Mixed Methods in Human Resource Development: Reviewing the Research Literature
Bibliographic record
Abstract
This paper is written with a novice social sciences researcher (management, education, public administration, public policy, and human resource development etc.) in mind at the graduate or doctoral level. A mixed methods research design has been made in this paper for a human resource development (HRD) project after extensively reviewing the research literature. This paper is useful for researchers who are looking for a mixed methods research design plan based on a real-world example that can be adapted to their specific research. The paper is based on a research titled, “Transfer of Training: A mixed methods research”. It explains a rationale for the use of mixed methods in an HRD project, followed by the research questions, the research methods and procedures. The paper also debates on sampling and data integration issues, data types, research instruments, data organization and cleaning, data analysis using software such as SPSS and NVIVO and issues of validity and reliability. The paper concludes with a discussion on limitations and delimitations.
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 | 0.124 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.026 | 0.031 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| 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; 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".