The goal of jrt is to provide tools to use Item-Response Theory (IRT) models on judgment data, especially in the context of the Consensual Assessment Technique, as presented in Myszkowski (2021).

- Myszkowski, N. (2021). Development of the R library “jrt”: Automated
item response theory procedures for judgment data and their application
with the consensual assessment technique.
*Psychology of Aesthetics, Creativity, and the Arts, 15*(3), 426-438. http://dx.doi.org/10.1037/aca0000287

You can install the released version of jrt from CRAN with:

`install.packages("jrt")`

- Load the library

```
library(jrt)
#> Loading required package: directlabels
```

- Load example dataset

`<- jrt::ratings data `

- To automatically select models

```
<- jrt(data, progress.bar = F)
fit #> The possible responses detected are: 1-2-3-4-5
#>
#> -== Model Selection (6 judges) ==-
#> AIC for Rating Scale Model: 4414.163 | Model weight: 0.000
#> AIC for Generalized Rating Scale Model: 4368.776 | Model weight: 0.000
#> AIC for Partial Credit Model: 4022.956 | Model weight: 0.000
#> AIC for Generalized Partial Credit Model: 4014.652 | Model weight: 0.000
#> AIC for Constrained Graded Rating Scale Model: 4399.791 | Model weight: 0.000
#> AIC for Graded Rating Scale Model: 4308.616 | Model weight: 0.000
#> AIC for Constrained Graded Response Model: 3999.248 | Model weight: 0.673
#> AIC for Graded Response Model: 4000.689 | Model weight: 0.327
#> -> The best fitting model is the Constrained Graded Response Model.
#>
#> -== General Summary ==-
#> - 6 Judges
#> - 300 Products
#> - 5 response categories (1-2-3-4-5)
#> - Mean judgment = 2.977 | SD = 0.862
#>
#> -== IRT Summary ==-
#> - Model: Constrained (equal slopes) Graded Response Model (Samejima, 1969) | doi: 10.1007/BF03372160
#> - Estimation package: mirt (Chalmers, 2012) | doi: 10.18637/jss.v048.i06
#> - Estimation algorithm: Expectation-Maximization (EM; Bock & Atkin, 1981) | doi: 10.1007/BF02293801
#> - Method of factor scoring: Expected A Posteriori (EAP)
#> - AIC = 3999.248 | AICc = 4003.993 | BIC = 4091.843 | SABIC = 3999.248
#>
#> -== Model-based reliability ==-
#> - Empirical reliability | Average in the sample: .893
#> - Expected reliability | Assumes a Normal(0,1) prior density: .894
```

- To select models a priori

```
<- jrt(data, irt.model = "PCM")
fit #> The possible responses detected are: 1-2-3-4-5
#>
#> -== General Summary ==-
#> - 6 Judges
#> - 300 Products
#> - 5 response categories (1-2-3-4-5)
#> - Mean judgment = 2.977 | SD = 0.862
#>
#> -== IRT Summary ==-
#> - Model: Partial Credit Model (Masters, 1982) | doi: 10.1007/BF02296272
#> - Estimation package: mirt (Chalmers, 2012) | doi: 10.18637/jss.v048.i06
#> - Estimation algorithm: Expectation-Maximization (EM; Bock & Atkin, 1981) | doi: 10.1007/BF02293801
#> - Method of factor scoring: Expected A Posteriori (EAP)
#> - AIC = 4022.956 | AICc = 4027.701 | BIC = 4115.551 | SABIC = 4022.956
#>
#> -== Model-based reliability ==-
#> - Empirical reliability | Average in the sample: .889
#> - Expected reliability | Assumes a Normal(0,1) prior density: .759
```

- To plot all category curves

`jcc.plot(fit)`

- To plot on judge’s category curves

`jcc.plot(fit, judge = 1)`

- Graphical options

`jcc.plot(fit, judge = 1, overlay.reliability = T, greyscale = T, theme = "classic")`

- To plot total information

`info.plot(fit)`

- To plot judge information

`info.plot(fit, judge = 1)`

- Other options for information plots

```
info.plot(fit, type = "Reliability",
y.line = .70,
y.limits = c(0,1),
theta.span = 4,
theme = "classic")
```