jrt

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).

Installation

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

install.packages("jrt")

Example use

library(jrt)
#> Loading required package: directlabels
data <- jrt::ratings
fit <- jrt(data, progress.bar = F)
#> 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

fit <- jrt(data, irt.model = "PCM")
#> 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

jcc.plot(fit)

jcc.plot(fit, judge = 1)

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

info.plot(fit)

info.plot(fit, judge = 1)

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