Realist evaluators argue that evaluations need to ask not just what works but also what works for whom under what conditions. They argue interventions need to be evaluated in terms of the mechanisms they trigger and how these interact with context to generate different outcomes in different settings or populations. Hypotheses should be worded as context–mechanism–outcome configurations (CMOCs). Many realist evaluators argue that randomised trials are not a proper scientific design, do not encompass sufficient variation in contexts to test CMOCs and are inappropriately positivist in orientation. They argue that it is better to test CMOCs using observational designs which do not use randomisation. We welcome the focus on CMOCs but disagree with the view that trials cannot be used for realist evaluation. Trials are an appropriate scientific design when it is impossible for experimenters to control all the factors which have an influence on the result of an experiment. Trials can include sufficient variety of contexts to test CMOCs. Trials need not embody a positivist approach to the science of complex health interventions if they are oriented towards testing hypotheses, draw on theory which engages with deeper mechanisms of causation and use distinctly social science approaches such as qualitative research.

Drawing on Putnam’s famous fact–value entanglement argument, Chapter 7 shows how economics is inescapably value-entangled, and argues that while economics is an inherently value-laden discipline it may still be an objective one. It describes economics’ value structure as being anchored by its main normative ideal shared across different approaches, individual realization – what most people in the discipline believe is most valuable and good about human society and characteristic of human nature. It compares two competing interpretations of what that ideal involves – one in mainstream economics and one in capability economics – distinguishing them according to the different additional values regarding what well-being involves, they adopt to give content to the individual realization ideal. It then evaluates these two approaches according to whether their different value structures are consistent – an analysis I characterize as value disentanglement. After this, the chapter turns to a general framework for ethics and economics – or ethics in economics – distinguishes four different forms of disciplinary relationships between economics and ethics, and argues that while cross-disciplinarity best describes the current status of economics and ethics, transdisciplinarity represents an aspirational conception of what an objective, value-laden economics ultimately requires.

This chapter introduces the readers to case study research, with the help of historical and contemporary examples. We define case study research and briefly discuss the existing case study designs. Subsequently, we explain the main purpose of this book: To take case study research to the next level by discussing the combinations of different case study designs in the same study, which we call "sequencing case study designs." Furthermore, we discuss the building blocks of case study designs, the strengths/weaknesses of archetypical designs, the conundrum surrounding the crafting/relaying of theoretical contributions, some concrete examples of designs, and the differences/similarities amongst different paradigmatic camps in case study research. We end the chapter by briefly introducing the contents of the subsequent chapters.