Bibliographic record
Abstract
This insightful book examines all aspects of the design process and implementation of questionnaire surveys on the activities of business, public sector, and non-profit organizations. Anthony Arundel discusses how different aspects of the survey method and planned statistical analysis can constrain question design, and how these issues can be effectively resolved. Throughout this engaging yet practical book, Arundel promotes good practices for questionnaire design, sample construction, and survey delivery systems including online, postal, and verbal methods, with a focus on obtaining high-quality data in line with ethics and confidentiality requirements. Chapters include constructive advice on questionnaire design and testing, survey implementation, and data processing, analysis, and reporting, with examples of time and financial cost budgets. Considering the recent developments in survey methods, the book explores how to use web probing as a substitute for cognitive testing and examines the use of tablets and smartphones in answering questionnaires. Combining theoretical and practical insights into survey design, implementation, and data processing and analysis, this book will be essential reading for business and management scholars and students, with a particular interest in research methods and organization studies. It will also be useful for practitioners and business managers seeking to understand how to create and use surveys.
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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 | 0.028 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.098 |
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".