The practice of combining multiple data sources, methods, or perspectives to build more robust research findings. Reduces reliance on any single source and increases confidence in conclusions.
Definition: The practice of combining multiple data sources, methods, or perspectives to build more robust research findings. Reduces reliance on any single source and increases confidence in conclusions.
Triangulation is the practice of combining different data sources, methods, or perspectives to build more robust findings. The term comes from navigation: using multiple reference points to determine a precise location.
No single research method is perfect. Each has blind spots:
By combining sources, you compensate for individual weaknesses. When multiple methods point to the same conclusion, your confidence increases.
Method triangulation: Using different research methods (e.g., interviews plus analytics plus surveys)
Data triangulation: Collecting data at different times, places, or from different groups
Investigator triangulation: Having multiple researchers analyze the same data
Theory triangulation: Applying different theoretical frameworks to interpret findings
The most common form in UX research is the mixed-method approach:
Neither alone tells the full story. Together, they create comprehensive, convincing insight.
A research approach that deliberately combines qualitative and quantitative methods to build a more complete picture. Qualitative explains the 'why'; quantitative measures the 'how much.'
Research focused on understanding the 'what' and 'why' through rich stories, observations, and context. Seeks depth of understanding rather than statistical measurement.
Research focused on numerical measurement with the goal of generalizing findings from a sample to a broader population. Answers 'how much,' 'how many,' and 'how often.'
Research proactively designed to investigate a specific question, with researcher-controlled participant engagement through interviews, tests, or surveys. Also called directed research.
This term is referenced in the following articles:
Transform interview transcripts and observation notes into actionable themes through systematic coding. The difference between an opinion and a finding is whether two people agree.
The most powerful insights rarely come from a single source. They emerge from the strategic partnership between UX research and Data Science, fusing deep contextual understanding with patterns identified at massive scale.
Even the most rigorous, data-driven findings are worthless if they are ignored. Moving from a data collector to a trusted strategic partner requires a fundamental shift in how you position yourself and handle resistance.
Good research is not a series of disconnected activities, it is a cohesive process that transforms business questions into actionable insights. This is the map for that journey.
Rather than a sharp divide, qualitative and quantitative research exist on a continuum. The most powerful insights come from combining both, understanding why something happens and measuring how often.