Marketing or methodology? Exposing the fallacies of PLS with simple demonstrations
Bibliographic record
Abstract
Purpose Over the past 20 years, partial least squares (PLS) has become a popular method in marketing research. At the same time, several methodological studies have demonstrated problems with the technique but have had little impact on its use in marketing research practice. This study aims to present some of these criticisms in a reader-friendly way for non-methodologists. Design/methodology/approach Key critiques of PLS are summarized and demonstrated using existing data sets in easily replicated ways. Recommendations are made for assessing whether PLS is a useful method for a given research problem. Findings PLS is fundamentally just a way of constructing scale scores for regression. PLS provides no clear benefits for marketing researchers and has disadvantages that are features of the original design and cannot be solved within the PLS framework itself. Unweighted sums of item scores provide a more robust way of creating scale scores. Research limitations/implications The findings strongly suggest that researchers abandon the use of PLS in typical marketing studies. Practical implications This paper provides concrete examples and techniques to practicing marketing and social science researchers regarding how to incorporate composites into their work, and how to make decisions regarding such. Originality/value This work presents a novel perspective on PLS critiques by showing how researchers can use their own data to assess whether PLS (or another composite method) can provide any advantage over simple sum scores. A composite equivalence index is introduced for this purpose.
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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 | 0.111 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.038 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".