Semiautomated text analytics for qualitative data synthesis.

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.Wellcome Trus

Conversations between carers and people with schizophrenia: A qualitative analysis using leximancer

We examined conversations between people with schizophrenia (PwS) and family or professional carers with whom they interacted frequently. We allocated PwS to one of two communication profiles: Low-activity communicators talked much less than their conversational partners, whereas high-activity communicators talked much more. We used Leximancer text analytics software to analyze the conversations. We found that carers used different strategies to accommodate to the PwS's behavior, depending on the PwS's communication profile and their relationship. These findings indicate that optimal communication strategies depend on the PwS's conversational tendencies and the relationship context. They also suggest new opportunities for qualitative assessment via intelligent text analytics technologies. © 2010 The Author(s)