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McNeish, Daniel – Educational and Psychological Measurement, 2017
In behavioral sciences broadly, estimating growth models with Bayesian methods is becoming increasingly common, especially to combat small samples common with longitudinal data. Although Mplus is becoming an increasingly common program for applied research employing Bayesian methods, the limited selection of prior distributions for the elements of…
Descriptors: Models, Bayesian Statistics, Statistical Analysis, Computer Software
Sweet, Tracy M. – Journal of Educational and Behavioral Statistics, 2015
Social networks in education commonly involve some form of grouping, such as friendship cliques or teacher departments, and blockmodels are a type of statistical social network model that accommodate these grouping or blocks by assuming different within-group tie probabilities than between-group tie probabilities. We describe a class of models,…
Descriptors: Social Networks, Statistical Analysis, Probability, Models
Li, Feiming; Cohen, Allan; Bottge, Brian; Templin, Jonathan – Educational and Psychological Measurement, 2016
Latent transition analysis (LTA) was initially developed to provide a means of measuring change in dynamic latent variables. In this article, we illustrate the use of a cognitive diagnostic model, the DINA model, as the measurement model in a LTA, thereby demonstrating a means of analyzing change in cognitive skills over time. An example is…
Descriptors: Statistical Analysis, Change, Thinking Skills, Measurement
Ezen-Can, Aysu; Boyer, Kristy Elizabeth – Journal of Educational Data Mining, 2015
Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language…
Descriptors: Classification, Dialogs (Language), Computational Linguistics, Information Retrieval
Browne, William; Goldstein, Harvey – Journal of Educational and Behavioral Statistics, 2010
In this article, we discuss the effect of removing the independence assumptions between the residuals in two-level random effect models. We first consider removing the independence between the Level 2 residuals and instead assume that the vector of all residuals at the cluster level follows a general multivariate normal distribution. We…
Descriptors: Computation, Sampling, Markov Processes, Monte Carlo Methods
Levy, Roy; Mislevy, Robert J. – 2003
This paper aims to describe a Bayesian approach to modeling and estimating cognitive models both in terms of statistical machinery and actual instrument development. Such a method taps the knowledge of experts to provide initial estimates for the probabilistic relationships among the variables in a multivariate latent variable model and refines…
Descriptors: Bayesian Statistics, Cognitive Processes, Markov Processes, Mathematical Models
Ghanti, Srinivas; Evans, John E. – 1995
For much of the twentieth century, Markov theory and Markov processes have been widely accepted as valid ways to view statistical variables and parameters. In the complex realm of online searching, where researchers are always seeking the route to the best search strategies and the most powerful query terms and sequences, Markov process analysis…
Descriptors: Coding, Markov Processes, Mathematical Models, Online Searching

Stevens, Rodney Lynn – Journal of Geological Education, 1990
Methodology which can be used to evaluate cyclic trends in sequential data are discussed. Examples and suggestions for teaching using this method are provided. A list of 25 references is included. (CW)
Descriptors: College Science, Data Analysis, Earth Science, Geology

Shah, Chandra; Burke, Gerald – Higher Education, 1999
A Markov chain is used to model the movement of undergraduates through the higher education system in Australia. Given the student's age on commencing a course of study, the model provides estimates of the probability of course completion, mean time for completion, and mean time spent in the higher education system. (Author/MSE)
Descriptors: Academic Persistence, Age, College Students, Enrollment Management