Quantitatius

Quantitatius is a theoretical framework in data analysis that aims to bridge qualitative and quantitative research by converting qualitative data into quantitative indicators suitable for statistical analysis. It centers on designing structured coding schemes that translate non-numeric material—such as interview transcripts, open-ended survey responses, and textual media content—into numerical measures while attempting to preserve contextual meaning.

Origin and scope

The term has appeared in methodological discussions since the early 21st century and is described as a family of practices rather than a single universal method. Proponents emphasize that quantified representations enable comparisons, replication, and integration across studies, provided coding rules are transparent and reliability is demonstrated.

Core concepts and methods

Quantitatius relies on explicit coding, intercoder agreement, and validation steps. Techniques include content coding, sentiment scoring, frequency counts, and the construction of composite indicators. The resulting data can be analyzed with standard statistics, such as cross-tabulations, regression, and time-series analysis, often with weighting or multiple coders for robustness.

Applications

In social sciences, communications, market research, and user experience studies, quantitized qualitative data can reveal patterns across groups, track change over time, and support theory testing. It is frequently used alongside qualitative methods in mixed-methods designs.

Criticisms and limitations

Critics warn that quantification can obscure nuance, encode bias in coding schemes, and yield unreliable indicators if definitions are unclear. Ethical concerns arise when sensitive meaning is reduced to numbers. Proponents argue that rigorous protocols and transparency can mitigate these risks.

See also

See also: content analysis, mixed methods research, quantitative content analysis.