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Enregistrement W243937886 · doi:10.1177/104515950301400408

Best Practices in Interviewing

2003· article· en· W243937886 sur OpenAlexaff
Marion Terry

Notice bibliographique

RevueAdult Learning · 2003
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducation Systems and Policy
Établissements canadiensUniversity of Regina
Organismes subventionnairesnon disponible
Mots-clésRespondentInterviewStakeholderGovernment (linguistics)Agency (philosophy)Qualitative researchMedical educationLiteracyPublic relationsPsychologyPedagogySociologyPolitical scienceMedicine

Résumé

récupéré en direct d'OpenAlex

In 2003, I conducted a qualitative study that examined the experiences of stakeholders connected to two community-based adult literacy programs. My 70 research participants belonged to seven stakeholder categories as follows: 37 learners (adult literacy students), 2 coordinators/instructors (head practitioners), 11 other staff (instructors and office support workers), 7 parents/significant others (learners' close relatives), 2 administrators (volunteer board members), 8 referral agents (government and community agency workers who refer learners to adult literacy programs), and 3 provincial funding agents (government employees responsible for adult literacy grants). In addition to program documents and long-answer questionnaires, the research data included 75 interviews with 58 individuals representing every stakeholder category Interviews were my richest source of information. They were also the most difficult and the most time consuming, totaling 68 hours of taped conversations, which took 584 hours to transcribe. This personal reflection uses my interviewing experiences as a means to prepare other qualitative researchers for the challenges, and the joys, of interviewing adult education stakeholders. My first challenge was to recruit program stakeholders who were willing to be interviewed. I was fortunate to have an enthusiastic provincial funding agent in my region of the province, and to have cooperative coordinators/instructors as key program informants. Not only did they actively recruit more individuals than I requested from their own programs, according to lists of respondent criteria that I provided, but they also recommended that I interview more than one provincial funding agent; they kindly provided names of agents who were most familiar with their programs. Therefore, instead of interviewing 35 stakeholders as indicated in my research proposal, I interviewed 58 altogether, each who added a new dimension to my understanding of their adult literacy programs. The support that I received from one provincial funding agent and from both coordinators/instructors was essential to my study's success. The first task of every qualitative researcher therefore should be to recruit key informants who endorse the project's means of data collection and who are committed to its research goals. I was afraid to ask respondents for permission to audiotape the interviews. I feared that they would refuse to speak in front of the microphone, or perhaps even seize the opportunity to back out of the study altogether. Thanks, I'm sure, to their preparatory conversations with their programs' coordinators/instructors, only one participant asked that I take handwritten notes instead of taping our conversation. He was a government employee referral agent whom I had expected to feel more comfortable with the interviewing process. Some respondents, of course, were reluctant to speak up at first, and a few others actually whispered throughout their interviews. Several lowered their voices or leaned way back in their chairs away from the microphone whenever they thought they might be making unpopular or unfavorable comments, and one learner periodically slipped papers over the microphone to muffle the conversation. Most respondents, however, were very cooperative and eager to have their stories heard. It is essential to reassure participants that the interviewer is the only person who will later listen to the tapes and that they will have an opportunity to review their transcripts and make any changes they wish. Given my position as a white, middle-aged female university employee, I expected that I would feel more comfortable conversing with some respondents than others and they with me. The interview guides gave me sets of questions to follow in a logical order, but I decided early in the interviewing process to engage in less formal conversations wherein I could gently probe for more detailed data and encourage respondents to share whatever information they wished to convey I therefore tried to find a common ground of experience or interest to ease into each interview, and then I let the conversation take its own course as we discussed the respondent's adult literacy program experiences. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,296
score de la tête « metaresearch » (Gemma)0,325
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,296
Score d'incertitude au seuil0,868

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,2960,325
Méta-épidémiologie (sens strict)0,0040,006
Méta-épidémiologie (sens large)0,0040,003
Bibliométrie0,0140,017
Études des sciences et des technologies0,0130,025
Communication savante0,0160,011
Science ouverte0,0100,019
Intégrité de la recherche0,0080,014
Charge utile insuffisante (le modèle a refusé de juger)0,0320,024

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,130
Tête enseignante GPT0,449
Écart entre enseignants0,319 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2003
Routes d'admission1
Résumé présentoui

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