Bibliographic record
Abstract
The main purpose of this study is to explore various dimensions of financial well-being of individuals and households as retail investors during scenario like pandemic and economic downturns. This study uses structural equation modelling framework to develop the financial well-being index of retail investors. The index was developed using exploratory factor analysis on the first sample (475 respondents), and tested and validated using confirmatory factor analysis on the second sample (964 respondents) as the formative indicators of financial well-being. The results of exploratory factor analysis on seventeen distinct statements yielded four-factor construct of financial well-being: (a) Absorb financial shocks (AFS), (b) Meet financial goals of (MFG), (c) Financial freedom of choice (FFC), and (d) Control over finances (COF). The formative construct of financial well-being was then validated using the confirmatory factor analysis in variance based Structural Equational Modelling approach. This study provides a good source to understand the dimensions of financial well-being as a formative index. This index is good overview for the researchers and practitioners to understand how individual investor behave during turbulence time in the economy as well as in their personal lives based upon their financial independence and control over finances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".