and personalised teaching. Thus, in this…

Bayesian techniques have become increasingly present in the social sciences, fueled by advances in computer speed and the development of user-friendly software. In this paper, we forward the use of Bayesian Asymmetric Regression (BAR) to monitor intervention responsiveness when using Curriculum-Based Measurement (CBM) to assess oral reading…

This article reviews the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on latent variable models, given their growing use in theory testing and construction. Theoretical statistical results in regression are discussed, and more important…

A dominant theme in modeling human perceptual judgments is that sensory neural activity is summed or integrated until a critical bound is reached. Such models predict that, in general, the shape of response time distributions change across conditions, although in practice, this shape change may be subtle. An alternative view is that response time…

Professional psychologists are often confronted with the task of making binary decisions about individuals, such as predictions about future behavior or employee selection. Test users familiar with linear models and Bayes's theorem are likely to assume that the accuracy of decisions is consistently improved by combination of outcomes across valid…

This article reviews the concepts and methods of Bayesian statistical analysis, which can offer innovative and powerful solutions to some challenging analytical problems that characterize developmental research. In this article, we demonstrate the utility of Bayesian analysis, explain its unique adeptness in some circumstances, address some…

In general, candidates selected for employment will probably perform worse than estimated. Bayesian statistical methods may be useful in adjusting the estimates. (Author)

Statistical procedures based on Bayesian estimation for obtaining estimates of a propensity (which would include estimates of proportions or relative frequencies) are described for the special case where the observer can only note whether the propensity exceeds or does not exceed a constant between 0 and 1. (JKS)

The application of the Minnesota Adaptive Instructional System (MAIS) decision procedure by R. D. Tennyson et al. (1975, 1977) is examined. The MAIS is a computer-based adaptive instructional system. The problems of determining the optimal number of interrogatory examples in the MAIS can be formalized as a problem of Bayesian decision making. Two…

Approaches to the determination of cut-scores have used threshold, normal ogive, linear and discrete utility functions. These approaches are examined by investigating conditions on the posterior, likelihood and utility functions required for setting cut-scores in a Bayesian approach. (Author/JKS)

The classification problem in educational testing is a decision problem. One must assign subjects to one of several available treatments on the basis of test scores, where the success of each treatment is measured by a different criterion. Examples of classification decisions include individualized instruction, counseling, and clinical settings.…

A decision-theoretic procedure is outlined which provides a framework within which Bayesian statistical methods can be employed with criterion-referenced tests to improve the quality of decision making in objectives based instructional programs. (Author/DEP)