Three pre-conference short courses will be offered on Monday July 9, 2018 - see below. Register for short courses here.
Continuous- and Discrete-Time Dynamic Modeling in R
Instructors: Sy-Miin Chow, Michael Hunter, Charles Driver, and Peter Molenaar
The course will provide an overview and hands-on tutorial on options for performing dynamic modeling using multivariate longitudinal data, particularly those collected from ecological momentary assessment (e.g., observational coding; daily diary; intensive longitudinal data) and psychophysiological (e.g., functional MRI) studies. Topics to be covered include discrete-time state-space/difference equation models, differential equation models, and continuous-time structural equation models. We will discuss and show examples of several packages for fitting dynamic models using the freely available statistical software, R, including: dynamic modeling in R (dynr), continuous-time structural equation modeling (ctsem), and group Iterative multiple model estimation (GIMME). Students will be guided through using software programs to develop familiarity with different types of dynamic models, followed by model construction, estimation, interpretations, and diagnostics. Participants are expected to have completed at least a graduate course on regression techniques and are comfortable working with multivariate data. Users who have not had prior exposure to R will be provided with access to pre-workshop electronic tutorials compiled and delivered by the workshop instructors.
Computerized Adaptive Testing and Multistage Testing with R
Instructors: David Magis, Duanli Yan, and Alina A. von Davier
The goal of this workshop is to provide a practical (and brief) overview of the theory on computerized adaptive testing (CAT) and multistage testing (MST), and illustrate the methodologies and applications using R open source language and several data examples. The implementations rely on the R packages catR and mstR that have been already or are being developed and include some of the newest research algorithms developed by the authors. This workshop will cover several topics: the basics of R, theoretical overview of CAT and MST, CAT and MST designs, assembly methodologies, catR and mstR packages, simulations and applications. The intended audience for the workshop is undergraduate/graduate students, faculty, researchers, practitioners at testing institutions, and anyone in psychometrics, measurement, education, psychology and other fields who is interested in computerized adaptive and multistage testing, especially in practical implementations of simulation using R.
New Matching for Causal Inference and Impact Evaluation
Instructor: Jose R. Zubizarreta
In observational studies of causal effects, matching methods are extensively used to approximate the ideal study that would be conducted if controlled experimentation was possible. In this workshop we will explore new advancements in matching methods that overcome three limitations of standard matching approaches, and: (1) directly obtain flexible forms of covariate balance, ranging from mean balance to balance of entire joint distributions, (2) produce self-weighting matched samples that are representative of target populations by design, and (3) handle multiple treatment doses without resorting to a generalization of the propensity score and instead balancing the original covariates. We will discuss extensions to matching with instrumental variables and in discontinuity designs, and for matching before randomization in experiments. The methods discussed build upon recent advancements in computation and optimization for big data. We will use the statistical software package 'designmatch' for R. Participants will gain a clear picture of role of matching for causal inferences, and its pros and cons. They will learn how to construct balanced and representative matched samples, improving the core tenets of traditional matching methods on the estimated propensity score. The target audience of the workshop is applied researchers with quantitative training and familiarity with traditional regression methods. Facility with R is ideal, but not strictly necessary, as well-documented, step-by-step code will be provided.