Open AccessDirected Qualitative Content Analysis (DQlCA): A Tool for Conflict Analysis
Author(s) -
Kibiswa,
K Naupess
Publication year - 2019
Publication title -
the qualitative report
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 35
ISSN - 2160-3715
DOI - 10.46743/2160-3715/2019.3778
Subject(s) - qualitative research , content analysis , grounded theory , qualitative analysis , aka , set (abstract data type) , qualitative property , computer science , epistemology , sociology , social science , library science , philosophy , machine learning , programming language
Qualitative Content Analysis (QlCA) is a research methodology carried on in either an inductive or deductive way. The former way is widely used by qualitative researchers and is more presented in qualitative research manuals than the latter is. While in the inductive approach, the researcher draws categories/themes from data she collected to start her research, in the deductive, aka, directed approach, she rather draws them from (an) existing theory/ies to set up the categories/themes that guide her research. The deductive or directed qualitative content analysis (DQlCA) is used to test, to corroborate the pertinence of the theory/ies guiding the study or to extend the application of the theory/ies to contexts/cultures other than those in which that/those theory/ies was/were developed. It is more used by quantitative researchers than by qualitative ones. And while using it, these create their data. This article aims at reducing the above holes in the qualitative research tradition by proposing an 8-step DQlCA within three phases (Study Preparation, Data Analysis, and Results’ Reporting) to respond to the same purposes with data not created by the researcher. Some appendixes provide, in tables/displays, illustrations to serve as models to inspire conflict analyst researchers who choose DQlCA as their research methodology.