RESEARCH METHODOLOGIES IN ENGINEERING SCIENCES: A CRITICAL ANALYSIS
Bibliographic record
Abstract
This study evaluates the research methodologies utilized in the engineering sciences. The primary goal is to identify best practices in research design, data acquisition, and result interpretation to suggest future engineering research. A systematic literature review evaluated the methodological approaches and trends of studies published over the past decade. The study's methodology included article selection based on predefined criteria, data extraction on research design, data collection and analysis methods, and result communication. The results revealed a variety of approaches and techniques, with quantitative research predominating, although an increase in the use of qualitative and blended methods was also noted. There were identified trends in research design, data acquisition and analysis, and communication of results that reflect the evolution and requirements of the engineering field. This study concludes by emphasizing the significance of understanding and employing various approaches and techniques in engineering research to address the field's complex and interdisciplinary problems effectively. By promoting methodological diversity and adopting best practices in research design, data collection and analysis, and result communication, engineering researchers and professionals can improve the quality and impact of their research and make significant contributions to advancing engineering knowledge and problem-solving.
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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 | 0.277 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.034 | 0.020 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".