Large Language Models (LLMs) have shown promise in text clustering and dimensionality analysis through embeddings, yet their potential for optimization remains largely unexplored. We conducted a comprehensive simulation study to enhance the accuracy of LLM embeddings in trait mapping using Dynamic Exploratory Graph Analysis (Dynamic EGA). The simulation generated 200 items across 4 traits of...
The rapid advancement of large language models (LLMs) has enabled automated psychological scale development, yet questions remain about the correspondence between in-silica and human-gathered validation. This study examines whether structural validity metrics computed during automated item development match empirical validation results. Using AI-GENIE (Automatic Item Generation and Validation...
The construction of forced-choice questionnaires often relies on item banks with single-stimulus or Likert-type items. In its simplest form, items must be paired to create a desired number of blocks. A key challenge in this process is pairing items while accounting for factors such as item polarity and social desirability, which can impact the quality of the measures. Recent combinatorial...
Parallel to the development of new technologies, computational language models have emerged as automated tools for analyzing semantic relationships between linguistic units. Due to their success in performing human-like tasks, such as vocabulary tests and sentiment analysis, interest in the practical applications of these models has grown exponentially, resulting in the development of larger...
