Measuring Strong, Skillful, Good and Transpersonal Will: The development of the Multidimensional Will Scale
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: This cross-sectional study aimed to provide a scale to assess different aspects of the will based on Roberto Assagioli's theory. METHODS AND RESULTS: The scale development followed three steps. Step 1 focused on operationalizing the construct and developing the items. It was carried out through several phases of item generation and refinement, resulting in a pool of 38 items. At Step 2 we tested the psychometric properties of the initial 38-item scale with the goal of excluding the items that weakened the structural validity and reliability of the scale. Descriptive, internal consistency, and exploratory factor analyses statistics were computed on a large sample (Sample 1: N = 587; age: M = 21.55, SD = 4.14, 66% female) and they led to a five-dimension model (Strong, Skillful, Good toward Self and Other, and Transpersonal Will) and the exclusion of 15 items. Analyses conducted at Step 3 on a different sample (Sample 2: N = 683; age: M = 34.09, SD = 16.27, 54% female) allowed for further refinement of the scale. Confirmatory factor analysis conducted on the resulting 19-item scale showed a good fit for the five-factor model (χ2 (142) = 507.63, p< .001, TLI = .91; CFI = .93; RMSEA = .06 [90%CI: .06‒.07]), and evidence of its invariance across genders and ages was provided. Reliability indices (internal consistency and intraclass correlation coefficients) were adequate (ranging from .66 to .83) and correlations with measures of related constructs supported the external validity of the scale. CONCLUSION: This study provides researchers, therapists, and counselors with an efficient measurement tool to assess Assagioli's construct of will.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".