# Team-Based Analysis of Large-Scale Qualitative Data: Tutorial Using a Nationwide SMS Text Messaging Poll of Youth

## TL;DR

This tutorial explains how to analyze large qualitative datasets, using youth SMS poll data as an example, to help novice researchers manage short text segments effectively.

## Contribution

The paper introduces a team-based approach for analyzing large-scale qualitative data, particularly short text segments, making it accessible for novice researchers.

## Key findings

- Team-based analysis is effective for managing large qualitative datasets composed of short text segments.
- The approach is accessible and meaningful for youth and novice researchers in digital health contexts.
- The method can be applied to SMS text messaging, social media posts, and open-ended survey data.

## Abstract

With the growing use of technology in qualitative data collection and analysis, there is an opportunity to gather rich and varied perspectives to improve health and well-being. However, large-scale qualitative datasets can be difficult to manage using traditional qualitative methods, and there are few examples of the application of large-scale qualitative analysis. In the context of digital health, large qualitative datasets are increasingly made up of short text segments, which need to be analyzed differently from lengthy transcripts from interviews or focus groups. Therefore, this tutorial describes the use of traditional qualitative methods to analyze a large corpus of qualitative text data. We use examples from a nationwide SMS text messaging poll of youth to highlight the opportunities to use this team-based analysis approach, which has been accessible and meaningful to youth researchers and novice qualitative researchers. These large-scale qualitative strategies may benefit novice researchers analyzing large volumes of qualitative data and short text segments, including SMS text messaging, social media posts, medical notes, and open-ended survey questions, among others.

RR2-10.2196/resprot.8502