AI is transforming qualitative research by automating data analysis, improving accuracy, and scaling insights. Discover the tools, use cases, and advantages that are reshaping how we analyze emotions, ...

Op-ed: Qualitative research helps us understand the “why” behind the numbers. Without these methods, we risk misunderstanding people — and making bad policy.

Precoding is a signal processing technique that modifies the phases and amplitudes of wireless signals to combat channel distortion and optimize the quality and reliability of data transmissions. It ...

Taking advantage of QM as the foundational theory of NMR will advance both qualitative and quantitative analysis. Current data interpretation schemes are primarily visual (peak, cross peak), ...

Thanks to the plummeting costs of continuously evolving omics analytical platforms, research centers collect multiomics data more routinely. They are, however, confronted with the lack of a versatile ...

Discover the key differences between qualitative and quantitative research. This guide explains their unique benefits, challenges, and practical applications.

In other words, AI could be used for data preprocessing and preliminary analysis, followed by in-depth qualitative analysis conducted by human researchers.

This workshop covers coding and development of themes: codes vs. themes, developing a code book, coding, establishing consensus, secondary coding, developing themes, arriving at findings, and ...

Opinion Digital twins—why qualitative data is needed to build realistic personas The better marketers understand the real people behind the synthetic data, the more customers they can reach ...

Screening oncology articles in a qualitative literature review using large language models: A comparison of GPT4 versus fine-tuned open source models using expert-annotated data.