Considerations for Best Practice when Conducting Qualitative Research with Deaf and Hard of Hearing (D/HH) Participants
Bibliographic record
Abstract
A review of existing research with deaf or hard of hearing students reveals a focus on academic and social outcomes utilizing predominantly quantitative methods of research. Quantitative research typically generates numerical data and the measurement of discrete variables, whereas qualitative research allows researchers to delve deeper into what cannot easily be put into numbers: lived experiences, meaning, and context. To gain a nuanced understanding of the experiences and perspectives of students who are deaf, more qualitative research is needed. Qualitative research on deaf students’ perspectives has the potential to contribute to the development of beneficial practices that will support students. This article describes considerations and best practices when conducting qualitative research with deaf participants, followed by an example of how such practices were applied in a research study on deaf students’ lived experiences of inclusion. Keywords: Qualitative research; Deaf participants; hard of hearing participants; best practices; participants’ perspectives Un examen des recherches existantes sur les élèves sourds ou malentendants révèle que l'accent est mis sur les résultats scolaires et sociaux en utilisant principalement des méthodes de recherche quantitatives. La recherche quantitative génère généralement des données numériques et la mesure de variables discrètes, alors que la recherche qualitative permet aux chercheurs d'approfondir ce qui n'est pas facilement quantifiable : les expériences vécues, la signification et le contexte. Pour parvenir à une compréhension nuancée des expériences et des perspectives des élèves sourds, il est nécessaire de mener davantage de recherches qualitatives. La recherche qualitative sur les perspectives des élèves sourds a le potentiel de contribuer au développement de pratiques bénéfiques qui soutiendront les élèves. Cet article décrit les considérations et les meilleures pratiques pour mener une recherche qualitative avec des participants sourds, suivi d'un exemple de la façon dont ces pratiques ont été appliquées dans une étude de recherche sur les expériences vécues par les élèves sourds en matière d'inclusion. Mots clés : recherche qualitative ; participants sourds ; participants malentendants ; meilleures pratiques ; perspectives des participants
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 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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".