The Noetic Signature Inventory: 12-Factor Confirmatory Analysis
Bibliographic record
Abstract
Background The Noetic Signature Inventory (NSI) is a 44-item self-report questionnaire that evaluates people’s experiences of intuition or inner knowing. Previous research developing and validating the measure demonstrated its validity and reliability, and a 12-factor model describing the variability of noetic experiences was found. This current study aims to confirm this factor model in a new population. Methods In a cross-sectional study, 1,752 participants completed demographic information and the NSI. Confirmatory Factor Analysis was conducted on the collected data. Results Participants were 49.3 ± 14.8 years old with 16.6 ± 3.4 years of education. They hailed from 62 countries although most were from the United States, United Kingdom and Canada. The CFA results for the 12-factor model were as follows: the chi square statistic equaled 2866.65 with 836 degrees of freedom and p < .001. The model diagnostics demonstrated a very good model fit to the data. All 44 items had factor loadings above the 0.5 cutoff, ranging from 0.58 to 0.77, with an average factor loading of 0.71. Conclusions The 12-factor structure of the NSI was confirmed, supporting its potential as a valid and reliable tool for assessing noetic characteristics. However, there are limitations to consider, and further research is needed to confirm and extend the findings in diverse populations and settings. The findings contribute to our understanding of the multidimensionality of these phenomena. Future research could build upon these findings by replicating the factor structure of the NSI in other populations, incorporating objective measures, conducting longitudinal studies, exploring underlying mechanisms, and using qualitative research methods to gain a deeper understanding of inner knowing experiences.
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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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".