Using Paradata in Electronic Business Survey Questionnaires *
Bibliographic record
Abstract
This chapter discusses the use of paradata in business surveys, illustrated with a number of examples. Paradata are data that cover any aspect of the survey process. The use of paradata in business surveys is still quite rare, unlike social surveys, although their inclusion can allow designers to improve surveys. The chapter starts with a general discussion on paradata: event log data describing individual events throughout survey processes. They are used both in process in quality management, as illustrated by a data collection control dashboard from Statistics Norway. The main focus of the chapter is on questionnaire completion paradata, such as audit trails, and login and key stroke data. These data show how respondents complete an electronic questionnaire, for example, the access dates and time spent, completion paths, buttons and functionalities used, and error messages activated. This provides information on completion behavior, data quality, and response burden. These paradata are used in two ways: (i) to monitor the fieldwork in real time and (ii) in post-field analysis to evaluate the questionnaire. Examples from Statistics Netherlands and Statistics Canada show whether an electronic questionnaire was completed as intended by the designer, how the electronic questionnaire can be improved, and what steps need to be taken to follow up non-responding businesses. It is concluded that paradata are a rich source of information, that help surveyors to minimize errors in each step of the survey process, and to make better (fact-based) design decisions.
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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 | 0.072 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.021 |
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".