Qualitative Data Analysis using QDA Miner Lite - Coding, Themes, Text Retrieval, and Auto Coding

Qualitative data analysis in QDA Miner Lite becomes manageable once coding is treated as an iterative workflow: define cases and variables, build codes under themes, then use text retrieval to auto-locate relevant quotations and refine the codebook as understanding sharpens. The practical payoff is speed and consistency—researchers can stop re-reading entire transcripts and instead search for keywords, synonyms, and phrase patterns, then immediately attach those hits to the right code.

The walkthrough starts with getting QDA Miner Lite installed and choosing the free edition. From there, it guides users through creating a new project from a folder of documents—specifically importing Microsoft Word files (with options also available for text, HTML, Rich Text, and PDF). Once imported, the interface organizes data into “cases” (one per document/interview) and “variables” (attributes like respondent identity, age, gender, or organization type). This structure matters because later coding and frequency analysis depend on linking coded segments back to cases and variables.

Coding then follows a clear hierarchy. Users read a document in the document pane, highlight relevant text, and assign it to a “code” that sits under a broader “theme” (category). The example shows how a sentence comparing systems (e.g., “BPS” versus “TTS”) might be coded as “system,” then placed under a theme such as “job structure.” Another excerpt about benefits is coded under a different theme (“incentives”), with additional codes like “salary” created as needed. The workflow emphasizes that themes and codes can start as drafts: a theme can be named early, then later adjusted after more coding.

As coding accumulates, the process supports refinement through merging. When a code is discovered to belong under an existing concept, QDA Miner Lite lets users merge “parallel system” into “system,” consolidating the codebook and preventing fragmentation. This iterative approach is presented as essential: codes are not fixed on day one; they evolve as patterns emerge across interviews.

To avoid manual re-reading, the guide uses “Text Retrieval.” Researchers can search uncoded or specific segments using keyword logic: OR for synonyms (e.g., “benefits” or “return”), wildcard characters for word variants (e.g., “procedur*” via an athetic wildcard), and phrase logic using AND (words that must appear together) or END (words that must appear in sequence within a unit). Search results show the number of hits across documents, and selected sentences can be coded directly into existing codes like “benefit” or “knowledge.” The workflow also includes auditing and cleanup: retrieving segments for a code to delete irrelevant quotations, moving quotations between codes, and removing duplicate coding.

Finally, the analysis layer adds structure and reporting. Coding frequencies provide counts, percentages, and descriptive statistics by code, and the interface supports multiple text views (highlighted, dimmed) and formatting controls (font color, background color). For write-up, the guide recommends a results chapter structure: introduce the chapter, list themes and their codes, define what each code and theme means, then present quotations with brief interpretive descriptions—using signposting language like “similarly,” “however,” or “contradicting”—so the results read as analysis, not transcript dumping. The session closes by positioning QDA Miner Lite as a tool for building a codebook, analyzing patterns, and producing a print-ready code book for later use.