CDM (version 4.991-1)

data.timss03.G8.su: TIMSS 2003 Mathematics 8th Grade (Su et al., 2013)

Description

This is a dataset with a subset of 23 mathematics items from TIMSS 2003 items used in Su et al. (2013).

Usage

data(data.timss03.G8.su)

Format

The data contains scored item responses (data), the Q-matrix (q.matrix) and further item informations (iteminfo). The format is List of 3 $ data :'data.frame': ..$ idstud : num [1:757] 1e+07 1e+07 1e+07 1e+07 1e+07 ... ..$ idbook : num [1:757] 1 1 1 1 1 1 1 1 1 1 ... ..$ M012001 : num [1:757] 0 1 0 0 1 0 1 0 0 0 ... ..$ M012002 : num [1:757] 1 1 0 1 0 0 1 1 1 1 ... ..$ M012004 : num [1:757] 0 1 1 1 1 0 1 1 0 0 ... [...] ..$ M022234B: num [1:757] 0 0 0 0 0 0 0 0 0 0 ... ..$ M022251 : num [1:757] 0 0 0 0 0 0 0 0 0 0 ... ..$ M032570 : num [1:757] 1 1 0 1 0 0 1 1 1 1 ... ..$ M032643 : num [1:757] 1 0 0 0 0 0 1 1 0 0 ... $ q.matrix: int [1:23, 1:13] 1 0 0 0 0 0 1 0 0 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:23] "M012001" "M012002" "M012004" "M012016" ... .. ..$ : chr [1:13] "S1" "S2" "S3" "S4" ... $ iteminfo: chr [1:23, 1:9] "M012001" "M012002" "M012004" "M012016" ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:9] "item" "ItemType" "reporting_category" "content" ... For a detailed description of skills S1, S2, ..., S15 see Su et al. (2013, Table 2).

Source

Subset of US 8th graders (Booklet 1) in the TIMSS 2003 mathematics study

References

Su, Y.-L., Choi, K. M., Lee, W.-C., Choi, T., & McAninch, M. (2013). Hierarchical cognitive diagnostic analysis for TIMSS 2003 mathematics. CASMA Research Report 35. Center for Advanced Studies in Measurement and Assessment (CASMA), University of Iowa.

Examples

## Not run: 
# data(data.timss03.G8.su)
# data <- data.timss03.G8.su$data[,-c(1,2)]
# q.matrix <- data.timss03.G8.su$q.matrix
# 
# #*** Model 1: DINA model with complete skill space of 2^13=8192 skill classes
# mod1 <- CDM::din( data , q.matrix )
# 
# #*** Model 2: Skill space approximation with 3000 skill classes instead of
# #    2^13 = 8192 classes as in Model 1
# ss2 <- CDM::skillspace.approximation( L = 3000 , K = ncol(q.matrix) )
# mod2 <- CDM::din( data , q.matrix , skillclasses = ss2 )
# 
# #*** Model 3: DINA model with a hierarchical skill space
# #   see Su et al. (2013): Fig. 6
# B <- "S1 > S2 > S7 > S8
#       S15 > S9
#       S3 > S9
#       S13 > S4 > S9
#       S14 > S5 > S6 > S11"
# # Note that S10 and S12 are not included in the dataset contained in this package
# skill.names <- colnames(q.matrix)
# ss3 <- CDM::skillspace.hierarchy(B=B , skill.names=skill.names)
# # The reduced skill space "only" contains 325 skill classes
# mod3 <- CDM::din( data , q.matrix , skillclasses = ss3$skillspace.reduced )
# ## End(Not run)