class: center, middle, inverse, title-slide # Applications of Item Response Theory in R ### W. Jake Thompson, Ph.D. --- class: reg-slide layout: true --- class: reg-section # Materials --- # Where can I get the stuff? - Slides: https://epsy922.wjakethompson.com - Github: https://github.com/wjakethompson/r-irt --- class: reg-section # Welcome to R --- # R Statistical Programming - Programming language for data analysis - Like SAS, Mplus, SPSS, but better - End to end data analysis &mdash; no dependence on other programs - Professional graphics - Free and open source! --- # Extending R - R is like the default phone software - Just like we download apps for our phones, we can download packages for R - Packages can replace base functionality, or offer new features - [**tidyverse**](https://tidyverse.org) - Suite of packages for data science - Data visualization, manipulation, tidying - [**mirt**](https://cran.r-project.org/web/packages/mirt/index.html) - Estimate IRT models --- # Using R - R : iOS :: **Integrated Development Environment** : iPhone - Many choices of IDE - RStudio - R Console - Emacs + ESS, Vim, Sublime - Today: RStudio Cloud --- class: center, middle .huge[[**bit.ly/epsy922-r-irt**](http://bit.ly/epsy922-r-irt)] --- # What's Next? - More analyses included in the `full-example` directory - Polytomous items - Multidimensional model --- # Item Characteristic Curves .pull-left[<img src="figures/dichot-icc.png" width="2560" />] .pull-right[<img src="figures/poly-icc.png" width="2560" />] --- # Test Characteristics .pull-left[<img src="figures/test-dist-1.png" width="2688" />] .pull-right[<img src="figures/test-info-1.png" width="2688" />] --- # Item-Level Fit .pull-left[<img src="figures/dichot-sr.png" width="2560" />] .pull-right[<img src="figures/poly-sr.png" width="2560" />] --- # Model-Level Fit - Absolute fit .pull-left[<img src="figures/plot-q3-1.png" width="2688" />] .pull-right[<img src="figures/sr-test-plot-1.png" width="2688" />] - Relative fit through model comparisons --- # Beyond **mirt** - Many R packages for IRT - Choi & Asilkalkan (2019) provide an overview of features offered in 45 (!!!) packages - More flexibility with Bayesian modeling - *Stan* (Carpenter et al., 2017) - **brms** (Bürkner, 2019, 2020) - **rstan** (Guo, Gabry, & Goodrich, 2020) --- layout: false class: final-slide # Questions? 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