Manual Versus Online Card Sorts: A Cautionary Tale for IS Researchers.
Bibliographic record
Abstract
Card sorting is an important and frequently used survey validation technique for information systems (IS) research. A card sort can be time consuming and thus, with the advent of technology, card sorting has been automated with a number of online card sorting programs available for use by researchers. Since the early 1990s, there have been numerous studies that compared online to manual card sorts with most reporting that manual and online card sorts yield similar results. This study found that observation may impact the efficacy between online and manual card sorts thereby casting doubt into the interchangeability of manual and online card sorts. Specifically we find a significant difference between the results of a manual observed card sort and an online unobserved card sort. With the proliferation of card sorts as a construct validation technique, this paper is a caution for IS researchers to be mindful of the design of their cards sort and to be consistent in the delivery of them.
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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 | 0.267 | 0.568 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".