The unique correspondence of the item response function and item category response functions in polytomously scored item response models

Psychometrika ◽  
1994 ◽  
Vol 59 (3) ◽  
pp. 391-404 ◽  
Author(s):  
Hua-Hua Chang ◽  
John Mazzeo
Keyword(s):  
Item Response ◽  
Response Function ◽  
Response Functions ◽  
Response Models ◽  
Item Response Models ◽  
Item Response Function

When Do Item Response Function and Mantel-Haenszel Definitions of Differential Item Functioning Coincide?

1990 ◽  
Vol 15 (3) ◽  
pp. 185-197 ◽  
Author(s):  
Rebecca Zwick
Keyword(s):  
Differential Item Functioning ◽  
Item Response ◽  
Response Function ◽  
Response Functions ◽  
Local Independence ◽  
Matching Criterion ◽  
Demographic Groups ◽  
Item Functioning ◽  
Item Response Function ◽  
The Rasch Model

A test item is typically considered free of differential item functioning (DIF) if its item response function is the same across demographic groups. A popular means of testing for DIF is the Mantel-Haenszel (MH) approach. Holland and Thayer (1988) showed that, under the Rasch model, identity of item response functions across demographic groups implies that the MH null hypothesis will be satisfied when the MH matching variable is test score, including the studied item. This result, however, cannot be generalized to the class of items for which item response functions are monotonic and local independence holds. Suppose that all item response functions are identical across groups, but the ability distributions for the two groups are stochastically ordered. In general, the population MH result will show DIF favoring the higher group on some items and the lower group on others. If the studied item is excluded from the matching criterion under these conditions, the population MH result will always show DIF favoring the higher group.

scholarly journals Scale Alignment in the Between-Item Multidimensional Partial Credit Model

2021 ◽  
pp. 014662162110131
Author(s):  
Leah Feuerstahler ◽  
Mark Wilson
Keyword(s):  
Item Response ◽  
Kindergarten Readiness ◽  
Latent Trait ◽  
Individual Development ◽  
Partial Credit Model ◽  
Partial Credit ◽  
Response Models ◽  
Item Response Models ◽  
Item Parameters ◽  
Polytomous Item Response

In between-item multidimensional item response models, it is often desirable to compare individual latent trait estimates across dimensions. These comparisons are only justified if the model dimensions are scaled relative to each other. Traditionally, this scaling is done using approaches such as standardization—fixing the latent mean and standard deviation to 0 and 1 for all dimensions. However, approaches such as standardization do not guarantee that Rasch model properties hold across dimensions. Specifically, for between-item multidimensional Rasch family models, the unique ordering of items holds within dimensions, but not across dimensions. Previously, Feuerstahler and Wilson described the concept of scale alignment, which aims to enforce the unique ordering of items across dimensions by linearly transforming item parameters within dimensions. In this article, we extend the concept of scale alignment to the between-item multidimensional partial credit model and to models fit using incomplete data. We illustrate this method in the context of the Kindergarten Individual Development Survey (KIDS), a multidimensional survey of kindergarten readiness used in the state of Illinois. We also present simulation results that demonstrate the effectiveness of scale alignment in the context of polytomous item response models and missing data.

Stochastic Approximation Methods for Latent Regression Item Response Models

2010 ◽  
Vol 35 (2) ◽  
pp. 174-193 ◽  
Author(s):  
Matthias von Davier ◽  
Sandip Sinharay
Keyword(s):  
Item Response ◽  
Stochastic Approximation ◽  
Latent Variable ◽  
Reading Assessment ◽  
Mathematics Assessment ◽  
National Assessment ◽  
Variable Model ◽  
Response Models ◽  
Item Response Models ◽  
Latent Regression

This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates serving as predictors of the conditional distribution of ability. Applications to estimating latent regression models for National Assessment of Educational Progress (NAEP) data from the 2000 Grade 4 mathematics assessment and the Grade 8 reading assessment from 2002 are presented and results of the proposed method are compared to results obtained using current operational procedures.

Mixture Item Response Models for Inattentive Responding Behavior

2017 ◽  
Vol 21 (1) ◽  
pp. 197-225 ◽  
Author(s):  
Kuan-Yu Jin ◽  
Hui-Fang Chen ◽  
Wen-Chung Wang
Keyword(s):  
Item Response ◽  
Response Models ◽  
Item Response Models

Supervised item response models for informative prediction

2016 ◽  
Vol 51 (1) ◽  
pp. 235-257 ◽  
Author(s):  
Tsuyoshi Idé ◽  
Amit Dhurandhar
Keyword(s):  
Item Response ◽  
Response Models ◽  
Item Response Models
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