Overview: Qualitative Data Analysis Using NVivo 15
created using Gemini (10/20/2025)
Categories of thematic coding using NVivo: (we will demonstrate items in green)
- Manual
- Keyword-Assisted
- Autocode Themes (Windows-only, customized TF-IDF keyword analysis)
- Autocode Wizard: Use Existing Coding Patterns (Windows only, text classification using ML model)
- AI Assistant: Suggest Child Codes
Data Enrichment:
- Autocode Sentiment (Windows only)
- Autocode Separate Speakers
Overview of Crime Content
Escaping Scam City: Season 1: Finding Ruby
Finding Ruby is a six-part immersive true-crime podcast that tells the gripping true story of a young girl’s fight for freedom in the shadows of the internet.
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Episode 1: The TrickPiece by piece, the layers of protection around Ruby are pulled away. It is into this space that the message arrives on Facebook.
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Episode 2: The TrapRuby is caught in the online sex trafficking den. The days are gruelling but she is determined to escape.
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Episode 3: The TrespassWho exactly is on the other side of the webcam? We come face to face with the kind of men exploiting Ruby.
YouTube: Phony Home Contractors Steal Over $450,000
80-year-old David lives in a tight-knit community, frequently helping his neighbors with yard care and other tasks. So when he gets a knock on the door from two brothers claiming they are working on his neighbor's home, and noticed a hole in his roof, David agrees to let them fix it. But one repair turns into many as the brothers find more and more work that needs to be done on his house. David's daughter is suspicious, and when she finds out that the work has cost her father over $450,000, she jumps into action to help catch the criminals.
YouTube: "FBI Agent" Stole My $600K...Then Vanished
A former nurse with a history of humanitarian work, Judith lost $600K in an elaborate FBI impersonation scam tied to a fake drug cartel investigation. Manipulated into liquidating her accounts, she now volunteers with AARP Fraud Watch Network™ to help protect others from falling victim like she did.
