Concept mapping internal validity: A case of misconceived mapping?
Bibliographic record
Abstract
Since the early 1990s, the concept mapping technique developed by William M. K. Trochim has been widely used by evaluators for program development and evaluation and proven to be an invaluable tool for evaluators and program planners. The technique combines qualitative and statistical analysis and is designed to help identify and prioritize the components, dimensions, and particularities of a given reality. The aim of this paper is to propose an alternative way of conducting the statistical analysis to make the technique even more useful and the results easier to interpret. We posit that some methodological choices made at the inception stage of the technique were ill informed, producing maps of participants' points-of-view that were not optimal representations of their reality. Such a depiction resulted from the statistical analysis process by which multidimensional scaling (MDS) is being applied on the similarity matrix, followed by a hierarchical cluster analysis (HCA) on the Euclidian distances between statements as plotted on the resulting two-dimensional MDS map. As an alternative, we suggest that HCA should be performed first and MDS second, rather than the reverse. To support this proposal, we present three levels of argument: 1) a logical argument backed up by expert opinions on this issue; 2) statistical evidence of the superiority of our proposed approach and 3) the results of a social validation experiment.
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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 | 0.375 | 0.676 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.012 | 0.084 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".