Making the Most of What We Have: A Practical Application of Multidimensional Item Response Theory in Test Scoring

2005 ◽  
Vol 30 (3) ◽  
pp. 295-311 ◽  
Author(s):  
Jimmy de la Torre ◽  
Richard J. Patz
Keyword(s):  
Item Response ◽  
Practical Method ◽  
Multidimensional Item Response Theory ◽  
Model Parameters ◽  
Practical Application ◽  
Multidimensional Item Response ◽  
Multiple Tests ◽  
Monte Carlo Techniques ◽  
Estimation Of Model Parameters ◽  
Highly Correlated

This article proposes a practical method that capitalizes on the availability of information from multiple tests measuring correlated abilities given in a single test administration. By simultaneously estimating different abilities with the use of a hierarchical Bayesian framework, more precise estimates for each ability dimension are obtained. The efficiency of the proposed method is most pronounced when highly correlated abilities are estimated from multiple short tests. Employing Markov chain Monte Carlo techniques allows for straightforward estimation of model parameters.

Reducing the Length of the Multidimensional Scale of Perceived Social Support

2020 ◽  
Vol 36 (5) ◽  
pp. 829-838 ◽  
Author(s):  
Ben Porter ◽  
Claire A. Kolaja ◽  
Teresa M. Powell ◽  
Jacqueline C. Pflieger ◽  
Valerie A. Stander ◽  
...  
Keyword(s):  
Social Support ◽  
Item Response ◽  
Undergraduate Students ◽  
Perceived Social Support ◽  
Multidimensional Item Response Theory ◽  
Multidimensional Item Response ◽  
Us Military ◽  
Survey Length ◽  
Highly Correlated ◽  
Reduced Scale

Abstract. The Multidimensional Scale of Perceived Social Support (MSPSS) is a widely used 12-item measure that assesses perceived social support from three sources: friends, family, and significant others. Previously published psychometric properties indicate that a shorter version of this scale may adequately assess perceived social support and reduce participant burden. The current studies sought to develop such a reduced scale across two studies. Study 1 examined a sample of spouses of US military personnel ( N = 5,436) randomly separated into exploratory and confirmatory samples. In the exploratory sample, we developed a 6-item reduced MSPSS using multidimensional item response theory. In the confirmatory sample, the reduced MSPSS fit the hypothesized structure and was highly correlated with the full MSPSS. Study 2 administered the full and reduced MSPSS separately within a sample of undergraduate students ( N = 188). The reduced MSPSS had high correlations with the full measure ( r = .90) and fit the hypothesized factor structure. Across both studies, correlations with related constructs were similar between the reduced and full MSPSS, demonstrating almost no loss of construct validity. Overall, the reduced MSPSS captured perceived social support with little loss of information. This reduced scale may be useful for minimizing survey length and participant burden.

A Bifactor Multidimensional Item Response Theory Model for Differential Item Functioning Analysis on Testlet-Based Items

2011 ◽  
Vol 35 (8) ◽  
pp. 604-622 ◽  
Author(s):  
Hirotaka Fukuhara ◽  
Akihito Kamata
Keyword(s):  
Item Response Theory ◽  
Differential Item Functioning ◽  
Item Response ◽  
Estimation Method ◽  
Multidimensional Item Response Theory ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Data Set ◽  
Detection Rates ◽  
Item Functioning

A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor multidimensional item response theory (MIRT) model for testlets. Unlike traditional item response theory (IRT) DIF models, the proposed model takes testlet effects into account, thus estimating DIF magnitude appropriately when a test is composed of testlets. A fully Bayesian estimation method was adopted for parameter estimation. The recovery of parameters was evaluated for the proposed DIF model. Simulation results revealed that the proposed bifactor MIRT DIF model produced better estimates of DIF magnitude and higher DIF detection rates than the traditional IRT DIF model for all simulation conditions. A real data analysis was also conducted by applying the proposed DIF model to a statewide reading assessment data set.

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