data.ecpe: Dataset ECPE

In CDM: Cognitive Diagnosis Modeling

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.

Details

The dataset has been used in Templin and Hoffman (2013), and Templin and Bradshaw (2014).

Source

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.

See Also

GDINA::ecpe

Examples

## Not run: 
data(data.ecpe, package="CDM")

dat <- data.ecpe$data[,-1]
Q <- data.ecpe$q.matrix

#*** Model 1: LCDM model
mod1 <- CDM::gdina( dat, q.matrix=Q, link="logit")
summary(mod1)

#*** Model 2: DINA model
mod2 <- CDM::gdina( dat, q.matrix=Q, 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(Q)
# 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(Q) > 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( dat, q.matrix=Q, link="logit",
            zeroprob.skillclasses=zeroprob.skillclasses, Mj=Mj.user,
            avoid.zeroprobs=TRUE )
summary(mod3)

#*****************************************
#** estimate further models

#*** Model 4: RRUM model
mod4 <- CDM::gdina( dat, q.matrix=Q, rule="RRUM")
summary(mod4)
# compare some models
IRT.compareModels(mod1, mod2, mod3, mod4 )

#*** Model 5a: GDINA model with identity link
mod5a <- CDM::gdina( dat, q.matrix=Q, link="identity")
summary(mod5a)
#*** Model 5b: GDINA model with logit link
mod5b <- CDM::gdina( dat, q.matrix=Q, link="logit")
summary(mod5b)
#*** Model 5c: GDINA model with log link
mod5c <- CDM::gdina( dat, q.matrix=Q, link="log")
summary(mod5c)
# compare models
IRT.compareModels(mod5a, mod5b, mod5c)

## End(Not run)

CDM documentation built on Aug. 25, 2022, 5:08 p.m.