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Games, Paul A. – Journal of Experimental Education, 1988
A distinction is made between statistics based on scientific theory and theory-free statistics. This distinction is discussed in the contexts of hypothesis testing, Bayesian inference, a priori planned contrasts, a new simple computational method, and alternative data interpretations. (TJH)
Descriptors: Bayesian Statistics, Computation, Hypothesis Testing, Scientific Research

Bradlow, Eric T.; Wainer, Howard; Wang, Xiaohui – Psychometrika, 1999
Proposes a parametric approach that involves a modification of standard Item Response Theory models that explicitly accounts for the nesting of items within the same testlets and that can be applied to multiple-choice sections comprising a mixture of independent items and testlets. (Author/SLD)
Descriptors: Bayesian Statistics, Item Response Theory, Models, Multiple Choice Tests

Bajari, Patrick; Hortacsu, Ali – Journal of Political Economy, 2005
Recently, economists have developed methods for structural estimation of auction models. Many researchers object to these methods because they find the strict rationality assumptions to be implausible. Using bid data from first-price auction experiments, we estimate four alternative structural models: (1) risk-neutral Bayes-Nash, (2) risk-averse…
Descriptors: Computation, Bids, Models, Bayesian Statistics
Stanfield, William D.; Carlton, Matthew A. – American Biology Teacher, 2004
The use of Bayes' formula is applied to the biological problem of pedigree analysis to show that the Bayes' formula and non-Bayesian or "classical" methods of probability calculation give different answers. First year college students of biology can be introduced to the Bayesian statistics.
Descriptors: Probability, Bayesian Statistics, Computation, Biology
Khuri, Andre – International Journal of Mathematical Education in Science and Technology, 2004
The Dirac delta function has been used successfully in mathematical physics for many years. The purpose of this article is to bring attention to several useful applications of this function in mathematical statistics. Some of these applications include a unified representation of the distribution of a function (or functions) of one or several…
Descriptors: Maximum Likelihood Statistics, Bayesian Statistics, Statistics, College Mathematics
Goenner, Cullen F.; Snaith, Sean M. – Research in Higher Education, 2004
Empirical analysis requires researchers to choose which variables to use as controls in their models. Theory should dictate this choice, yet often in social science there are several theories that may suggest the inclusion or exclusion of certain variables as controls. The result of this is that researchers may use different variables in their…
Descriptors: Models, Prediction, Graduation Rate, Universities
Trafimow, David – Psychological Review, 2005
In their comment on D. Trafimow, M. D. Lee and E. Wagenmakers argued that the requisite probabilities to use in Bayes's theorem can always be found. In the present reply, the author asserts that M. D. Lee and E. Wagenmakers use a problematic assumption and that finding the requisite probabilities is not straightforward. After describing the…
Descriptors: Probability, Bayesian Statistics, Error Patterns, Criticism
Sinharay, Sandip – Journal of Educational Measurement, 2005
Even though Bayesian estimation has recently become quite popular in item response theory (IRT), there is a lack of works on model checking from a Bayesian perspective. This paper applies the posterior predictive model checking (PPMC) method (Guttman, 1967; Rubin, 1984), a popular Bayesian model checking tool, to a number of real applications of…
Descriptors: Measurement Techniques, Item Response Theory, Bayesian Statistics, Models
Zhu, Mu; Lu, Arthur Y. – Journal of Statistics Education, 2004
In Bayesian statistics, the choice of the prior distribution is often controversial. Different rules for selecting priors have been suggested in the literature, which, sometimes, produce priors that are difficult for the students to understand intuitively. In this article, we use a simple heuristic to illustrate to the students the rather…
Descriptors: Bayesian Statistics, Maximum Likelihood Statistics, Probability, Statistical Distributions
Linn, Shai – Journal of Statistics Education, 2004
Courses in clinical epidemiology usually include acquainting students with a single 2X2 table. All diagnostic test characteristics are explained using this table. This pedagogic approach may be misleading. A new didactic approach is hereby proposed, using two tables, each with specific analogous notations (uppercase and lowercase) and derived…
Descriptors: Epidemiology, Diagnostic Tests, Bayesian Statistics, Prediction
Dagne, Getachew A.; Brown, C. Hendricks; Howe, George W. – Journal of Educational and Behavioral Statistics, 2003
Intervention studies often rely on microcoded data of social interactions to provide evidence of change due to development or treatment. Traditionally these data have been collapsed into small contingency tables. Such an approach can introduce spurious findings. Instead of treating each unit's contingency table independently, or collapsing the…
Descriptors: Statistical Analysis, Bayesian Statistics, Intervention, Unemployment
Meyer, Katrina A.; Xu, Yonghong Jade – Internet and Higher Education, 2007
This study answered questions about which faculty come to use technology in their teaching and used a novel statistical analysis to develop a model that captures the primary factors influencing faculty technology use. It used a sample of 16,914 faculty within the 2004 National Study of Postsecondary Faculty to explore explanations for faculty…
Descriptors: Classification, Educational Technology, Bayesian Statistics, College Faculty
Lockwood, J. R.; McCaffrey, Daniel F.; Mariano, Louis T.; Setodji, Claude – Journal of Educational and Behavioral Statistics, 2007
There is increased interest in value-added models relying on longitudinal student-level test score data to isolate teachers' contributions to student achievement. The complex linkage of students to teachers as students progress through grades poses both substantive and computational challenges. This article introduces a multivariate Bayesian…
Descriptors: Urban Schools, Academic Persistence, Reading Achievement, Mathematics Achievement
Almond, Russell G.; Mulder, Joris; Hemat, Lisa A.; Yan, Duanli – ETS Research Report Series, 2006
Bayesian network models offer a large degree of flexibility for modeling dependence among observables (item outcome variables) from the same task that may be dependent. This paper explores four design patterns for modeling locally dependent observations from the same task: (1) No context--Ignore dependence among observables; (2) Compensatory…
Descriptors: Bayesian Statistics, Networks, Models, Design
Mislevy, Robert J.; Huang, Chun-Wei – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2006
Advances in cognitive research increase the need for assessment that can address the processes and the strategies by which persons solve problems. Several psychometric models have been introduced to handle claims cast in information-processing terms, explicitly modeling performance in terms of theory-based predictions of performance. Cognitively…
Descriptors: Cognitive Science, Cognitive Processes, Problem Solving, Psychometrics