CDM (version 4.991-1)

data.sda6: Dataset SDA6 (Jurich & Bradshaw, 2014)

Description

This is a simulated dataset of the SDA6 study according to informations given in Jurich and Bradshaw (2014).

Usage

data(data.sda6)

Format

The datasets contains 17 items observed at 1710 students. The format is: List of 2 $ data : num [1:1710, 1:17] 0 1 0 1 0 0 0 0 1 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:17] "MCM01" "MCM03" "MCM13" "MCM17" ... $ q.matrix:'data.frame': ..$ CM: int [1:17] 1 1 1 1 0 0 0 0 0 0 ... ..$ II: int [1:17] 0 0 0 0 1 1 1 1 0 0 ... ..$ PP: int [1:17] 0 0 0 0 0 0 0 0 1 1 ... ..$ DG: int [1:17] 0 0 0 0 0 0 0 0 0 0 ... The meaning of the skills is CM -- Critique Methods II -- Identify Improvements PP -- Protect Participants DG -- Discern Generalizability

Source

Simulated data

References

Jurich, D. P., & Bradshaw, L. P. (2014). An illustration of diagnostic classification modeling in student learning outcomes assessment. International Journal of Testing, 14, 49-72.

Examples

## Not run: 	
# data(data.sda6)
# data <- data.sda6$data
# q.matrix <- data.sda6$q.matrix
# 
# #*** Model 1a: LCDM with gdina
# mod1a <- CDM::gdina( data , q.matrix , rule="ACDM" , linkfct="logit" ,
#                   reduced.skillspace=FALSE )  
# summary(mod1a)
# 
# #*** Model 1b: estimate LCDM with gdm
# mod1b <- CDM::gdm( data , q.matrix=q.matrix , theta.k=c(0,1) )  
# summary(mod1b)
# 
# #*** Model 2: LCDM with hierarchy II > CM
# B <- "II > CM"
# ss2 <- CDM::skillspace.hierarchy(B=B , skill.names= colnames(q.matrix ) )
# mod2 <- CDM::gdina( data , q.matrix , rule="ACDM" , linkfct="logit" ,
#                 skillclasses = ss2$skillspace.reduced ,
#                 reduced.skillspace=FALSE )                        
# summary(mod2)
# 
# #*** Model 3: LCDM with hierarchy II > CM and DG > CM
# B <- "II > CM
#       DG > CM"
# ss2 <- CDM::skillspace.hierarchy(B=B , skill.names= colnames(q.matrix ) )
# mod3 <- CDM::gdina( data , q.matrix , rule="ACDM" , linkfct="logit" ,
#                skillclasses = ss2$skillspace.reduced ,
#                reduced.skillspace=FALSE )                        
# summary(mod3)
# 
# # model comparisons
# anova(mod1a,mod2)
# anova(mod1a,mod3)
# # model fit
# summary( CDM::modelfit.cor.din(mod1a))
# summary( CDM::modelfit.cor.din(mod2) )
# summary( CDM::modelfit.cor.din(mod3) )
# ## End(Not run)