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The School offers a variety of programs at Joseph R. Advanced Theory and Applications of Item Response Theory. This course is designed to acquaint students with knowledge of advanced theory and applications in the field of item response theory (IRT). Computer Programming and Applications for Educational Research, Measurement and Statistics. The purpose of this course is to provide advanced students in the areas of educational research, psychometrics, and statistics with techniques for computer programming, analysis, and carrying out research using computer simulations. This course provides students with an introductory background in the basic principles and applications of hierarchical linear modeling (HLM). This seminar will provide an overview of what we have learned about administering tests on computer between the 1960s and today. This course will explore approaches used by individually administered tests to provide diagnostic information, new psychometric models that hold promise of providing better diagnostic information, and implications for test design. The course will treat a series of thematic areas with a focus on latest developments and emerging theories in learning, development and quantitative methods. Prerequisite: Prior graduate level course work in development, learning, measurement, and statistics. Topics covered include: a review of basic probability, Bayes' rule, probability distributions, Markov Chain Monte Carlo (MCMC) estimation and software for its implementation, and applications of MCMC to a variety of statistical models.
Pearson Hall and Robinson Center on the Lawrence campus. Topics to be covered include: advanced IRT models for dichotomous and polytomous, multidimensional, rater effects, and testlet-based item response data, estimation of parameters for these models and related software, and goodness of fit tests. The topics covered are: Programming with Fortran languages, data manipulation and management, analysis, simulation of data according to statistical and psychometric models, numerical techniques for matrix operations, sampling from distributions, solutions for non-linear equations, and Markov-Chain Monte-Carlo techniques. The course will review both the conceptual issues and methodological issues in using hierarchical linear modeling by working step-by-step with real data sets. The focus will be on measurement issues, but depending on class interest topics will vary. A primary focus will be on how psychometric models can be used with diagnostic subscores that are more reliable and less correlated than traditional approaches. Prerequisite: EPSY 905 or equivalent or consent of instructor.
With the best intentions, functional approaches to organization tend to result in departments that seek to optimize to meet their own goals: more efficient manufacturing runs, larger sales campaigns, or lower cost procurement.
as a measure of model validity is that it can always be increased by adding more variables into the model, except in the unlikely event that the additional variables are exactly uncorrelated with the dependent variable in the data sample being used.
The residuals from a fitted model are the differences between the responses observed at each combination values of the explanatory variables and the corresponding prediction of the response computed using the regression function.
Mathematically, the definition of the residual for the i observation in the data set.
If the model fit to the data were correct, the residuals would approximate the random errors that make the relationship between the explanatory variables and the response variable a statistical relationship.
She has written eight books, including the best-seller Needs Assessment for Organizational Success, and over 100 other publications in performance measurement and evaluation, needs assessment, and strategic alignment. She has been recognized by the Florida State University's Instructional Systems Design program for her contributions to the field, and received the distinguished Gagne/Briggs Outstanding Alumnus award in 2014. Guerra-López’s current research focuses on a set of interrelated processes for supporting the strategic alignment of learning and performance management/improvement programs that includes system analysis, design, implementation, monitoring and evaluation. Guerra-Lopez does significant work in capacity building for international development through collaborations with the World Bank, The United States Agency for International Development, the Interamerican Development Bank, and the United Nations.