Data-sim {CDM} | R Documentation |
Artificial Data: DINA and DINO
Description
Artificial data: dichotomously coded fictitious answers of 400 respondents to 9 items assuming 3 underlying attributes.
Usage
data(sim.dina)
data(sim.dino)
data(sim.qmatrix)
Format
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.
Example Index
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),
References
Rupp, A. A., Templin, J. L., & Henson, R. A. (2010) Diagnostic Measurement: Theory, Methods, and Applications. New York: The Guilford Press.