scholarly journals Parameter Recovery in Multidimensional Item Response Theory Models Under Complexity and Nonnormality

2017 ◽  
Vol 41 (7) ◽  
pp. 530-544 ◽  
Author(s):  
Dubravka Svetina ◽  
Arturo Valdivia ◽  
Stephanie Underhill ◽  
Shenghai Dai ◽  
Xiaolin Wang
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Computerized Adaptive Testing ◽  
Latent Trait ◽  
Item Parameter ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Parameter Recovery ◽  
Assessment Development ◽  
Latent Traits

Information about the psychometric properties of items can be highly useful in assessment development, for example, in item response theory (IRT) applications and computerized adaptive testing. Although literature on parameter recovery in unidimensional IRT abounds, less is known about parameter recovery in multidimensional IRT (MIRT), notably when tests exhibit complex structures or when latent traits are nonnormal. The current simulation study focuses on investigation of the effects of complex item structures and the shape of examinees’ latent trait distributions on item parameter recovery in compensatory MIRT models for dichotomous items. Outcome variables included bias and root mean square error. Results indicated that when latent traits were skewed, item parameter recovery was generally adversely impacted. In addition, the presence of complexity contributed to decreases in the precision of parameter recovery, particularly for discrimination parameters along one dimension when at least one latent trait was generated as skewed.

scholarly journals Item Response Theory Models for Ipsative Tests With Multidimensional Pairwise Comparison Items

2017 ◽  
Vol 41 (8) ◽  
pp. 600-613 ◽  
Author(s):  
Wen-Chung Wang ◽  
Xue-Lan Qiu ◽  
Chia-Wen Chen ◽  
Sage Ro ◽  
Kuan-Yu Jin
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Pairwise Comparison ◽  
Forced Choice ◽  
Measurement Properties ◽  
Response Theory ◽  
Psychological Differentiation ◽  
Parameter Recovery ◽  
Latent Traits ◽  
Item Response Theory Models

There is re-emerging interest in adopting forced-choice items to address the issue of response bias in Likert-type items for noncognitive latent traits. Multidimensional pairwise comparison (MPC) items are commonly used forced-choice items. However, few studies have been aimed at developing item response theory models for MPC items owing to the challenges associated with ipsativity. Acknowledging that the absolute scales of latent traits are not identifiable in ipsative tests, this study developed a Rasch ipsative model for MPC items that has desirable measurement properties, yields a single utility value for each statement, and allows for comparing psychological differentiation between and within individuals. The simulation results showed a good parameter recovery for the new model with existing computer programs. This article provides an empirical example of an ipsative test on work style and behaviors.

scholarly journals Incorporating the Q-Matrix Into Multidimensional Item Response Theory Models

2018 ◽  
Vol 79 (4) ◽  
pp. 665-687
Author(s):  
Marcelo A. da Silva ◽  
Ren Liu ◽  
Anne C. Huggins-Manley ◽  
Jorge L. Bazán
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Multidimensional Item Response Theory ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Diagnostic Classification Models ◽  
Applied Study ◽  
Item Responses ◽  
Q Matrix ◽  
Latent Traits

Multidimensional item response theory (MIRT) models use data from individual item responses to estimate multiple latent traits of interest, making them useful in educational and psychological measurement, among other areas. When MIRT models are applied in practice, it is not uncommon to see that some items are designed to measure all latent traits while other items may only measure one or two traits. In order to facilitate a clear expression of which items measure which traits and formulate such relationships as a math function in MIRT models, we applied the concept of the Q-matrix commonly used in diagnostic classification models to MIRT models. In this study, we introduced how to incorporate a Q-matrix into an existing MIRT model, and demonstrated benefits of the proposed hybrid model through two simulation studies and an applied study. In addition, we showed the relative ease in modeling educational and psychological data through a Bayesian approach via the NUTS algorithm.

scholarly journals Assessing the physical violence component of the Revised Conflict Tactics Scales when used in heterosexual couples: an item response theory analysis

2007 ◽  
Vol 23 (1) ◽  
pp. 53-62 ◽  
Author(s):  
Michael E. Reichenheim ◽  
Ruben Klein ◽  
Claudia Leite Moraes
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Physical Violence ◽  
Latent Trait ◽  
Heterosexual Couples ◽  
Heterosexual Relationships ◽  
Response Theory ◽  
Conflict Tactics Scales ◽  
Conflict Tactics ◽  
Revised Conflict Tactics Scales

Although there are psychometric evaluations of the Revised Conflict Tactics Scales (CTS2) when applied to heterosexual relationships, none has used item response theory (IRT). To address this gap, the present paper assesses the instrument's physical violence subscale. The CTS2 was applied to 764 women who also responded for their partners. Single dimensionality assumption was corroborated. A 2-parameter logistic IRT model was used for estimating location and discriminating power of each item. Differential item functioning and item information pattern along the violence continuum were assessed. Gender differences were detected in 3 out of 12 items. Item coverage of the latent trait spectrum indicated little information at the lower ends, while plenty in the middle and upper ranges. Still, depending on gender, some item overlaps and regions with gaps could be detected. Despite some unresolved problems, the analysis shows that the items form a theoretically coherent information set across the continuum. Provided the user is aware of possible drawbacks, using the physical violence subscale of the CTS2 in heterosexual couples is still a sensible option.

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