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# Applications of Item Response Theory in R
### W. Jake Thompson, Ph.D.

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layout: true

---
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# Materials

---
# Where can I get the stuff?

- Slides: https://epsy922.wjakethompson.com

- Github: https://github.com/wjakethompson/r-irt

---
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# 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)

---
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# Questions?

<svg style="height:0.8em;top:.04em;position:relative;fill:#165C7D;" viewBox="0 0 496 512"><path d="M336.5 160C322 70.7 287.8 8 248 8s-74 62.7-88.5 152h177zM152 256c0 22.2 1.2 43.5 3.3 64h185.3c2.1-20.5 3.3-41.8 3.3-64s-1.2-43.5-3.3-64H155.3c-2.1 20.5-3.3 41.8-3.3 64zm324.7-96c-28.6-67.9-86.5-120.4-158-141.6 24.4 33.8 41.2 84.7 50 141.6h108zM177.2 18.4C105.8 39.6 47.8 92.1 19.3 160h108c8.7-56.9 25.5-107.8 49.9-141.6zM487.4 192H372.7c2.1 21 3.3 42.5 3.3 64s-1.2 43-3.3 64h114.6c5.5-20.5 8.6-41.8 8.6-64s-3.1-43.5-8.5-64zM120 256c0-21.5 1.2-43 3.3-64H8.6C3.2 212.5 0 233.8 0 256s3.2 43.5 8.6 64h114.6c-2-21-3.2-42.5-3.2-64zm39.5 96c14.5 89.3 48.7 152 88.5 152s74-62.7 88.5-152h-177zm159.3 141.6c71.4-21.2 129.4-73.7 158-141.6h-108c-8.8 56.9-25.6 107.8-50 141.6zM19.3 352c28.6 67.9 86.5 120.4 158 141.6-24.4-33.8-41.2-84.7-50-141.6h-108z"/></svg> [wjakethompson.com](https://wjakethompson.com)

<svg style="height:0.8em;top:.04em;position:relative;fill:#165C7D;" viewBox="0 0 512 512"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> [wjakethompson@ku.edu](mailto:wjakethompson@ku.edu)

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<svg style="height:0.8em;top:.04em;position:relative;fill:#165C7D;" viewBox="0 0 496 512"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> [@wjakethompson](https://github.com/wjakethompson)

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Applications of Item Response Theory in RW. Jake Thompson, Ph.D.1

Materials2

Where can I get the stuff?

Slides: https://epsy922.wjakethompson.com
Github: https://github.com/wjakethompson/r-irt

3

Welcome to R4

R Statistical Programming

Programming language for data analysis
- Like SAS, Mplus, SPSS, but better
End to end data analysis — no dependence on other programs
Professional graphics
Free and open source!

5

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
  - Suite of packages for data science
  - Data visualization, manipulation, tidying
- mirt
  - Estimate IRT models

6

Using R

R : iOS :: Integrated Development Environment : iPhone
Many choices of IDE
- RStudio
- R Console
- Emacs + ESS, Vim, Sublime
Today: RStudio Cloud

7

bit.ly/epsy922-r-irt

8

What's Next?More analyses included in the full-example directoryPolytomous items
Multidimensional model

9

Item Characteristic Curves

10

Test Characteristics

11

Item-Level Fit

12

Model-Level Fit

Absolute fit

Relative fit through model comparisons

13

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)

14

Questions?

wjakethompson.com

wjakethompson@ku.edu

15

Materials2

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