Artificial data: dichotomously coded fictitious answers of 400 respondents to 9 items assuming 3 underlying attributes.
data(sim.dina)
data(sim.dino)
data(sim.qmatrix)The sim.dina and sim.dino data sets include dichotomous
answers of \(N=400\) respondents to \(J=9\) items, thus they are
\(400 \times 9\) data matrices. For both data sets \(K=3\)
attributes are assumed to underlie the process of responding, stored
in sim.qmatrix.
The sim.dina data set is simulated according to the DINA condensation
rule, whereas the sim.dino data set is simulated according to the
DINO condensation rule. The slipping errors for the items 1 to 9 in both
data sets are 0.20, 0.20, 0.20, 0.20, 0.00, 0.50, 0.50, 0.10, 0.03
and the guessing errors are 0.10, 0.125, 0.15, 0.175, 0.2, 0.225,
0.25, 0.275, 0.3. The attributes are assumed to be mastered with expected
probabilities of -0.4, 0.2, 0.6, respectively. The correlation of
the attributes is 0.3 for attributes 1 and 2, 0.4 for
attributes 1 and 3 and 0.1 for attributes 2 and 3.
Dataset sim.dina
anova (Examples 1, 2),
cdi.kli (Example 1),
din (Examples 2, 4, 5),
gdina (Example 1),
itemfit.sx2 (Example 2),
modelfit.cor.din (Example 1)
Dataset sim.dino
cdm.est.class.accuracy (Example 1),
din (Example 3), gdina (Examples 2, 3, 4),
Rupp, A. A., Templin, J. L., & Henson, R. A. (2010) Diagnostic Measurement: Theory, Methods, and Applications. New York: The Guilford Press.