A Review of Methodological and Measurement Approaches to the Study of Work and Family
Bibliographic record
Abstract
This chapter provides a review of research methods reported in work–family (WF) articles published in peer-reviewed journals between 2004 and 2013. Methodological issues addressed include sampling (sampling methods, identification of target and source populations, response rate, and comparison of sample to source population), research designs (time horizon, laboratory vs. field setting, and level of control), data collection methods, levels of analysis, use of multiple data sources, triangulation, and the use of objective outcome measures. When possible, statistical comparisons were made between the results of this review and those reported in an earlier review by Casper, Eby, Bordeaux, Lockwood, and Lambert (2007). Results show that multiwave as well as qualitative research designs have been used more frequently since the period reviewed by Casper and colleagues. Still, there is room for improvement in the methodological rigor with which WF research is undertaken. In particular, WF scholars are encouraged to give more attention to sampling-related considerations, and to more strongly consider the use of experimental research designs, data/measurement triangulation, and the collection of data beyond the individual level of analysis.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".