Thematic Analysis

Acknowledgments
Introduction
	1.1	Overview: Word Association Detection, Contextualization and Thematic Analysis
	1.2	WAA and WATA Examples
	1.3	Research Philosophy: Mixed Methods
	1.4	Comparison with Traditional Social Research Methods
	1.5	 Software: Mozdeh
	1.6	Language and International Considerations
	1.7	Using this Book
Data Collection with Mozdeh
	2.1	Sample Size
	2.2	Data Collection Methods
		2.2.1	Query-Based Post Collection (Twitter)
		2.2.2	User-Based Post Collection (Twitter, YouTube)
		2.2.3	User Profile Bio Collection (Twitter)
		2.2.4	Other Methods to Identify a Set of Texts for Analysis
	2.3	Mozdeh Data Collection Procedures
		2.3.1	Query-Based Post Collection in Mozdeh
		2.3.2	User-Based Post Collection with Mozdeh
		2.3.3	User Profile Bio Collection with Mozdeh
		2.3.4	Importing Text Collections (Academic Publications, Other) into Mozdeh
	2.4	Filtering the Initial Dataset
	2.5	Summary
Word Association Detection: Term Identification
	3.1	Word Association Detection: Subset vs. the Rest
		3.1.1	Choice of Comparator Set
		3.1.2	Word Association Detection with Mozdeh
	3.2	Word Association Detection: Subset A vs. Subset B
	3.3	Statistical Details
		3.3.1	Theoretical Assumptions
		3.3.2	Effect Sizes
	3.4	Protection Against Accidentally Finding Irrelevant Words
	3.5	Sample Size Requirements
	3.6	Limitations
	3.7	Summary
Word Association Contextualization: Term Meaning and Context
	4.1	Word Association Contextualization from a Random Sample of Relevant Texts
		4.1.1	Word Association Contextualization Overview
		4.1.2	Creating the WAC Text Sample with Mozdeh
		4.1.3	Detecting Typical Meanings for Polysemous Words and Words in Expressions
		4.1.4	Identifying Word Contexts
		4.1.5	Difficult Cases: Pronouns and Stylistic Words
		4.1.6	Random Sample Summary
	4.2	Advanced Word Association Contextualization
		4.2.1	Follow-Up Word Association Detection for the Term
		4.2.2	Follow-Up Word Association Detection Within Texts Containing the Term
	4.3	Word Association Contextualization Test Assumptions
	4.4	Causes of Word Associations: News, Viral Sharing, or Societal Differences?
	4.5	Using Multiple Coders
	4.6	Reporting WAA
	4.7	Summary
Word Association Thematic Analysis: Theme Detection
	5.1	Assigning Themes to Words
	5.2	WATA Procedure
	5.3	WATA Visual and Performing Arts Example
	5.4	Using Multiple Coders
	5.5	Reporting WATA
		5.5.1	Theme Prevalence Information
		5.5.2	Discussing the Results
	5.6	Summary
Word Association Thematic Analysis Examples
	6.1	Gender Differences in Early Covid-19 Wweeting
	6.2	Gender Differences in Museum Comments on YouTube
	6.3	Gender Differences in AcademicPublishing
	6.4	International Differences in Academic Nursing Publishing
	6.5	Summary
Comparison Between WATA and Other Methods
	7.1	Content Analysis
	7.2	Thematic Analysis
	7.3	Word Clouds
	7.4	Top Hashtags
	7.5	Document Clustering
	7.6	Document Set Mapping
	7.7	Topic Modeling
	7.8	Text/Semantic Networks
	7.9	Natural Language Pprocessing and Pre-Processing Possibilities
	7.10	 Corpus-Assisted Critical Discourse Analysis
	7.11	Summary
Ethics
	8.1 	Is Permission Required to Analyze public texts?
	8.2 	Can Posters Be Named or Quoted?
	8.3 	Summary
Project Planning
Summary
References
Author Biography
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