Determination of Factors Affecting the Financial Literacy of University Students in Eastern Anatolia using Ordered Regression Models
Bibliographic record
Abstract
Financial literacy is a factor that has a significant effect on financial development, stabilization and the economy. This study determined the factors affecting the financial literacy levels of formal and secondary education undergraduate students at Atatürk University. The study population was formal and secondary education undergraduate students at Atatürk University. A questionnaire was sent to 1,008 students who agreed to participate in the survey in the last quarter of 2018. In the study, factors affecting the financial literacy levels of undergraduate students were determined by ordered logistic regression and ordered probit regression analysis. The ordered logistic regression model was the best according to model comparison criteria. According to the results of this model, age, class, basic science field, gender, marital status, monthly personal income, watching eco-finance news status, and economic knowledge variables were found to be factors that affected financial literacy levels. In the study, it was determined that the financial literacy levels of women, those under 25 years old, university students in science, in the fourth year and above, having a monthly personal income of ₺1251 and below, single, not watching economic and financial news and with lower economic literacy were low. This study emphasizes the need to target these groups. These groups’ financial literacy levels need to be improved.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".