Investigating User Estimation of Missing Data in Visual Analysis
Bibliographic record
Abstract
Missing data is a pervasive issue in real-world analytics, stemming from a multitude of factors (e.g., device malfunctions and network disruptions), making it a ubiquitous challenge in many domains. Misperception of missing data impacts decision-making and causes severe consequences. To mitigate risks from missing data and facilitate proper handling, computing methods (e.g., imputation) have been studied, which often culminate in the visual representation of data for analysts to further check. Yet, the influence of these computed representations on user judgment regarding missing data remains unclear. To study potential influencing factors and their impact on user judgment, we conducted a crowdsourcing study. We controlled 4 factors: the distribution, imputation, and visualization of missing data, and the prior knowledge of data. We compared users’ estimations of missing data with computed imputations under different combinations of these factors. Our results offer useful guidance for visualizing missing data and their imputations, which informs future studies on developing trustworthy computing methods for visual analysis of missing data.
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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 | 0.049 | 0.364 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".