Critical appraisal and selection of data collection instruments: A step-by-step guide
Bibliographic record
Abstract
It is essential that nurse researchers use the most precise and valid data collection instruments available to obtain trustworthy data when conducting research in education and practice. Today, there is a vast selection of existing quantitative data collection instruments from which to choose. Existing instruments can be located through reports of their use in the literature and at conferences, through internet searches and by word of mouth. Once the nurse researcher locates a potential data collection instrument for a given study, the instrument must be systematically appraised for use in that study. This article introduces a comprehensive Step-by-Step Guide that will enable users to quickly and thoughtfully appraise quantitative measurement instruments. The results from the use of this critical appraisal guide will assist researchers to objectively discuss, compare and make informed decisions before adopting a specific data collection instrument for use in a research study. The underlying principles of the Step-by-Step Guide for the Critical Appraisal and Selection of Data Collection Instruments are based on the tenets of measurement theory, literature, and experience of the authors in education and practice 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.
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 | 0.244 | 0.417 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.026 | 0.017 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".