Data-sim {CDM} | R Documentation |

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.

[Package *CDM* version 8.2-6 Index]