2 edition of **use of unidimensional item parameter estimates of multidimensional items in adaptive testing** found in the catalog.

use of unidimensional item parameter estimates of multidimensional items in adaptive testing

Terry A Ackerman

- 310 Want to read
- 6 Currently reading

Published
**1988**
by American College Testing Program in Iowa City, Iowa
.

Written in English

- Item response theory

**Edition Notes**

Statement | Terry A. Ackerman |

Series | ACT research report series -- 87-13, ACT research report -- 87-13 |

Contributions | American College Testing Program |

The Physical Object | |
---|---|

Pagination | 33 p. : |

Number of Pages | 33 |

ID Numbers | |

Open Library | OL14641874M |

Several unidimensional and multidimensional item response models for use with dichotomous and polytomous response data have been advanced, IRT parameter estimation, statistical software, and goodness-of-fit procedures have been developed, and small- and large-scale applications of IRT models to every aspect of testing and assessment have. Multidimensional item response theory (MIRT) models can be employed to report subscores. Several papers have suggested this approach, although current approaches have been somewhat problematic in terms of practical application to testing programs with limited time for analysis.

Item response theory (IRT) has a number of potential advantages over classical test theory in assessing self-reported health outcomes. IRT models yield invariant item and latent trait estimates (within a linear transformation), standard errors conditional on trait level, and trait estimates anchored to item content. IRT-Based Variable-Branching Adaptive Testing Algorithm. Appendix E. Miscellanea. Linear Logistic Test Model (LLTM) Using Principal Axis for Estimating Item Discrimination. Infinite Item Discrimination Parameter Estimates. Example: NOHARM Unidimensional Calibration. An Approximate Chi-Square Statistic for NOHARM. Mixture ModelsPrice: $

This is a highly accessible, comprehensive introduction to item response theory (IRT) models and their use in various aspects of assessment/testing. The book employs a mixture of graphics and simulated data sets to ease the reader into the material and covers the basics required to obtain a solid grounding in IRT. Written in an easily accessible way that assumes little mathematical knowledge. One of the main goals of computerized adaptive testing (CAT) is to obtain precise ability estimates with a small number of items. To achieve this goal, items are selected specifically for each examinee from a large bank. The maximum item information method (MI) is wildly used to select items during the testing session.

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The Use of Unidimensional Item Parameter Estimates of Multidimensional Items in Adaptive Testing Most item response theory models assume that an examinee’s test perfor mance can be explained by a single ability or latent trait. That is, an examinee's position in the latent ability space can be determined by measuring a single ability dimension.

The Use of Unidimensional Parameter Estimates of Multidimensional Items in Adaptive Testing Show all authors. Terry A. Ackerman. Terry A. Ackerman. University of Illinois See all articles by this author.

Fitting a unidimensional model to multidimensional item response by: The Use of Unidimensional Item Parameter Estimates of Multidimensional Items in Adaptive Testing. Ackerman, Terry A. The purpose of this study was to investigate the effect of using multidimensional items in a computer adaptive test (CAT) setting which assumes a unidimensional item response theory (IRT) by: Get this from a library.

The use of unidimensional item parameter estimates of multidimensional items in adaptive testing. [Terry A Ackerman; American College Testing Program.].

Trait parameter recovery using multidimensional computerized adaptive testing in reading and mathematics. Applied Psychological Measurement, 29, McDonald, R.P.

Linear versus models in item response theory Applied Psychological Measurement, 6, McDonald, R.P. A basis for multidimensional item response by: 7. Item parameter estimates for each set of items within each form were averaged over the replications, resulting in a mean for the estimated item parameters.

In structural equation modeling (SEM), researchers need to evaluate whether item response data, which are often multidimensional, can be modeled with a unidimensional measurement model without seriously biasing the parameter estimates.

In a calibrated item bank, model fit is established, item parameter estimates are available, and items with undesired characteristics are removed (Van Groen et al. During testing, it is assumed that the item parameters have been estimated with enough precision to consider them known (Veldkamp and Van der Linden ).

Item parameter estimation. The generated response matrix was then supplied to flexMIRT2 (Cai, ) from which item parameter estimates were obtained using the default Expectation Maximization (EM) algorithm.

The default Gauss-Hermite quadrature method was invoked to obtain numeric approximation of the multidimensional integrals. number of items is small the multidimensional approach provides more accurate estimates of item parameters, while the unidimensional approach prevails if the test length is long enough.

Further, the impact of violation of the simple structure assumption is also investigated. Speciﬁcally the. We use cookies to offer you a better experience, personalize content, tailor advertising, provide social media features, and better understand the use of our services.

Item Response Theory vs. Classical Test Theory. IRT Assumptions. 1) Monotonicity – The assumption indicates that as the trait level is increasing, the probability of a correct response also increases2) Unidimensionality – The model assumes that there is one dominant latent trait being measured and that this trait is the driving force for the responses observed for each item in the measure3.

A full chapter is devoted to methods for multidimensional computerized adaptive testing. The text is appropriate for an advanced course in psychometric theory or as a reference work for those interested in applying MIRT methodology.

A working knowledge of unidimensional item response theory and matrix algebra is assumed. Abstract. In this paper we discuss complexities of measurement that can arise in a multidimensional situation.

All of the complexities that can occur in a unidimensional situation, such as polytomous response formats, item dependence effects, and the modeling of rater effects such as harshness and variability, can occur, with a correspondingly greater degree of complexity, in the.

Unidimensional definition: of or having only one dimension | Meaning, pronunciation, translations and examples. A model for testing with multidimensional items. In D. Weiss (Ed.), Proceedings of the Computerized Adaptive Testing Conference Minneapolis: University of Minnesota, Department of Psychology, Psychometric Methods Program, Google Scholar.

‘A unidimensional scale or a single dimension of a multidimensional scale should consist of a set of items that correlate well with each other.’ ‘‘Individual components may have small effects that emerge only when the components are integrated into a simple, unidimensional score,’ the researchers said.’.

This article considers potential problems that can arise in estimating a unidimensional item response theory (IRT) model when some test items are multidimensional (i.e., show a complex factorial structure).

More specifically, this study examines (1) the consequences of model misfit on IRT item parameter estimates due to unintended minor item-level multidimensionality, and (2) whether a. Item response theory (IRT) is a system of models that explains the relationship between persons and test items.

Most of the time a one-parameter (1PL), two-parameter (2PL), or three-parameter (3PL) unidimensional model is calibrated. The 1PL and 2PL models can be seen as. Building a Unidimensional Test Using Multidimensional Items. The American College Testing Program.

Assistant Vice President, Assessment Programs Area, Test Development Division, ACT, P.O. BoxIowa City, IA Sets of items that measure the same composite of abilities as defined by multidimensional item response theory are shown.

The iterative process stops if the convergence criterion is met. Usually, the convergence criterion is the largest change in an item parameter estimate and/or the ratio of the change of the approximate observed log‐likelihood [] between two consecutive iterations over the previous being smaller than predefined values (e.g., and, respectively).

Item response theory (IRT) is a latent variable modeling approach used to minimize bias and optimize the measurement power of educational and psychological tests and other psychometric ed for researchers, psychometric professionals, and advanced students, this book clearly presents both the "how-to" and the "why" of IRT.4/5(3).multidimensional item response theory has been shown to produce more accurate and efficient parameter estimates (Reckase, ).

Thus, the use of multidimensional item response theory in composite score creation may provide better composite estimates. In many achievement-testing situations it is useful or sometime required to.