A Step-By-Step Tutorial for Performing a Moderated Mediation Analysis using PROCESS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interest in moderation and mediation models have gained momentum since the 1980s and have become widespread in numerous fields of research including clinical, social, and health psychology in addition to behavioral, educational, and organizational research. There are resources available to help the user understand an analysis of a moderated mediation using the PROCESS macro and its resultant output, however, many are in video format (e.g., YouTube) or lack detailed instructions based on real world examples. To our knowledge, there are no resources that provide a thorough yet accessible step-by-step explanation of the procedure involved in using PROCESS v4.1 to analyze and interpret a moderated mediation model using real data in SPSS v28. The aim of this guide is to address this knowledge gap. An overview of mediation, moderation, and moderated mediation models is presented followed by instructions for verifying that assumptions are respected. Finally, a procedure to analyze data using PROCESS v4.1 is presented along with an interpretation of the resultant output.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it