Spatial anxiety: A novel questionnaire with subscales for measuring three aspects of spatial anxiety
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it