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Evidence is needed to inform and guide the choices that healthcare organisations make in relation to how budgets are spent. The associated costs and benefits of health treatments are key components of such decisions. An economic evaluation is a way of systematically identifying the costs and benefits of different health activities and comparing these to make an informed decision about the best course of action based on the evidence available. Economic evaluations can also be used to identify uncertainty around the likely costs of a particular health activity and to compare this against a ‘willingness to pay’ threshold, in order to judge their value for money. This chapter examines the key parts of economic evaluations and the data that feed into them, and considers how the results of economic evaluations can be interpreted.
This chapter provides an explanation of what qualitative data is, and gives examples of different analysis methods and the factors that influence how and why they are chosen. Analysing data by looking for common themes (known as thematic analysis) is one of the most common ways in which researchers approach data they have gathered. There are various criticisms levelled at qualitative analysis including issues relating to validity, reliability and credibility. Researchers can address these through a range of methods including triangulation of data, member validation, careful sampling and transparency of approach.
Three main types of qualitative research methods were used within the EQUIP programme of work and these form the focus of this chapter: in-depth interviews, focus groups and observations. Throughout the chapter, the authors look at allied publications resulting from EQUIP as a way of providing examples of real life research to support the description of the methodological approaches provided. This chapter presents the three types of qualitative research methods, discusses the factors that influence the choice of research method, and gives practical advice on how to utilise qualitative research methods.
A systematic review is a vital part of the research process. It forms a clear and rigorous summary of existing evidence relating to a treatment, presented in a useful and comprehensible way to inform other healthcare professionals’ decision-making. This chapter breaks down each stage of the systematic review process, inviting the reader to critically consider a range of methods and techniques for the inclusion and analysis of studies and their findings.
This chapter defines and introduces the different stages of the research process: from identifying a problem, to reviewing the literature; then developing a research question; designing a study; obtaining funding and ethical approval; recruiting participants; collecting and analysing data; and reporting and disseminating findings. This chapter outlines how users of health services, their carers and family members, and other members of the public can be involved in these different research stages, and demonstrates the impact that this involvement can have. Examples of different ways of involving and engaging public members in research studies are drawn from the Enhancing the Quality of User-Involved Care Planning in Mental Health Services (EQUIP) research programme.
It is of great importance that research projects are informed by sound ethics, properly planned, approved by an independent ethical board and rigorously monitored throughout the duration of the study. This chapter introduces four principles that govern the conduct of ethical research using relevant case examples to bring each principle to life. Topics explored include ‘informed consent’, capacity to provide consent, minimising and managing harm and the fair and equal treatment of study participants.
This chapter will examine the origins of measurement scales in research by considering the science of psychological testing. In particular the chapter provides a brief definition of a measurement scale, outlines why scales are used, examines the design and evaluation of scales, discusses what the responses to scales mean, outlines advantages and limitations of their use, and provides examples of measurement scales developed and used in the EQUIP project and other published mental health research. In recent years, as a response to criticisms that measurement scales are often not patient-oriented, we have seen increasing emphasis placed on the development of Patient Reported Outcomes Measures (PROMs). These tend to be less focussed on symptoms and more on the everyday experiences of people using services. They are much more likely to be designed and developed in collaboration with service users. The EQUIP research project developed a good quality PROM for assessing user and carer involvement in care planning, the first such measure of its kind in mental health.
Quantitative data analysis makes sense of numerical data. 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. Perhaps most importantly, we can use numbers to look at differences between groups of people or the same group over time. This can help us understand the effect of new treatment or policy initiatives, both in terms of the type of effect (e.g. does a new policy make things better, worse or leave things unchanged?) and the size of its impact (e.g. are any changes big enough to be meaningful or could they have happened just by chance?). This chapter explores some of the different approaches to analysing numerical data, examines the difference between descriptive and inferential statistics, and highlights some of the ways in which you can begin to interpret research data presented as numbers.