Mental rotation is an important aspect of spatial ability. While the importance of measuring mental rotation has been explored, disputes still exist within the literature surrounding sources of item difficulty in mental rotation tests (MRTs). Furthermore, gender differences in MRT performance are often seen but not fully understood. In the current study, we analyzed sources of item difficulty in a set of spatial ability test items using the Linear Logistic Test Model (LLTM). We found that items with more cubes, color differences, and higher rotational complexity tend to be more difficult, whereas items that contain occlusion, a mirrored structure, and a homogenous configuration type tend to be easier. Next, using Differential Component Functioning (DCF) analysis, we analyzed gender differences across these different item characteristics, finding that the number of cubes and color characteristics made questions more difficult for males when compared to females. The results and implications of this study are discussed in further detail.

Spatial reasoning ability plays a critical role in predicting academic outcomes, particularly in STEM (science, technology, engineering, and mathematics) education. According to the Cattell–Horn–Carroll (CHC) theory of human intelligence, spatial reasoning is a general ability including various specific attributes. However, most spatial assessments focus on testing one specific spatial attribute or a limited set (e.g., visualization, rotation, etc.), rather than general spatial ability. To address this limitation, we created a mixed spatial test that includes mental rotation, object assembly, and isometric perception subtests to evaluate both general spatial ability and specific attributes. To understand the complex relationship between general spatial ability and mastery of specific attributes, we used a higher-order linear logistic model (HO-LLM), which is designed to simultaneously estimate high-order ability and sub-attributes. Additionally, this study compares four spatial ability classification frameworks using each to construct Q-matrices that define the relationships between test items and spatial reasoning attributes within the HO-LLM framework. Our findings indicate that HO-LLMs improve model fit and show distinct patterns of attribute mastery, highlighting which spatial attributes contribute most to general spatial ability. The results suggest that higher-order LLMs can offer a deeper and more interpretable assessment of spatial ability and support tailored training by identifying areas of strength and weakness in individual learners.

The Rasch Tree model for differential item functioning (DIF) detection is a useful tool that uses exploratory yet interpretable methods to investigate intersectional DIF. Recent research has explored Rasch Tree performance under specific data conditions. The present research expands upon this by examining the effect of Rasch model misspecifications (i.e., misfit) on Rasch Tree estimation performance. Using a Monte Carlo simulation, we test the effects of including discrimination and pseudo-guessing parameters in the data generation process on DIF detection accuracy, DIF magnitude estimates, and resulting Rasch Tree structure. Results indicate that imposing the Rasch model on data that contain varied item discriminations and guessing behavior leads to a decrease in DIF detection accuracy and stability. In addition, we also assess these effects under different sample sizes and test lengths and examine the Rasch Tree’s ability to distinguish between person ability differences and item level DIF under various conditions. We discuss implications and future directions from this research.

The Rasch Tree model uses model-based recursive partitioning to detect differential item functioning (DIF) across combinations of covariates. This intersectional approach is a powerful exploratory tool, but it has limitations when incorporating unordered polytomous covariates (e.g. race), particularly surrounding interpretability and computational efficiency. As a result, many studies restrict Rasch Tree analyses to contain only dichotomous or continuous covariates. Recent work has proposed collapsing polytomous covariates into fewer categories prior to model fitting, but the effects of this approach have not been assessed. The present study evaluates the impact of collapsing polytomous covariates on Rasch Tree performance using both simulations and empirical data. We compare DIF detection accuracy and model complexity under the default partitioning algorithm and an alternative approach based on k -means clustering. Results indicate that clustering reduces sensitivity and overall DIF detection accuracy. In addition, detection accuracy for polytomous covariates remains low even under the default procedure, highlighting the need for improved methods for incorporating such covariates in Rasch Tree models.