'Royal College of Obstetricians & Gynaecologists (RCOG)'
Abstract
Approaches to synthesizing qualitative data have, to date, largely focused on
integrating the findings from published reports. However, developments in text
mining software offer the potential for efficient analysis of large pooled primary
qualitative datasets. This case study aimed to (a) provide a step‐by‐step guide to
using one software application, Leximancer, and (b) interrogate opportunities
and limitations of the software for qualitative data synthesis. We applied
Leximancer v4.5 to a pool of five qualitative, UK‐based studies on transportation
such as walking, cycling, and driving, and displayed the findings of the
automated content analysis as intertopic distance maps. Leximancer enabled
us to “zoom out” to familiarize ourselves with, and gain a broad perspective
of, the pooled data. It indicated which studies clustered around dominant topics
such as “people.” The software also enabled us to “zoom in” to narrow the
perspective to specific subgroups and lines of enquiry. For example, “people”
featured in men's and women's narratives but were talked about differently,
with men mentioning “kids” and “old,” whereas women mentioned “things”
and “stuff.” The approach provided us with a fresh lens for the initial inductive
step in the analysis process and could guide further exploration. The limitations
of using Leximancer were the substantial data preparation time involved and the
contextual knowledge required from the researcher to turn lines of inquiry into
meaningful insights. In summary, Leximancer is a useful tool for contributing
to qualitative data synthesis, facilitating comprehensive and transparent data
coding but can only inform, not replace, researcher‐led interpretive work