The importance of simple descriptive data was recognised by William Farr, whom we mentioned briefly in Chapter 1 for his seminal work using the newly established vital statistics register of England in the nineteenth century. As we discussed in Chapter 1, this descriptive epidemiology, concerned as it is with ‘person, place and time’, attempts to answer the questions ‘Who?’, ‘What?’, ‘Where?’ and ‘When?’. This can include anything from a description of disease in a single person (a case report) or a special survey conducted to measure the prevalence of a particular health issue in a specific population, to reports from national surveys and data collection systems showing how rates of disease or other health-related factors vary in different geographical areas or over time (time trends). In this chapter we look in more detail at some of the most common types of descriptive data and where they come from. However, before embarking on a data hunt, we first need to decide exactly what it is we want to know, and this can pose a challenge. To make good use of the most relevant descriptive data, it is critical to formulate our question as precisely as possible.

Once the theory is specified and an operationalization has been chosen for the nodes and links, the next step is to acquire the data. This chapter goes deep into issues that arise when designing surveys to collect data. Although this is not the only method of data collection, it is one that illuminates issues that pertain to all others. This chapter covers the practical question of how to use surveys to elicit network information. The advice leans heavily on a well-formulated theory.

Chapter 2 covers the period in the late 1960s and early 1970s when the Brazilian military government planned and began building its big dams. It argues that political pressures encouraged the military regime to build dams with giant reservoirs and to do so quickly and without regard to their social and environmental footprints. The dictatorship looked to hydropower projects as a means of powering industrial and economic growth that would legitimize military rule, and it wasted no time in starting construction because it takes a long time to build big dams, often the better part of a decade, and sometimes longer. The 1973 oil crisis added urgency, raising the price of imported petroleum and pushing the government to invest in alternative sources of energy. The crisis encouraged the military regime to double down on the big dams already under construction and to plan a host of new ones. Political pressures also made their way into debates about specific dam sites. The most prominent case was the binational Itaipu Dam (on the Brazilian-Paraguayan Border), where the military government had to weigh geopolitical considerations alongside other criteria. The result of all these political pressures combined was a firm commitment to building large reservoirs in environmentally sensitive areas without public debate and without completing thorough environmental impact studies.

In working with network data, data acquisition is often the most basic yet the most important and challenging step. The availability of data and norms around data vary drastically across different areas and types of research. A team of biologists may spend more than a decade running assays to gather a cells interactome; another team of biologists may only analyze publicly available data. A social scientist may spend years conducting surveys of underrepresented groups. A computational social scientist may examine the entire network of Facebook. An economist may comb through large financial documents to gather tables of data on stakes in corporate holdings. In this chapter, we move one step along the network study life-cycle. Key to data gathering is good record-keeping and data provenance. Good data gathering sets us up for future success—otherwise, garbage in, garbage out—making it critical to ensure the best quality and most appropriate data is used to power your investigation.

This chapter tests two ways of overcoming uncertainty about relationality – having potential collaborators directly communicate how they will relate to each other, and using third parties such as matchmakers and boundary spanners. Both are useful for creating valuable new collaborative relationships, especially between people who begin as strangers. In addition, this chapter also presents evidence showing the impact of new collaborative relationships on strategic decision-making. Data in this chapter come from a variety of national surveys, field experiments, and case comparisons.

Chapter 6 presents an array of techniques for assessing motivation including self-reports, questionnaires, rating scales, checklists, surveys, interviews, and a diagnostic protocol. In addition to these assessments, Appendices 6A, 6B, and 6C – designed for teachers, students, and corporate folk – contain the Reisman Diagnostic Motivation Assessment (RDMA) items that emerged from and are categorized by the motivation theorists presented in Chapter 3. Appendix 6D includes the Reisman Diagnostic Creativity Assessment (RDCA) interpretation and Appendix 6E provides an alternative RDMA interpretation. This chapter also addresses why motivation in education is important, students and motivation, teachers and motivation, corporate employer and employee motivation, self-esteem and motivation, and motivation and creativity.

The main quantitative research designs are reviewed, to indicate their strengths and limitations. Even the best designed and conducted studies run the risk of being influenced by the ever-present threats of chance, bias, and confounding, terms that are explained and illustrated with examples, and of sampling and measurement error. The informed reader should therefore look beyond the results of a study, using critical appraisal skills to ask whether the study’s design, sample, and measures are robust enough to allow the findings to be taken as likely to be accurate.

We offer details of our empirical approach. Our method involved multiple distinct representative surveys of key college athletic stakeholders (i.e., student-athletes, coaches, athletic administrators, and the American public including nonfans and fans). Our surveys measure opinions on a set of gender equity policy proposals (e.g., equal spending, requiring schools to interview at least one woman for athletic director jobs, etc.). We also include an exercise that requires respondents to confront inevitable policy tradeoffs. Respondents are asked to allocate a fixed budget to fund gender equity initiatives or benefit initiatives (e.g., paying college student-athletes versus guaranteed scholarships). Our measures allow us to explore the empirical evidence for our argument while making generalizable statements about the relevant stakeholders. The chapter provides details of our measurement approach and samples, as well as how we connect our theory to our analyses. Our data allow us to excavate important dynamics within and across group that have interests in college athletics.

In June 2016 the UK shocked the world by voting to leave the European Union. In our previous book (Brexit: Why Britain Voted to Leave the European Union) we told the story of what happened in the referendum and why it produced a leave vote. This book is a sequel to the earlier one and examines what happened after the decision was made looking at events up to the point that the UK formally left the EU in January 2020. This was a period of unprecedented political and electoral turmoil in British politics which for a period looked like it could shatter the party system. It encompassed three elections and three different Prime Ministers and unprecedented volatility in both Parliamentary and electoral politics. The book maps out the twists and turns of the Brexit process, both at the level of the political elites and among the mass public. It then goes on to examine the long-run antecedents of this momentous decision, using data that goes back more than fifty years. Finally, it speculates about the economic and poltical consequences of Brexit for the future, while taking into account the Covid Pandemic which itself added to the turmoil in British politics.

Boris Johnsons victory over Jeremy Corbyn in the 2019 general election

The electoral consequences for Theresa Mays government in the 2017 general election examining the role of Brexit and other factors in explaining the results