CDM (version 4.991-1)

data.ecpe: Dataset ECPE

Description

ECPE dataset from the Templin and Hoffman (2013) tutorial of specifying cognitive diagnostic models in Mplus.

Usage

data(data.ecpe)

Format

The format of the data is a list containing the dichotomous item response data data (2922 persons at 28 items) and the Q-matrix q.matrix (28 items and 3 skills): List of 2 $ data :'data.frame': ..$ id : int [1:2922] 1 2 3 4 5 6 7 8 9 10 ... ..$ E1 : int [1:2922] 1 1 1 1 1 1 1 0 1 1 ... ..$ E2 : int [1:2922] 1 1 1 1 1 1 1 1 1 1 ... ..$ E3 : int [1:2922] 1 1 1 1 1 1 1 1 1 1 ... ..$ E4 : int [1:2922] 0 1 1 1 1 1 1 1 1 1 ... [...] ..$ E27: int [1:2922] 1 1 1 1 1 1 1 0 1 1 ... ..$ E28: int [1:2922] 1 1 1 1 1 1 1 1 1 1 ... $ q.matrix:'data.frame': ..$ skill1: int [1:28] 1 0 1 0 0 0 1 0 0 1 ... ..$ skill2: int [1:28] 1 1 0 0 0 0 0 1 0 0 ... ..$ skill3: int [1:28] 0 0 1 1 1 1 1 0 1 0 ... The skills are skill1: Morphosyntactic rules skill2: Cohesive rules skill3: Lexical rules.

Source

The dataset is used in Templin and Hoffman (2013) and Templin and Bradshaw (2014). The dataset was downloaded from http://psych.unl.edu/jtemplin/teaching/dcm/dcm12ncme/.

References

Templin, J., & Bradshaw, L. (2014). Hierarchical diagnostic classification models: A family of models for estimating and testing attribute hierarchies. Psychometrika, 79, 317-339. Templin, J., & Hoffman, L. (2013). Obtaining diagnostic classification model estimates using Mplus. Educational Measurement: Issues and Practice, 32, 37-50.

Examples

## Not run: 
# data(data.ecpe)
# 
# #*** Model 1: LCDM model 
# mod1 <- CDM::gdina( data.ecpe$data[,-1], q.matrix= data.ecpe$q.matrix , link="logit")
# summary(mod1)
# 
# #*** Model 2: DINA model 
# mod2 <- CDM::gdina( data.ecpe$data[,-1], q.matrix= data.ecpe$q.matrix , rule="DINA")
# summary(mod2)
# 
# # Model comparison using likelihood ratio test
# anova(mod1,mod2)
#   ##       Model   loglike Deviance Npars      AIC      BIC    Chisq df  p
#   ##   2 Model 2 -42841.61 85683.23    63 85809.23 86185.97 206.0359 18  0
#   ##   1 Model 1 -42738.60 85477.19    81 85639.19 86123.57       NA NA NA
# 
# #*** Model 3: Hierarchical LCDM (HLCDM) | Templin and Bradshaw (2014)
# #      Testing a linear hierarchy
# hier <- "skill3 > skill2 > skill1"
# skill.names <- colnames( data.ecpe$q.matrix )
# # define skill space with hierarchy
# skillspace <- CDM::skillspace.hierarchy( hier , skill.names= skill.names )
# skillspace$skillspace.reduced
#   ##        skill1 skill2 skill3
#   ##   A000      0      0      0
#   ##   A001      0      0      1
#   ##   A011      0      1      1
#   ##   A111      1      1      1
# zeroprob.skillclasses <- skillspace$zeroprob.skillclasses
# 
# # define user-defined parameters in LCDM: hierarchical LCDM (HLCDM)
# Mj.user <- mod1$Mj
# # select items with require two attributes
# items <- which( rowSums( data.ecpe$q.matrix ) > 1 )
# # modify design matrix for item parameters
# for (ii in items){
#     m1 <- Mj.user[[ii]]
#     Mj.user[[ii]][[1]] <- (m1[[1]])[,-2]
#     Mj.user[[ii]][[2]] <- (m1[[2]])[-2]
#                 }
# 
# # estimate model
# #    note that avoid.zeroprobs is set to TRUE to avoid algorithmic instabilities
# mod3 <- CDM::gdina( data.ecpe$data[,-1] , q.matrix= data.ecpe$q.matrix ,  link="logit" ,
#             zeroprob.skillclasses=zeroprob.skillclasses , Mj=Mj.user ,
#             avoid.zeroprobs=TRUE )
# summary(mod3)
# 
# #*****************************************
# #** estimate further models
# 
# #*** Model 4: RRUM model
# mod4 <- CDM::gdina( data.ecpe$data[,-1], q.matrix= data.ecpe$q.matrix , rule="RRUM")
# summary(mod4)
# # compare some models
# IRT.compareModels(mod1 , mod2, mod3, mod4 )
# 
# #*** Model 5a: GDINA model with identity link
# mod5a <- CDM::gdina( data.ecpe$data[,-1], q.matrix= data.ecpe$q.matrix , link="identity")
# summary(mod5a)
# #*** Model 5b: GDINA model with logit link
# mod5b <- CDM::gdina( data.ecpe$data[,-1], q.matrix= data.ecpe$q.matrix , link="logit")
# summary(mod5b)
# #*** Model 5c: GDINA model with log link
# mod5c <- CDM::gdina( data.ecpe$data[,-1], q.matrix= data.ecpe$q.matrix , link="log")
# summary(mod5c)
# # compare models
# IRT.compareModels(mod5a, mod5b, mod5c)
# ## End(Not run)