Establishing Trustworthiness Through Algorithmic Approaches to Qualitative Research

Ha Nguyen, June Ahn, Ashley Belgrave, Jiwon Lee, Lora Cawelti, Ha Eun Kim, Yenda Prado, Rossella Santagata, Adriana Villavicencio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Establishing trustworthiness is a fundamental component of qualitative research. In the following paper, we document how combining natural language processing (NLP), with human analysis by researchers, can help analysts develop insights from qualitative data and establish trustworthiness for the analysis process. We document the affordances of such an approach to strengthen three specific aspects of trustworthiness in qualitative research: credibility, dependability, and confirmability. We illustrate this workflow and shed light on its implications for trustworthiness from our own, recent research study of educators’ experiences with the 2020 COVID-19 pandemic; a context that compelled our research team to analyze our data efficiently to best aid the community, but also establish rigor and trustworthiness of our process.

Original languageEnglish (US)
Title of host publicationAdvances in Quantitative Ethnography - Second International Conference, ICQE 2020, Proceedings
EditorsAndrew R. Ruis, Seung B. Lee
PublisherSpringer Science and Business Media Deutschland GmbH
Pages47-61
Number of pages15
ISBN (Print)9783030677879
DOIs
StatePublished - 2021
Event2nd International Conference on Quantitative Ethnography, ICQE 2020 - Malibu, United States
Duration: Feb 1 2021Feb 3 2021

Publication series

NameCommunications in Computer and Information Science
Volume1312
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference2nd International Conference on Quantitative Ethnography, ICQE 2020
CountryUnited States
CityMalibu
Period2/1/212/3/21

Keywords

  • Natural language processing
  • Qualitative research
  • Trustworthiness

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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