### Abstract

Original language | Undefined |
---|---|

Place of Publication | Enschede, the Netherlands |

Publisher | University of Twente, Faculty Educational Science and Technology |

Publication status | Published - 1989 |

### Publication series

Name | OMD research report |
---|---|

Publisher | University of Twente, Faculty of Educational Science and Technology |

No. | 89-3 |

### Keywords

- Latent Trait Theory
- Test Construction
- Test Items
- Item Banks
- Simulation
- Mathematical Models
- Statistical Analysis
- Algorithms
- Measures (Individuals)
- IR-104187
- Selection

### Cite this

*Stepwise item selection procedures for Rasch scales using quasi-loglinear models*. (OMD research report; No. 89-3). Enschede, the Netherlands: University of Twente, Faculty Educational Science and Technology.

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*Stepwise item selection procedures for Rasch scales using quasi-loglinear models*. OMD research report, no. 89-3, University of Twente, Faculty Educational Science and Technology, Enschede, the Netherlands.

**Stepwise item selection procedures for Rasch scales using quasi-loglinear models.** / Knol, Dirk L.

Research output: Book/Report › Report › Other research output

TY - BOOK

T1 - Stepwise item selection procedures for Rasch scales using quasi-loglinear models

AU - Knol, Dirk L.

N1 - Project Psychometric Aspects of Item Banking No. 44

PY - 1989

Y1 - 1989

N2 - Two iterative procedures for constructing Rasch scales are presented. A log-likelihood ratio test based on a quasi-loglinear formulation of the Rasch model is given by which one item at a time can be deleted from or added to an initial item set. In the so-called "top-down" algorithm, items are stepwise deleted from a relatively large initial item set, whereas in the "bottom-up" algorithm items are stepwise added to a relatively small initial item set. Both algorithms are evaluated through a simulation study with generated data. Item parameters are given for four generated unidimensional data sets and two generated two-dimensional sets. Abilities were randomly sampled from a multivariate normal distribution with a sample size of 1,000. Results for the top-down algorithm were poor, but results for the bottom-up algorithm were more encouraging. It is suggested that alternating the bottom-up algorithm with one or two iterations of the top-down algorithm would allow the procedure to reject items that were added incorrectly in a previous step.

AB - Two iterative procedures for constructing Rasch scales are presented. A log-likelihood ratio test based on a quasi-loglinear formulation of the Rasch model is given by which one item at a time can be deleted from or added to an initial item set. In the so-called "top-down" algorithm, items are stepwise deleted from a relatively large initial item set, whereas in the "bottom-up" algorithm items are stepwise added to a relatively small initial item set. Both algorithms are evaluated through a simulation study with generated data. Item parameters are given for four generated unidimensional data sets and two generated two-dimensional sets. Abilities were randomly sampled from a multivariate normal distribution with a sample size of 1,000. Results for the top-down algorithm were poor, but results for the bottom-up algorithm were more encouraging. It is suggested that alternating the bottom-up algorithm with one or two iterations of the top-down algorithm would allow the procedure to reject items that were added incorrectly in a previous step.

KW - Latent Trait Theory

KW - Test Construction

KW - Test Items

KW - Item Banks

KW - Simulation

KW - Mathematical Models

KW - Statistical Analysis

KW - Algorithms

KW - Measures (Individuals)

KW - IR-104187

KW - Selection

M3 - Report

T3 - OMD research report

BT - Stepwise item selection procedures for Rasch scales using quasi-loglinear models

PB - University of Twente, Faculty Educational Science and Technology

CY - Enschede, the Netherlands

ER -