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Quantitative Data Analysis Patrick Callaghan and Penny Bee Chapter overview Quantitative data analysis makes sense of numerical data. We often refer to quantitative data analysis as statistical analysis, and you may see this term used in published research papers. We can use numbers to summarise the experiences or characteristics of a group of participants, for example their average age or the number of symptoms they report. We can also use numbers to look at people’s behaviours, experiences and views, for example the number of people using mental health
Chapter 8: Introduction to Qualitative Data Analysis Helen Brooks, Penny Bee and Anne Rogers Chapter overview Qualitative data includes a range of textual (e.g. transcripts of interviews and focus groups) and visual (photographic and video) data. During qualitative analysis researchers make sense of this data gathered from research. Analysing the data by looking for common themes (known as thematic analysis) is one of the most common ways in which to do this and involves examining and recording patterns within the data relating to a specific research question
2 Making effective participatory environmental health science through collaborative data analysis Barbara L. Allen Introduction Recent politics has amplified, albeit in stark terms, some simmering issues with the frame of participatory science. For example, when claims of environmental injustice are raised, citizen groups often produce a different set of data from that used by industry or the state to back up their assertions – “alternative facts,” if you will, to borrow a term from the contemporary political arena. This is part of epistemic modernization (Hess
competing interests. The salary of the first author is covered partially by UK Research and Innovation (UKRI) as part of the Global Challenges Research Fund (GCRF); Research for Health in Conflict in the Middle East and North Africa (R4HC-MENA) project, grant number ES/P010962/1. Authors’ Contributions Abdulkarim Ekzayez carried out the study design, conceptual framework, data analysis, literature search, first draft of the paper, multiple rounds of edits and produced the final manuscript. Ammar Sabouni contributed a substantial amount of content, added further
about numbers produced by artificial intelligence and remote sensing. This dream has been somewhat validated with spending on big data, machine learning, digital tools and data analysis over the past decade. But the hype for digital data production has concealed the fact that the production of numbers is still a labour-intensive process. The reality is that most numbers are still produced by institutions – configurations made up of labour, materials, technology, hierarchies
This handbook is written for patients and members of the public who want to understand more about the approaches, methods and language used by health-services researchers. Patient and public involvement (PPI) in research is now a requirement of most major health-research programmes, and this book is designed to equip these individuals with the knowledge and skills necessary for meaningful participation. Edited by award-winning mental-health researchers, the book has been produced in partnership with mental-health-service users and carers with experience of research involvement. It includes personal reflections from these individuals alongside detailed information on quantitative, qualitative and health-economics research methods, and comprehensively covers all the basics needed for large-scale health research projects: systematic reviews; research design and analysis using both qualitative and quantitative approaches; health economics; research ethics; impact and dissemination. This book was developed during a five-year research programme funded by the UK’s National Institute for Health Research (NIHR) called Enhancing the Quality of User Involved Care Planning in Mental Health Services (EQUIP). The handbook clearly outlines research practices, and gives an insight into how public and patient representatives can be involved in them and shape decisions. Each chapter ends with a reflective exercise, and there are also some suggested sources of additional reading. People who get involved in health research as experts from experience now have a textbook to support their research involvement journey.
practices would therefore be insufficient to explain the current interest in digital data analysis both outside and within anthropology. To do this we must turn to the current configuration which the chapters of this book elaborate on that combines both the production and the use of new digital data sources, and the form that ethnographic practice takes within anthropology today. Digital anthropology For the past two decades, the main debates about computers within anthropology have come under the umbrella of what is now known now as ‘digital anthropology’. The aim of
Research dissemination and impact Helen Brooks and Penny Bee Chapter overview Research activity does not finish when data analysis is complete. Once research findings are available, researchers still have obligations to fulfil. These obligations include sharing the findings with different audiences and ensuring maximum impact from the study. A Research Handbook for Patient and Public Involvement Researchers Chapter 10: The process of sharing research learning with others can be an enjoyable but challenging one. Often it is referred to as dissemination, but
where textbooks are saying that ninety per cent of your time is going to be spent on one thing, but then focus all of their effort on something else. Data science is in this funny space. Duncan had started his career in the Statistics and Data Mining group at Bell Labs, where exploratory data analysis (EDA) and figuring out what the real problem was and what data matters was how data scientists spent their time. That was a place where these skills were as important as algorithm-tuning. Duncan was one of core developers of the statistical computing language R, designed
-mentioned disciplines influenced it. 20 STUDYING SOCIAL ACTION IN INTERACTION Methodological influences: ethnomethodology and CA In his attempt to break away from the traditional sociological approaches, Garfinkel (1967) developed a radically new method for data analysis that focused on the examination of intersubjective social accomplishments. Ethnomethodology focuses on the methods and practices that members of society employ to make sense of takes the world they live in (Garfinkel, 1974; ten Have, 2002), these could be for instance how queues are formed (Livingston, 1987