Bueno, existen diferentes formas y combinación de métodos, por ejemplo; la aplicación de SIG y análisis multivariado en las variables de intervención.

Thematic Data Analysis is a foundational method in qualitative research design that involves systematically identifying, organizing, and interpreting patterns of meaning (themes) across a dataset, such as interview transcripts, focus group discussions, or textual documents. Developed by researchers like Braun and Clarke, it emphasizes flexibility, allowing for inductive (data-driven) or deductive (theory-driven) approaches, and can be applied within various paradigms, from realist to constructionist. To use it for identifying patterns and meanings, researchers follow a six-phase process: (1) familiarizing themselves with the data through repeated reading; (2) generating initial codes to label interesting features; (3) searching for themes by collating codes into potential patterns; (4) reviewing themes for coherence and relevance; (5) defining and naming themes to capture their essence; and (6) producing a report that weaves themes into a narrative with vivid data extracts. This iterative method uncovers latent meanings, reveals relationships between ideas, and provides rich insights into participants' experiences, making it ideal for exploratory studies while ensuring rigor through reflexivity and audit trails.

The term "thematic analysis" is most commonly associated with the work of Braun and Clarke, starting with their 2006 article on this topic, which has now received over 250,000 citations on Google Scholar.

More recently, their 2022 book has further distinguished their own preferred approach, Reflexive Thematic Analysis, from other versions of TA.