CDM: data.jang – R documentation

Pricing

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

Documentation

CDM

data.jang

Dataset Jang (2009)

Description

Simulated dataset according to the Jang (2005) L2 reading comprehension study.

Usage

data(data.jang)

Format

The format is:

List of 2
$ data : num [1:1500, 1:37] 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:37] "I1" "I2" "I3" "I4" ...
$ q.matrix:'data.frame':
..$ CDV: int [1:37] 1 0 0 1 0 0 0 0 0 0 ...
..$ CIV: int [1:37] 0 0 1 0 0 0 1 0 1 1 ...
..$ SSL: int [1:37] 1 1 1 1 0 0 0 0 0 0 ...
..$ TEI: int [1:37] 0 0 0 0 0 0 0 1 0 0 ...
..$ TIM: int [1:37] 0 0 0 1 1 1 0 0 0 0 ...
..$ INF: int [1:37] 0 1 0 0 0 0 1 0 0 0 ...
..$ NEG: int [1:37] 0 0 0 0 1 0 1 0 0 0 ...
..$ SUM: int [1:37] 0 0 0 0 1 0 0 0 0 0 ...
..$ MCF: int [1:37] 0 0 0 0 0 0 0 0 0 0 ...

Source

Simulated dataset.

References

Jang, E. E. (2009). Cognitive diagnostic assessment of L2 reading comprehension ability: Validity arguments for Fusion Model application to LanguEdge assessment. Language Testing, 26, 31-73.

Examples

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

data <- data.jang$data
q.matrix <- data.jang$q.matrix

#*** Model 1: Reduced RUM model
mod1 <- CDM::gdina( data, q.matrix, rule="RRUM", conv.crit=.001, increment.factor=1.025 )
summary(mod1)

#*** Model 2: Additive model (identity link)
mod2 <- CDM::gdina( data, q.matrix, rule="ACDM", conv.crit=.001, linkfct="identity" )
summary(mod2)

#*** Model 3: DINA model
mod3 <- CDM::gdina( data, q.matrix, rule="DINA", conv.crit=.001 )
summary(mod3)

anova(mod1,mod2)
  ##       Model   loglike Deviance Npars      AIC      BIC    Chisq df  p
  ##   1 Model 1 -30315.03 60630.06   153 60936.06 61748.98 88.29627  0  0
  ##   2 Model 2 -30270.88 60541.76   153 60847.76 61660.68       NA NA NA
anova(mod1,mod3)
  ##       Model   loglike Deviance Npars      AIC      BIC    Chisq df  p
  ##   2 Model 2 -30373.99 60747.97   129 61005.97 61691.38 117.9128 24  0
  ##   1 Model 1 -30315.03 60630.06   153 60936.06 61748.98       NA NA NA

# RRUM
summary( CDM::modelfit.cor.din( mod1, jkunits=0) )
  ##          type    value       p
  ##   1   max(X2) 11.79073 0.39645
  ##   2 abs(fcor)  0.09541 0.07422
  ##                       est
  ##   MADcor          0.01834
  ##   SRMSR           0.02300
  ##   MX2             0.86718
  ##   100*MADRESIDCOV 0.38690
  ##   MADQ3           0.02413

# additive model (identity)
summary( CDM::modelfit.cor.din( mod2, jkunits=0) )
  ##          type   value       p
  ##   1   max(X2) 9.78958 1.00000
  ##   2 abs(fcor) 0.08770 0.22993
  ##                       est
  ##   MADcor          0.01721
  ##   SRMSR           0.02158
  ##   MX2             0.69163
  ##   100*MADRESIDCOV 0.36343
  ##   MADQ3           0.02423

# DINA model
summary( CDM::modelfit.cor.din( mod3, jkunits=0) )
  ##          type    value       p
  ##   1   max(X2) 13.48449 0.16020
  ##   2 abs(fcor)  0.10651 0.01256
  ##                       est
  ##   MADcor          0.01999
  ##   SRMSR           0.02495
  ##   MX2             0.92820
  ##   100*MADRESIDCOV 0.42226
  ##   MADQ3           0.02258

## End(Not run)

CDM

Cognitive Diagnosis Modeling

v7.5-15

GPL (>= 2)

Authors

Alexander Robitzsch [aut, cre], Thomas Kiefer [aut], Ann Cathrice George [aut], Ali Uenlue [aut]

Initial release

2020-03-10 14:19:21