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Stewart, Wayne; Stewart, Sepideh – PRIMUS, 2014
For many scientists, researchers and students Markov chain Monte Carlo (MCMC) simulation is an important and necessary tool to perform Bayesian analyses. The simulation is often presented as a mathematical algorithm and then translated into an appropriate computer program. However, this can result in overlooking the fundamental and deeper…
Descriptors: Markov Processes, Monte Carlo Methods, College Mathematics, Mathematics Instruction
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Blazquez, Maite; Budria, Santiago – Education Economics, 2012
In this paper, we use the 2000-2008 waves of the German Socioeconomic Panel to examine overeducation transitions. The results are based on a first-order Markov model that allows us to account for both the initial conditions problem and potential endogeneity in attrition. We found that overeducation dynamics, especially the probability of entering…
Descriptors: Educational Attainment, Overachievement, Education Work Relationship, Personality Traits
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Koskinen, Johan; Stenberg, Sten-Ake – Journal of Educational and Behavioral Statistics, 2012
When studying educational aspirations of adolescents, it is unrealistic to assume that the aspirations of pupils are independent of those of their friends. Considerable attention has also been given to the study of peer influence in the educational and behavioral literature. Typically, in empirical studies, the friendship networks have either been…
Descriptors: Foreign Countries, Bayesian Statistics, Models, Friendship
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Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio – Journal of Educational and Behavioral Statistics, 2011
An extension of the latent Markov Rasch model is described for the analysis of binary longitudinal data with covariates when subjects are collected in clusters, such as students clustered in classes. For each subject, a latent process is used to represent the characteristic of interest (e.g., ability) conditional on the effect of the cluster to…
Descriptors: Markov Processes, Data Analysis, Maximum Likelihood Statistics, Computation
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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
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Li, Guo-Dong; Yamaguchi, Daisuke; Nagai, Masatake; Masuda, Shiro – International Journal of Learning and Change, 2008
In this paper, we propose a new prediction analysis model which combines the first order one variable Grey differential equation Model (abbreviated as GM(1,1) model) from grey system theory and time series Autoregressive Integrated Moving Average (ARIMA) model from statistics theory. We abbreviate the combined GM(1,1) ARIMA model as ARGM(1,1)…
Descriptors: Markov Processes, Prediction, Statistical Data, Foreign Countries
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Boncek, John; Harden, Sig – Australian Senior Mathematics Journal, 2009
As teachers of first-year college mathematics and science students, the authors are constantly on the lookout for simple classroom exercises that improve their students' analytical and computational skills. In this article, the authors outline a project entitled "Predicting Precipitation in Darwin." In this project, students: (1) analyze…
Descriptors: College Mathematics, Markov Processes, Prediction, Foreign Countries
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Fahrmeir, Ludwig; Raach, Alexander – Psychometrika, 2007
In this paper we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variables are modelled through a flexible semiparametric Gaussian regression model. We extend existing LVMs with the usual linear covariate effects by including nonparametric components for nonlinear…
Descriptors: Markov Processes, Social Sciences, Monte Carlo Methods, Bayesian Statistics
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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