CDM (version 4.991-1)

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)
# 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)