The workshop will be about Bayesian multilevel models and their implementation in R using the package brms. At start there will be an introduction to multilevel modeling and to Bayesian statistics in general followed by an introduction to Stan, which is an incredibly flexible language to fit open-ended Bayesian models. I will then explain how to access Stan using just basic R formula syntax via the brms package for a set of increasingly complex models. brms supports a wide range of response distributions and modeling options such as splines, Gaussian processes, autocorrelation, or censoring all in a multilevel context. A lot of post-processing and plotting methods are implemented as well and will be introduced during the workshop.
 
The workshop will include some hands-on exercises in R. It is highly recommended to have a working version of brms installed on your machine to be able to run the exercises yourself.
 

This workshop aims (1) to provide a short course on the theoretical foundations of cognitive diagnosis modeling (CDM), (2) to introduce the development of a cognitive diagnostic assessment on proportional reasoning, (3) to illustrate how an R package developed by the instructors can be used for various CDM analyses, (4) and to discuss some recent developments in the area. 

Unlike unidimensional IRT, CDM aims to provide information about the attributes or skills that are finer-grained and more relevant to classroom instruction and student learning. This workshop will be useful for faculty and students specializing in educational measurement, as well as testing professionals working in government or testing organizations.

In this workshop, the GDINA R package will be introduced. This package overcomes several drawbacks in existing software packages and offers a set of functions for CDM analyses, such as calibration of various diagnostic models, validation of the Q-matrix, and detection of differential item functioning. After this workshop, participants are expected to be able to conduct various CDM analyses on their own.

Tentatively, the workshop will be structured as follows: 

Part 1: Introduction
1.1. Introduction to diagnostic modeling framework 
1.2. Development of a diagnostic assessment
1.3. The G-DINA model framework

Part 2: CDM analysis with the GDINA R package
2.1. Introduction to GDINA R package
2.2. Model calibration, fit evaluation, comparison, and identifiability 
3.3. Q-matrix validation, differential item functioning, and classification accuracy 

Part 3: Recent areas of development
3.1. Cognitive-diagnosis computerized adaptive testing 
3.2. Methods for small sample sizes and large number of attributes 
3.3. Models for polytomous skills, polytomous responses, and longitudinal data
 

Based on formal logic, set theory and Boolean algebra, Qualitative Comparative Analysis (QCA) is a research method used to analyse combinations of conditions which are sufficient and/or necessary for the outcome under study. Developed by the political scientist and sociologist Charles Ragin, QCA focuses holistically on cases as combinations of factors rather than on the relationship between variables (as correlation-based methods such as regression analysis do). While it was developed in a case study tradition where just a small number of cases form the focus of an analysis, it is well suited to analysing large n datasets. Thus, while combining elements from qualitative as well as quantitative research approaches, it is genuinely innovative in transcending the qualitative-quantitative divide.

This workshop provides an introduction to QCA in its crisp, fuzzy and multi-value variants as well as practice sessions on the application of QCA using fsQCA and R software (package QCApro). Researchers are welcome to bring their own data if they wish to analyse them using QCA, but this is no requirement; practice datasets will be provided. No prior knowledge of QCA or any particular software is assumed.