DNDi short film about new treatment for sleeping sickness awarded Grand Prix at inaugural WHO film festivalby The Editorial Team
Coalition launched to accelerate research on the prevention and treatment of COVID-19 in low- and middle-income countriesby The Editorial Team
This article is divided into two main parts and aims, on the one hand, at explaining the origins and chronology of the phenomenon called the "diary", and on the other hand, at the sociological aspects of its use and role in social research. It intends to shed a new light on the historical, methodological, epistemological and ethical challenges facing the social scientist when choosing to use diaries for qualitative knowledge of social life.
Analyzing longitudinal qualitative data: the application of trajectory and recurrent cross-sectional approachesby Daniel Grossoehme, Ellen Lipstein
Longitudinal qualitative research methods can add depth and understanding to health care research, especially on topics such as chronic conditions, adherence and changing health policies. In this manuscript we describe when and how to undertake two different applied approaches to analyzing longitudinal qualitative data: a recurrent cross sectional approach and a trajectory approach.
There has been considerable recent interest in methods of determining sample size for qualitative research a priori, rather than through an adaptive approach such as saturation. Extending previous literature in this area, we identify four distinct approaches to determining sample size in this way: rules of thumb, conceptual models, numerical guidelines derived from empirical studies, and statistical formulae.
Grounded Theory is used frequently and discussed often by qualitative researchers, and can be a very useful methodology for indepth analysis of data - yet it can be quite confusing for new researchers to learn about - partly because there are different variants and methods. Here, we provide a list of useful resources to help you get your head around grounded theory.
We explore the clear links between data analysis and evidence. We argue that transparency in the data analysis process is integral to determining the evidence that is generated. Data analysis must occur concurrently with data collection and comprises an ongoing process of ‘testing the fit’ between the data collected and analysis. We discuss four steps in the process of thematic data analysis: immersion, coding, categorisingand generation of themes.
This practical guide can be accessed online, and comes with Global Health Social Sciences' complete recommendation! This book provides a practical way of thinking about GT in relation to your project, taking the reader through the stages involved with developing a coding framework and generating theory from your data.