As a publisher, we play a significant role in supporting the development of new research understanding and skills, and in reflecting on emerging agendas and dilemmas, including online data, evidence use, ethical practice, mixed methods, participatory approaches and cross-disciplinary learning.
Our titles on social research methods and research practices span disciplines and embrace new collaborations and ways of working as part of a focus on challenge-led research.
Highlights in this area include the Social Research Association Shorts, which provide academics and research users with short, high-quality and focused guides to specific topics, and the Longitudinal and Life Course Studies journal.
Social Research Methods and Research Practices
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Creative methods of collecting data draw on the imagination of participants and researchers. There are myriad options available and as this chapter can only cover a few it tries to demonstrate the diversity on offer by presenting a range of examples. The chapter points out that all methods were once considered ‘creative’ and warns against the use of creative methods for their own sake. The creative data collection methods described are: online; smartphones; enhanced interviews and focus groups; diaries, field notes, journals and logs; visual data; mapping; mobile methods; case studies and collaborative methods. Each method is described in detail, and a list of pros and cons provided, to help guide and inspire researchers in the use of creative methods to address their specific research question. The chapter closes with an update of the case studies followed by exercises, discussion questions and a debate topic.
Qualitative data comes in a variety of forms: text, images, sound and so on, which can be difficult for researchers to analyse confidently. This chapter begins by setting out how qualitative data can be prepared, for example by transcribing interview or focus group data, converting pictorial data to text or creating metadata records ready for analysis. This is followed by guidance on how to code the prepared data, using a coding frame or emergent coding, and an overview of the pros and cons of each method. Next, various ways of analysing qualitative data including content analysis, thematic analysis, discourse analysis and narrative analysis are outlined, followed by a real-life example of qualitative data analysis. Data synthesis is discussed briefly, and the chapter concludes with an update of the case studies followed by exercises, discussion questions and a debate topic.
All researchers should have a grasp of both quantitative and qualitative data analysis methods as most projects will involve both to some degree. This chapter provides advice on the preparation of quantitative data and information about how to code this data. It then examines ways of analysing quantitative data using descriptive and inferential statistics. The statistics methods described include frequency distributions (tables, graphs or pie charts), measures of central tendency (mode, median, and mean or average) measures of variability (correlation coefficient and chi-square test). It also describes the most common parametric tests (t-test, F-test, analysis of variance (ANOVA), cluster analysis, factor analysis and regression analysis) and lists the non-parametric equivalents. Univariate and bivariate statistics are covered including and explanation of covariant and independent data relationships. The chapter closes with an update of the case studies followed by exercises, discussion questions and a debate topic.
This chapter explores some recent studies that support the argument for an ideological or religious orientation to the crisis of expertise, including Professor Shi-Ling Hsu’s identification of anti-science ideology in the Trump administration. Such studies are helpful in showing how science can become politicized—scientists become associated with a political party—and can lose its mooring in evidence. The problem with the term “anti-science ideology,” however, is the implication that believers in consensus science have no ideology—they (allegedly) simply follow the facts. The scientific enterprise, however, issues numerous facts, sometimes inconsistent and sometimes outdated in terms of scientific progress. Even consensus scientists belong to a community of believers with values and commitments.
Every researcher will encounter ethical dilemmas as social research has the capacity to do great harm, to individuals, groups, and society as a whole, if it is unethically managed. This chapter, which is new to this third edition, explores research and evaluation ethics. It begins with an overview of ethics, then explains the current system of research ethics management including the process of obtaining ethical approval from research ethics committees. Emphasis is given to the importance of researchers thinking and acting ethically throughout the research process. The chapter gives an overview of ethics considerations for each stage of a research project from conception to dissemination of results. Researcher wellbeing is another important ethical consideration and one that is often neglected by researchers and ethics committees alike. Factors affecting researcher wellbeing are outlined along with strategies for managing and reducing stress. The chapter concludes with an update of the case studies followed by exercises, discussion questions and a debate topic.
Research doesn’t exist in a bubble but co-exists with a multitude of other tasks and commitments, yet there is more need for people to save time than ever before.
Brilliantly attuned to the demands placed on researchers, this book considers how students, academics and professionals alike can save time and stress without compromising the quality of their research or its outcomes. This third edition:
- is fully revised with new chapters on research and evaluation ethics, creative methods of collecting data and how research can make a positive difference;
-includes illustrative case studies throughout the book and each chapter concludes with exercises, discussion questions and a debate topic;
- is accompanied by a fully updated companion website.
This supportive book is designed for any student or practitioner who wants to know how to do research on top of their main job and still have a life.
Secondary data is data which has been collected by someone else, for a purpose other than a researcher’s own research or evaluation project, and has been made available for re-use. Due to great efforts to make data available to the public, there is now a huge amount of secondary data freely available online, and more is being added all the time. Secondary data is also held in libraries, museums and archives. This chapter explains the advantages and disadvantages of working with secondary data and shows how to find online data sources. It provides information about open data worldwide and looks at application programming interfaces (APIs). It discusses large-scale and international surveys, alongside examples. It includes advice on working with secondary data and concludes with an update of the case studies followed by exercises, discussion questions and a debate topic.
A good research project is built on solid foundations and this chapter begins with some advice on choosing a research or evaluation topic. Information is provided about how to refine your topic into a question followed by a discussion of how to use your research question to guide your data collection. Consideration is then given to how much data is needed and whether it should be quantitative or qualitative. An outline of sampling techniques follows including probability and non-probability sampling. Next, the issue of what constitutes evidence for research is discussed including the ‘hierarchy of evidence’. Guidance on writing a proposal is provided including a suggested list of contents and this is followed by some information about research funders. The chapter concludes with an update of the case studies followed by exercises, discussion questions and a debate topic.
This chapter acknowledges the complexity of the crisis of expertise, as well as the danger of assuming that only one “side” is operating ideologically. The chapter surveys some recent explanations (of our crisis of expertise) offered by sociologists and endorses their suggestion that recourse to scientific explanations needs to be accompanied by some level of modesty. Finally, the chapter considers two institutions that some hope will solve the crisis of expertise, namely, the law and the scientific establishment. In other words, why not just impose scientific truths through legal regulation? Or, alternatively, why not just have a majority of scientists announce a compelling consensus that everyone should accept? The chapter explains that both of these institutions have failed, and will continue to fail, due to (1) the current capture of the legal system by political parties and (2) the fact that the consensus of a majority of scientists alone is not necessarily compelling nowadays.
This chapter focuses historically on the philosophical theory of quasi-religious worldviews in conflict by analyzing a 17th-century Dutch Golden Age painting. The painting reflects the effects of iconoclasm—destruction of images—in formerly Catholic churches by Dutch Calvinists, as well as providing an example of citizens—Protestants and Catholics—who live in different worlds, that is, who see things differently and have different values. There is an analogy here with Latour’s sense that there are two worlds in the climate-change debate, making it difficult for us to have a common understanding of a scientific issue. Finally, the chapter revisits a unique 19th-century critique of Enlightenment claims to have risen above religious belief. Dutch Calvinist thinkers identified a religion among the supporters of the French Revolution, not in the sense of a deistic religion, but a set of faith-like commitments—perhaps a “quasi-religion” or ideology.