Spatial anxiety: A novel questionnaire with subscales for measuring three aspects of spatial anxiety
Bibliographic record
Abstract
Spatial skills are a strong predictor of achievement and pursuit of employment in STEM fields. However, some individuals experience anxiety arising from situations that require performing spatial tasks in an evaluative context, and as a result, may avoid spatial related mental activities and exposure to spatially relevant experiences. We sought to generate and validate an instrument capable of reliably measuring individual differences in experiences of spatial anxiety. We developed a spatial anxiety data-driven approach, wherein an exploratory factor analysis was conducted within the framework for different types of spatial skills outlined by Uttal et al. (2013; https://doi.org/10.1037/a0028446). In Study 1, factor analyses revealed that items loaded on three factors that corresponded well with some of the most common spatial abilities that have been discussed in the broader literature: navigation, mental-manipulation and imagery. The three subscales were high in internal reliability and between-scale selectivity. Study 2 then established that external validity was good for the navigation and manipulation subscales: higher anxiety ratings uniquely predicted lower objective performance and lower attitude/ability ratings on established measures within the respective subdomains. External validity was acceptable for the imagery subscale, uniquely predicting lower attitude/ability ratings on an established spatial imagery questionnaire. The overall result is an empirically validated Spatial Anxiety scale for use with adults that also respects the multifaceted nature of spatial processing. This questionnaire has the potential to provide a more comprehensive screening tool for spatial anxiety, and is a step toward identifying potential barriers to STEM education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".