A step-by-step approach to developing scales for survey research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: While questionnaires and scales are some of the simplest methods of collecting data, their development requires a rigorous process. In recent years, several new questionnaires and scales have been developed. Although various papers have outlined how to develop questionnaires, their use in survey research, as well as how to ensure their validity and reliability, the actual development of scales - including the generation of items, scaling, the testing of validity and reliability, and refinement of the scale - is missing in the literature. AIM: To outline a systematic and rigorous process for developing scales for survey research and to differentiate between three interchangeably used terms: scale, questionnaire and inventory. DISCUSSION: Developing a valid and reliable scale is daunting because of the challenges associated with the conceptualisation, contextualisation and operationalisation of the phenomenon of interest. Researchers should use multiple approaches at each step of development to tackle these challenges. CONCLUSION: This paper provides a step-by-step approach to developing scales by providing explicit instructions and practical examples. This six-step process can enable nurse researchers to develop a scale applicable to their study's intended population, which is also valid and reliable for measuring the phenomenon of interest. IMPLICATIONS FOR PRACTICE: Rigorous nursing research demands that instruments be valid and reliable measures. Systematic development of scales is key to ensuring that nurse researchers accurately measure abstract concepts when conducting surveys with a given population. This paper is a first step in addressing the gap in the methodological literature and will contribute to greater rigour in research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it