Exploring the Relationship Between Financial Education, Financial Attitude, Financial Advice, and Financial Knowledge: Insights Through Financial Capabilities and Financial Well-Being
Bibliographic record
Abstract
This study analyzes the relationship between financial education, financial attitude, financial advice, financial knowledge, and behavior and its influence on financial capabilities, as well as their impact on financial well-being. The population consists of individuals over 18 years of age, who are primarily higher education students. A non-probabilistic self-selection sampling method was used, and data were collected through an electronic form on Google Forms. The design is quantitative, non-experimental, and cross-sectional. The instrument includes sections on sociodemographic profiles, financial education, financial attitudes, financial advice, financial knowledge and behavior, financial capabilities, and financial well-being using a 1 to 5 Likert scale. To ensure validity and reliability, statistical indices such as Cronbach’s alpha and McDonald’s omega were applied. Data normality was assessed, and exploratory and confirmatory factor analyses were conducted using structural equation modeling (SEM). The findings from the results of this study largely align with the existing literature regarding the relationship between financial knowledge and financial capabilities, as well as between financial capabilities and financial well-being. However, a discrepancy is observed in the hypotheses related to financial education, financial attitudes, and financial counseling, suggesting that although these factors are important, their influence may depend on other contextual elements or mediators not considered in this study. This opens the possibility for further investigation into how these factors interact in the development of financial capabilities.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".