Software
Features:
Coding, thematic analysis, text search, visualization, integration with SPSS
and Excel.
Supports
multimedia, social media, and survey data.
Features:
Code management, co-occurrence analysis, network visualization, team
collaboration.
Supports
documents, images, audio, and video.
Web-based;
focuses on mixed methods research.
Efficient
for collaborative research and real-time data updates.
Features:
Mixed methods integration, code mapping, visual tools, statistical analysis.
Supports
quantitative-qualitative integration and survey analysis.
Visual
interface optimized for small-scale qualitative projects.
Real-time
coding visualization and simplicity in use.
QDA
Miner Lite - Qualitative Data
Free
version of QDA Data Miner; allows importation of common file types (Office
Suite, PDF, HTML, RTF). Coding uses a tree structure. Allows for Boolean text
searches.
Computer
Aided Textual Markup and Analysis (CATMA)
Good
for projects where there s not a lot of coding or when a data dictionary is not
needed.
Offers
graphical interface for tagging and marking up text; also includes a natural language based query builder to create and run queries.
Support is available in multiple languages. Useful for data visualization
projects.
Free
tool, but limited to use with text analysis (such as
content analysis procedures). Also allows for interactivity between users.
Supports
theory building and hypothesis testing.
Integrates
with HyperTRANSCRIBE for transcription.
Specialized
for video/audio qualitative analysis.
Timeline-based
coding, ideal for discourse and conversation analysis.
GenAI (ChatGPT, Gemini, etc.)
GenAI
can support qualitative data analysis (QDA) as an assistive tool, not a
replacement for methodological rigor. Its utility lies in enhancing efficiency
and exploratory analysis through the following functions:
1.
Data Familiarization
Summarization
of large text corpora (interviews, focus groups, open-ended survey responses).
Extraction
of salient themes or recurring concepts.
Identification
of outliers or unusual responses.
2.
Coding Assistance
Suggesting initial open codes based on inductive
analysis.
Highlighting co-occurring phrases for axial coding.
Providing
semantic categorization or clustering based on meaning similarity.
3.
Thematic Analysis Support
Grouping codes into candidate themes.
Providing explanations for potential theme
interrelations.
Comparing across cases or participant groups.
4.
Pattern Recognition and Discourse Analysis
Detecting
patterns in word usage, rhetorical devices, sentiment shifts, or discourse
markers.
Suggesting frames or narrative structures in textual
data.
5.
Data Querying
Allowing researchers to pose specific questions to the
data (e.g., What reasons do
participants give for quitting their jobs? ).
6.
Visualization Preparation
Generating
input formats for word clouds, frequency tables, or code co-occurrence
matrices.
7. Memoing and Reflexivity
Assisting in the writing of analytic memos.
Prompting reflexive questions to challenge
assumptions.
8.
Cross-Linguistic Analysis
Translating
or comparing meaning across languages (with caveats regarding cultural nuance).
Limitations:
Lacks
epistemological grounding (e.g., cannot understand phenomenology,
constructivism, or critical theory).
Risk of introducing bias via language model priors.
Does
not replace interpretive depth, triangulation, or context-aware analysis.