Transform unstructured communication into replicable, valid research findings. Our consultants support PhD scholars across disciplines with rigorous content analysis methods from coding frame development to reliable interpretation of textual, visual, and multimodal data.
Convert raw text, images, or media into systematic, analyzable categories with clear operational definitions.
Choose between quantitative, qualitative, or mixed-method content analysis approaches tailored to your research questions.
Implement rigorous inter-coder agreement, stability testing, and replicability protocols for trustworthy results.
A systematic, replicable framework for content-driven doctoral research.
Define communication universe, select representative samples, and establish inclusion/exclusion criteria for content sources.
Construct category systems, define code units, pilot test, and refine operational definitions for consistent application.
Train research assistants, conduct pilot coding rounds, calculate intercoder reliability, and refine the coding scheme.
Apply coding scheme across entire corpus using manual or software-assisted methods with rigorous quality checks.
Run frequency analyses, co-occurrence tests, correspondence analysis, and interpretive thematic synthesis.
Present findings with illustrative examples, statistical summaries, and theoretically grounded discussion.
News framing analysis, social media content, advertising messages, political discourse, and audience-generated content
Brand mentions, customer reviews, annual reports, corporate communications, and competitive intelligence
Medical records, patient narratives, clinical guidelines, policy documents, and health campaign messages
Textbook analysis, curriculum documents, student essays, classroom discourse, and teaching materials
Legislative records, policy briefs, political speeches, public comments, and government reports
Images, videos, memes, infographics, advertisements, and multimedia content across platforms
Creation of reliable category systems, codebooks, unitization rules, and pilot testing protocols for consistent content classification.
Expert support with NVivo, MAXQDA, ATLAS.ti, Leximancer, KH Coder, and custom Python/R scripts for computational content analysis.
Calculation of Cohen's Kappa, Krippendorff's Alpha, percentage agreement, and advanced reliability metrics with statistical guidance.
Integration of quantitative frequency tables with qualitative exemplars, thematic interpretations, and visual data displays.