CDM (version 2.7-7)

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.

Example Index

gdina (Example 9)

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

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

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

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

#*** Model 3: DINA model
mod3 <- 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( 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( 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( 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