We investigate between-user drivers of query variability through a controlled between-subject, full-factorial user study that manipulates age, gender, and language proficiency across six backstory-driven search tasks. From initial queries, session logs, and post-task interviews, we quantify how demographic and task factors shape query-, task-, and session-level behaviors. We further derive a small set of interpretable latent search dimensions from user evidence to analyze and simulate heterogeneous query behavior. Our results show that age is the most consistent predictor of query formulation and search interaction patterns. Gender and language differences are more selective, and task context is further associated with these patterns. The latent dimensions help explain the differences as variation in search strategy rather than uniform differences in engagement or ability. The paper provides a trait-informed view of heterogeneous search behavior that supports more user-aware analysis and robustness-oriented evaluation in IR.

Formulating queries is a challenging stage of the search process. This study investigates how crowd workers formulate an initial query for a common information need described in a backstory, resulting in diverse query variations. Using the UQV100 dataset of information need backstories and corresponding queries, we analyze the variations. Our findings show that 70% of the query terms used in crowd worker queries did not appear in the backstory text. Examining such terms we developed a taxonomy of search strategies, with the most common being semantic variations of backstory terms, followed by information type specifications. Additionally, we categorized the backstories by cognitive complexity, showing that higher complexity led to greater diversity in query variations and a wider range of term variant categories. This study highlights the importance of accounting for query variations, term variants, user strategies, and cognitive complexity in designing search systems and test collections to better align with users' information needs, influenced by the cognitive demands of a task, and enhance system performance and usability.