The baby and the bathwater: On the need for substantive–methodological synergy in organizational research
Bibliographic record
Abstract
Murphy (2021) argues that the field of Industrial-Organizational (I/O) Psychology needs to pay more attention to descriptive statistics ('Table 1'; e.g., M, SD, reliability, correlations) when reporting and interpreting results. We agree that authors need to present a clear and transparent description of their data and that descriptive statistics and plots can be helpful in making sense of one's data and analyses (Tay et al., 2016). Many journals already require this. Although this information can be presented in the manuscript, more details can be placed in online supplements where there are fewer space limitations (e.g., detailed presentation and discussion of descriptive statistics, missing data and outliers, plots and diagrams, conceptual issues, and computer syntax). However, we strongly disagree with the claim that 'increasing complexity and diversity of data-analytic methods in organizational research has created several problems in our field' (p. 2). This claim suffers from two important oversights: (1) it neglects the crucial role of methodological fit, or the notion that theory, methods, and analyses need to be aligned, and (2) it neglects the fact that in I/O research, most constructs are not directly observable but need to be inferred indirectly though latent variable models. We expand on both issues, using examples to illustrate that the complexity and diversity of data-analytic methods is not a threat but a blessing for I/O research (and beyond). Finally, we conclude by highlighting the need for substantive-methodological synergies to solve some of the issues raised by Murphy (2021).
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 | 0.679 | 0.701 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.022 | 0.207 |
| Scholarly communication | 0.047 | 0.117 |
| Open science | 0.011 | 0.053 |
| Research integrity | 0.032 | 0.075 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".