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Andrew Gelman; Daniel Lee; Jiqiang Guo – Journal of Educational and Behavioral Statistics, 2015
Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…
Descriptors: Programming Languages, Bayesian Statistics, Inferences, Monte Carlo Methods
Shin, Yongyun; Raudenbush, Stephen W. – Journal of Educational and Behavioral Statistics, 2010
In organizational studies involving multiple levels, the association between a covariate and an outcome often differs at different levels of aggregation, giving rise to widespread interest in "contextual effects models." Such models partition the regression into within- and between-cluster components. The conventional approach uses each…
Descriptors: Academic Achievement, National Surveys, Computation, Inferences
Wainer, Howard – Journal of Educational and Behavioral Statistics, 2010
In this essay, the author tries to look forward into the 21st century to divine three things: (i) What skills will researchers in the future need to solve the most pressing problems? (ii) What are some of the most likely candidates to be those problems? and (iii) What are some current areas of research that seem mined out and should not distract…
Descriptors: Research Skills, Researchers, Internet, Access to Information