A hybrid Structural Equation Modeling and Fuzzy-set Qualitative Comparative Analysis framework for using limited samples to relate cycling performance to behavioral traits
Bibliographic record
Abstract
The objective of this investigation was to explore a hybrid application of Structural Equation Modelling (SEM) and Fuzzy-set Qualitative Comparative Analysis (FsQCA) to assess the impact of safety-related behavioural traits on the performance of bicyclists using a limited sample of data as a case study. These behavioural characteristics of cyclists are fundamental when specific cyclist groups are targeted for safety countermeasures. The data were collected from individuals who completed a survey and participated in bicycle simulator experiments. Each of the items in the survey aimed to describe how keen the rider is to take risks. The motivation for the study is the recognition that traditional statistical methods have limitations related to the model structure and the type of variables they can analyze, especially where sample sizes are limited. To address these limitations and to estimate complex relationships between variables, SEM can be used to assess the individual effect of each variable on the response variable(s). In the real-world context, however, a combination of variables can affect response variables. To address this issue, this study used the hybrid SEM-FsQCA approach, in which SEM was applied as the first step in analyzing the latent behavioural variables. Then, using the outputs of the previous step, FsQCA was applied to assess the effect of combinations of variables on the performance of cyclists. Based on SEM, none of the factors significantly affected performance, likely due to the low sample size. However, when applying FsQCA, it was observed that combinations of factors significantly affect performance. This hybrid approach was seen to be promising for the case study application and, in that context, to have the potential, even with smaller samples, to significantly contribute to a deep understanding of the safety-related behavioral traits, which can be used in designing targeted countermeasures to improve their safety.
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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 | 0.052 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".