LR.2step {cdcatR} | R Documentation |

## Item-level model comparison using 2LR test

### Description

This function evaluates whether the saturated G-DINA model can be replaced by reduced CDMs without significant loss in model data fit for each item using two-step likelihood ratio test (2LR). Sorrel, de la Torre, Abad, and Olea (2017) and Ma & de la Torre (2018) can be consulted for details. Conducting this type of analysis can facilitate the calibration of the item bank and have implications for the CAT accuracy and item usage (Sorrel, Abad, & Nájera, 2021).

### Usage

```
LR.2step(fit, p.adjust.method = "holm", alpha.level = 0.05)
```

### Arguments

`fit` |
Calibrated item bank with the |

`p.adjust.method` |
Scalar character. Correction method for |

`alpha.level` |
Scalar numeric. Alpha level for decision. Default is |

### Value

`LR2.step`

returns an object of class `LR2.step`

- LR2
Numeric matrix. LR2 statistics

- pvalues
Numeric matrix.

*p*-values associated with the 2LR statistics- adj.pvalues
Numeric matrix. Adjusted

*p*-values associated with the 2LR statistics- df
Numeric matrix. Degrees of freedom

- models.adj.pvalues
Character vector denoting the model selected for each item using the

*largestp*rule (Ma et al., 2016). All statistics whose*p*-values are less than`alpha.level`

are rejected. All statistics with*p*-value larger than`alpha.level`

define the set of candidate reduced models. The G-DINA model is retained if all statistics are rejected. Whenever the set includes more than one model, the model with the largest*p*-value is selected as the best model for that item

### References

Ma, W. & de la Torre, J. (2018). Category-level model selection for the sequential G-DINA model. *Journal of Educational and Behavorial Statistic, 44*, 45-77.

Ma, W. & de la Torre, J. (2020). GDINA: The generalized DINA model framework. R package version 2.7.9. Retrived from https://CRAN.R-project.org/package=GDINA

Ma, W., Iaconangelo, C., & de la Torre, J. (2016). Model similarity, model selection and attribute classification.
*Applied Psychological Measurement, 40*, 200-217.

Robitzsch, A., Kiefer, T., George, A. C., & Uenlue, A. (2020). CDM: Cognitive Diagnosis Modeling. R package version 7.5-15. https://CRAN.R-project.org/package=CDM

Sorrel, M. A., de la Torre, J., Abad, F. J., & Olea, J. (2017). Two-step likelihood ratio test for item-level model comparison in cognitive diagnosis models. *Methodology, 13*, 39-47.

Sorrel, M. A., Abad, F. J., & Nájera, P. (2021). Improving accuracy and usage by correctly selecting: The effects of model selection in cognitive diagnosis computerized adaptive testing. *Applied Psychological Measurement, 45*, 112-129.

### Examples

```
Q <- sim180DINA$simQ
dat <- sim180DINA$simdat
resGDINA <- GDINA::GDINA(dat = dat, Q = Q, model = "GDINA",verbose = FALSE)
#resCDM <- CDM::gdina(data = dat, q.matrix = Q, rule = "GDINA", progress = FALSE)
LR2.GDINA <- LR.2step(fit = resGDINA) # GDINA package
#LR2.CDM <- LR.2step(fit = resCDM) # CDM package
mean(LR2.GDINA$models.adj.pvalues[which(rowSums(Q) != 1)] ==
sim180DINA$specifications$item.bank$specifications$model[which(rowSums(Q) != 1)])
#mean(LR2.CDM$models.adj.pvalues[which(rowSums(Q) != 1)] ==
# sim180DINA$specifications$item.bank$specifications$model[which(rowSums(Q) != 1)])
```

*cdcatR*version 1.0.6 Index]