The Scale Assessment and Validation of Microfinance Services and Household Socioeconomic Model: Using Parallel and Exploratory Factor Analyses
Bibliographic record
Abstract
Households’ socio-economic outcomes are considered a significant component of sustainable development. In this regard, public and private organizations are constantly devising several microfinance and development strategies to tackle economic deprivation. There exists a conflict in the literature regarding the role of such a strategy in enhancing households’ socio-economic outcomes. In addition, researchers are also struggling to identify crucial factors that could improve poor households’ socio-economic performance. However, the lack of established measuring instruments for various microfinance and households’ socio-economic factors is the major hurdle in conducting quality research. Hence, this study intends to develop and validate measurements for microfinance and households’ socio-economic model. By employing an Exploratory Factor Analysis (EFA) and Parallel Analysis as factor retention techniques, this research provides measuring constructs for microfinance financial services, business coaching, training programs, microfinance institutions’ efficiency, households’ entrepreneurial competencies, households’ financial management practices, and households’ socio-economic performance. Based on the results, it is proved that the developed instrument of the microfinance and households’ economic model is valid and reliable to be used in future related research.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".