scholarly journals Measuring Intelligence with the Sandia Matrices: Psychometric Review and Recommendations for Free Raven-Like Item Sets

2020 ◽  
Vol 6 (3) ◽  
Author(s):  
Alexandra Harris ◽  
Jeremiah McMillan ◽  
Benjamin Listyg ◽  
Laura Matzen ◽  
Nathan Carter
Keyword(s):  
Item Response ◽  
Multidimensional Item Response Theory ◽  
Future Research ◽  
Model Data ◽  
Multidimensional Item Response ◽  
Static Tests ◽  
Computer Adaptive Test ◽  
Show Evidence ◽  
Raven's Progressive Matrices ◽  
Mean Differences

The Sandia Matrices are a free alternative to the Raven’s Progressive Matrices (RPMs). This study offers a psychometric review of Sandia Matrices items focused on two of the most commonly investigated issues regarding the RPMs: (a) dimensionality and (b) sex differences. Model-data fit of three alternative factor structures are compared using confirmatory multidimensional item response theory (IRT) analyses, and measurement equivalence analyses are conducted to evaluate potential sex bias. Although results are somewhat inconclusive regarding factor structure, results do not show evidence of bias or mean differences by sex. Finally, although the Sandia Matrices software can generate infinite items, editing and validating items may be infeasible for many researchers. To aide implementation of the Sandia Matrices, we provide scoring materials for two brief static tests and a computer adaptive test. Implications and suggestions for future research using the Sandia Matrices are discussed.

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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