One (Financial Well-Being) Model Fits All? Testing the Multidimensional Subjective Financial Well-Being Scale Across Nine Countries
Bibliographic record
Abstract
Abstract A multidimensional model of emerging adults’ subjective financial well-being was proposed (Sorgente and Lanz, Int Journal of Behavioral Development, 43(5), 466–478 2019). The authors also developed a 5-factor scale (the Multidimensional Subjective Financial Well-being Scale, MSFWBS) intending to measure this construct in the European context. To date, data using this instrument have been collected in nine countries: Austria, Canada, Finland, India, Italy, Portugal, Romania, Slovenia, and Turkey. In the current study, data from these countries were analysed to test the validity of this model internationally. In particular, using an international sample of 4,475 emerging adults, we collected the following kinds of validity evidence for the MSFWBS: score structure, reliability, generalizability, convergent, and criterion-related evidence. Findings suggest that the MSFWBS (1) yields valid and reliable scores, and (2) works well in individualistic and economically developed countries, producing comparable scores. Implications for researchers and practitioners are discussed.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".