This book explores how the EU has attempted to balance its energy security objectives in the twenty-first century, to achieve security of supply, reasonable prices and ambitious climate goals. Specifically, the book focuses on how these challenges have played out in Central and Eastern Europe in the context of their accession to the EU, as members are both subject to and shape the EU’s agenda and legislative outputs. Here we introduce how general prioritisation of security of supply concerns has constrained and at times enabled energy transitions in the region, and how a consistent concern with import dependence on Russia was discursively adopted by the wider EU in the late 2000s, and as a policy goal from 2022. The introduction presents two main arguments of the book (priority of energy security of the CEE countries over climate goals and heterogeneity of the region) and its research design.

In this part of the book, I move from a comparative historical analysis of Poland and East Germany in Part II to an analysis of quantitative data drawn from all the socialist dictatorships of Bulgaria, Czechoslovakia, East Germany, Hungary, Poland and Romania. The purpose of the following two chapters is to test whether the argument developed in Chapter 2 can travel beyond the Polish and East German cases examined above to explain variation in the turnover of coercive elites and the size of coercive institutions across the region from 1945 to 1989.

Maggie B. Gale explores ways of both framing and structuring the beginnings of a research project, and finding what might be called a ‘research niche’. She uses the case study of an emerging research project to articulate different possible approaches to conceptualizing the starting point, direction, and shape of a project, as well as working practices which might be useful in research design and method. The chapter also explores a series of working principles for avoiding the pitfalls of research distractions, without missing out on the serendipitous discoveries which a more unstructured process might allow. Gale’s own research on Elsa Lanchester illustrates the principles.

Paul Rae concludes the volume by reflecting on why it is so hard for performance scholars to write about method and methodology. He proposes that the inherently aesthetic and performative dimensions of research practice mean that performance scholars can struggle to articulate a discourse on method that exists independently. He draws on examples from across the volume, as well as on his own research experience, to consider a particularly challenging (and fruitful) area of complexity in performance research: the aesthetics of research activity. By discussing the moments when the researcher becomes subsumed into the events of practices being researched, aesthetic conduct in research, and the aesthetic qualities of research design, he argues that it is only when performance researchers can better account for the integration of research activity and what is being researched, that they can arrive at a more robust account of method and methodology.

This chapter begins an in-depth discussion of the proposed STRIVE-4 Model. It focuses on the S (scalar) and T (trait) of the model. To demonstrate that virtues can be captured in a scalar manner, It highlights studies that support the reliability and validity of constructs. Virtues such as courage, gratitude, and compassion have all been established as scalar constructs and have been related to a variety of expected well-being outcomes. The chapter further highlights empirical evidence showing that virtues cannot be subsumed by personality research or social desirability, and that informant reports further confirm researchers’ ability to capture scalar virtues. To highlight empirical work on virtues as traits, it discusses the value of intensive longitudinal studies. These studies demonstrate between-person variability and within-person consistency to support the hypothesis that virtues are traits. Finally, the chapter closes by discussing some challenges of virtue assessment, including Aristotle’s assertion of the golden mean and how to understand vice traits. Altogether, the evidence favors assessing virtues as scalar traits. It suggests it is time for researchers to advance virtue science with more sophisticated methods.

Chapter 12 covers AN INTRODUCTION TO RESEARCH DESIGN and includes the following specific topics, among others: Descriptive, Relational, and Causal Research Studies , Blocking, Quasi-Experimental Designs, Threats to Internal Validity, and Threats to External Validity.

We turn to the problem of choosing between going “wide” and going “deep”: between seeking a little bit of information on a large number of cases versus studying a smaller number of cases intensively. We outline a simulation-based approach to identify the optimal mix of breadth and depth. Simulations suggest that going deep is especially valuable where confounding is a concern, for queries about causal pathways, and where models embed strong beliefs about causal effects. We also find that there are diminishing marginal returns to each strategy and that depth often provides the greatest gains when we have cross-case evidence on only a modest number of cases.

This chapter shows how to use causal models to inform the selection of cases for intensive analysis. We outline a procedure in which we predict the inferences that will be made when future data are found and use these predictions to inform case-selection strategies. We ask: Given a set of cases on which we already have data on X and Y, which cases will it be most advantageous to choose for more in-depth investigation? We show that the optimal case-selection strategy depends jointly on the model we start with and the causal question we seek to answer and draw out the implication that researchers should be wary of generic case-selection principles.

Chapter 12 covers an introduction to research design and includes the following specific topics, among others: descriptive, relational, and causal research studies, blocking, quasi-experimental designs, threats to internal validity, and threats to external validity.

This chapter argues for the utility of causal models as a framework for choosing research strategies and drawing causal inferences. It provides a roadmap for the rest of the book. The chapter highlights the approach’s payoffs for qualitative analysis, for combining intensive and extensive empirical strategies, and for making research design choices.

This chapter is the first of three in which we investigate how causal models can inform research design. In this chapter, we draw out the implications of the causal-model approach for clue-selection strategies, for figuring out which pieces of evidence are likely to be most informative about a question of interest. We demonstrate procedures for assessing which clues minimize expected posterior variance and how to construct an optimal decision tree for determining a dynamic clue-gathering strategy.