The format of the datasets data.reck61DAT1 and
data.reck61DAT2 (Table 6.1, p. 153) is
List of 4 $ data : num [1:2500, 1:30] 1 0 0 1 1 0 0 1 1 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:30] "A01" "A02" "A03" "A04" ... $ pars :'data.frame': ..$ a1: num [1:30] 0.747 0.46 0.861 1.014 0.552 ... ..$ a2: num [1:30] 0.025 0.0097 0.0067 0.008 0.0204 0.0064 0.0861 ... ..$ a3: num [1:30] 0.1428 0.0692 0.404 0.047 0.1482 ... ..$ d : num [1:30] 0.183 -0.192 -0.466 -0.434 -0.443 ... $ mu : num [1:3] -0.4 -0.7 0.1 $ sigma: num [1:3, 1:3] 1.21 0.297 1.232 0.297 0.81 ...
The dataset data.reck61DAT2 has correlated dimensions while
data.reck61DAT1 has uncorrelated dimensions.
Datasets data.reck73C1a and data.reck73C1b use item parameters
from Table 7.3 (p. 188). The dataset C1a has uncorrelated dimensions,
while C1b has perfectly correlated dimensions. The items are sensitive to
3 dimensions. The format of the datasets is
List of 4 $ data : num [1:2500, 1:30] 1 0 1 1 1 0 1 1 1 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:30] "A01" "A02" "A03" "A04" ... $ pars :'data.frame': 30 obs. of 4 variables: ..$ a1: num [1:30] 0.747 0.46 0.861 1.014 0.552 ... ..$ a2: num [1:30] 0.025 0.0097 0.0067 0.008 0.0204 0.0064 ... ..$ a3: num [1:30] 0.1428 0.0692 0.404 0.047 0.1482 ... ..$ d : num [1:30] 0.183 -0.192 -0.466 -0.434 -0.443 ... $ mu : num [1:3] 0 0 0 $ sigma: num [1:3, 1:3] 0.167 0.236 0.289 0.236 0.334 ...
The dataset data.reck75C2 is simulated using item parameters
from Table 7.5 (p. 191). It contains items which are sensitive to only
one dimension but individuals which have abilities in three
uncorrelated dimensions. The format is
List of 4 $ data : num [1:2500, 1:30] 0 0 1 1 1 0 0 1 1 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:30] "A01" "A02" "A03" "A04" ... $ pars :'data.frame': 30 obs. of 4 variables: ..$ a1: num [1:30] 0.56 0.48 0.67 0.57 0.54 0.74 0.7 0.59 0.63 0.64 ... ..$ a2: num [1:30] 0.62 0.53 0.63 0.69 0.58 0.69 0.75 0.63 0.64 0.64 ... ..$ a3: num [1:30] 0.46 0.42 0.43 0.51 0.41 0.48 0.46 0.5 0.51 0.46 ... ..$ d : num [1:30] 0.1 0.06 -0.38 0.46 0.14 0.31 0.06 -1.23 0.47 1.06 ... $ mu : num [1:3] 0 0 0 $ sigma: num [1:3, 1:3] 1 0 0 0 1 0 0 0 1
The dataset data.reck78ExA contains simulated item responses
from Table 7.8 (p. 204 ff.). There are three item clusters and
two ability dimensions. The format is
List of 4 $ data : num [1:2500, 1:50] 0 1 1 0 1 0 0 0 0 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:50] "A01" "A02" "A03" "A04" ... $ pars :'data.frame': 50 obs. of 3 variables: ..$ a1: num [1:50] 0.889 1.057 1.047 1.178 1.029 ... ..$ a2: num [1:50] 0.1399 0.0432 0.016 0.0231 0.2347 ... ..$ d : num [1:50] 0.2724 1.2335 -0.0918 -0.2372 0.8471 ... $ mu : num [1:2] 0 0 $ sigma: num [1:2, 1:2] 1 0 0 1
The dataset data.reck79ExB contains simulated item responses
from Table 7.9 (p. 207 ff.). There are three item clusters and
three ability dimensions. The format is
List of 4 $ data : num [1:2500, 1:50] 1 1 0 1 0 0 0 1 1 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:50] "A01" "A02" "A03" "A04" ... $ pars :'data.frame': 50 obs. of 4 variables: ..$ a1: num [1:50] 0.895 1.032 1.036 1.163 1.022 ... ..$ a2: num [1:50] 0.052 0.132 0.144 0.13 0.165 ... ..$ a3: num [1:50] 0.0722 0.1923 0.0482 0.1321 0.204 ... ..$ d : num [1:50] 0.2724 1.2335 -0.0918 -0.2372 0.8471 ... $ mu : num [1:3] 0 0 0 $ sigma: num [1:3, 1:3] 1 0 0 0 1 0 0 0 1
Source
Simulated datasets
References
Reckase, M. (2009). Multidimensional item response theory.
New York: Springer.