This chapter is an overview of the whole book and its findings. First, we note the shortcomings of traditional cost benefit analysis (CBA) – that it has no way to value things that just happen to us, like losing a job, being sick or being robbed. We then show how non-monetary outcomes can be valued using the new science of wellbeing.

Wellbeing is measured by life satisfaction (0–10), which is shown to be a reliable and valid measure (with good ability to predict mortality). So, to measure the benefits of a policy, we measure its non-pecuniary effects in terms of life satisfaction. We then convert the change in life satisfaction into the equivalent amount of money (that would produce that change in life satisfaction). For each policy, we then compute the ratio of benefits to net cost. This is the method now used by the UK Treasury.

This chapter critiques managerialist research on employee rule‑breaking, arguing that its central concepts and terms are conceptually chaotic and ethically biased. It distinguishes rational from chaotic abstractions and identifies two forms of terminological error – the jingle and jangle fallacies – where one term is used for multiple concepts or multiple terms for one concept. Using counterproductive work behaviour, employee deviance, antisocial behaviour, and variants such as time theft/time banditry as illustrations, the chapter shows how subsumption and unwarranted disaggregation produce overlapping definitions, unreliable scales, and contradictory operationalisations. It also highlights the moral colouring of managerialist vocabulary, which privileges managerial interests and pathologises non‑compliance while rarely interrogating employer practices (for example, wage theft). The authors call for clearer conceptual distinctions, consistent terminology aligned with definitions and measures, and attention to the relational and contextual origins of workplace misbehaviour.

This chapter provides a short introduction to climate change in the planetary boundaries framework.

Qualitative and quantitative methods can help make sense of the range of meanings connected with concepts, mapping usage and clarifying alternatives so people can position their intended meanings with respect to the space of existing alternatives. Likewise, qualitative and quantitative methods can help clarify where measurement problems come from, and can be used together to turn abstract ideas about measurement error into far research conversations about how social dynamics work to produce social science measures in the first place. Qualitative and quantitative approaches to conceptualization and measurement involves familiar tensions between deep attention to texts versus breadth and representation across a broader collection; between human comprehensibility and optimal work with complex, multidimensional mathematical and linguistic representations; and between familiarity and precision. Good research designs, like those discussed in this chapter, mitigate these tensions and emphasize the strengths of both methods.

This chapter details the novel methodology used to empirically compare Supreme Court rulings with public opinion. Moving beyond traditional measures that focus on individual justice ideology, this approach is designed to track the term-to-term ideological alignment between the Court’s policy outputs and the preferences of the American public. The foundation of this method is a series of five annual surveys (2020–2024) fielded to nationally representative samples. These surveys ask respondents their views on the underlying policy issues in a dozen or more of the most salient cases before the Court each term, translating complex legal disputes into accessible questions.</p>To synthesize this data, the authors employ ideal point estimation. This statistical technique jointly scales the survey responses and the justices’ votes, placing respondents, individual justices, and the Court’s aggregate rulings onto a common liberal-conservative continuum. A key innovation is the yearly rescaling of these estimates, which sets the average American’s ideology as a fixed benchmark at zero. This allows for a direct and intuitive measurement of how far the Court’s rulings deviate from the public mainstream in any given term. The chapter concludes by validating these new measures, demonstrating their strong correlation with established Martin–Quinn scores.

Despite decades of theoretical conceptualization of racism’s complexity as a multilevel and multidimensional construct, the measurement of racism has largely ignored these important nuances. Prior empirical research has largely focused on interpersonal racism, its antecedents, and its consequences. The present chapter summarizes the variety of methodological tools that have been used to evaluate youths’ experiences with interpersonal racism. We highlight opportunities to advance the scientific rigor of each method and technique, with particular considerations for measurement inclusive of the Intersectionality framework. We also argue for measuring racism beyond the individual level. Cultural, institutional, and structural racism are delineated, and tangible recommendations are provided for advancing the measurement of each type of racism among marginalized youth populations. The chapter concludes with promising avenues of future research to support the triangulation of measurement and enhance the scientific understanding of the impact of racism.

In this chapter, we review the current state of this science for the measurement of daily ethnic and racial discrimination among ethnoracially minoritized youth. The goal of this chapter is to provide an understanding of the primary theoretical and empirical foundations for this research, to review the most recent findings and advancements in ethnic and racial discrimination measurement, and to highlight future directions that may prove important to understanding the daily experiences and health of ethnoracially minoritized youth. We highlight the foundation of this research in critical race theory, stress and coping research, and developmental and socioecological frameworks, we discuss the importance of daily methods in researching quotidian ethnic and racial discrimination, we review the health research that has contributed to our understanding of daily ethnic and racial discrimination assessment, and we highlight how measurement approaches should continue to evolve as social spaces, online contexts, and US society and institutions continually to shift.

This chapter provides a critical reflection on past and current research on ethnic and racial discrimination and youth development with recommendations for future research directions. First and foremost, I emphasize the need for positionality, reflexivity, and representational ethics to avoid advancing false or problematic narratives and to advance research that is more transparent and accountable. It also is necessary to distinguish and better contextualize ethnic discrimination (rooted in ethnocentrism) and racial discrimination (rooted in modern imperialism and White supremacy) rather than conflate these two constructs and measure them in ahistorical ways. These considerations require researchers to select or develop critically appropriate measurement tools, moving beyond commonly used measures that may not be relevant or appropriate to all racialized groups. Ethnic and racial discrimination during youth development requires special considerations, as discrimination coincides with identity formation and pubertal development. Yet there remains limited research on the ways in which these developmental tasks and experiences interplay. Given the complexities of how ethnic and racial discrimination manifest during youth development, researchers may want to consider novel methods like storytelling to embody discriminatory experiences and strengthen ecological validity.

Chapter 9 focuses on how students develop foundational understandings of Measurement and Space in the early years (Foundation to Year 2). It explores how young learners engage with concepts such as length, area, time, and mass through hands-on experiences and everyday contexts. You will consider how to structure learning experiences that build conceptual understanding, support spatial reasoning, and introduce key mathematical vocabulary and thinking processes.

Chapter 10 builds on earlier learning and explores how students extend their understanding of Measurement and Space in the middle and upper primary years (Years 3 to 6). It focuses on the development of key concepts such as units of measure, angles, transformations, and geometric properties. You will investigate strategies for using precise mathematical language, addressing common misconceptions, and applying spatial and measurement thinking to solve purposeful, real-world problems.

Chapter 2 explains how the Economist Mental Model (EMM) is measured by capturing two key dimensions: familiarity with core economic concepts (e.g., inflation, supply–demand, market dynamics) and the application of economic reasoning tools (e.g., cost–benefit analysis, opportunity costs, marginal analysis). This measurement approach uses an index of “economic knowledge,” drawn from original surveys in the US, Italy, and the UK. By assessing both factual economic knowledge and the ability to apply it to hypothetical scenarios, the index distinguishes EMM users (high economic knowledge) from those with Alternative Mental Models (AMMs) (low economic knowledge). Descriptive findings reveal that older, educated, wealthier men tend to score higher, with no strong partisan gap. Notably, EMM adopters do not value economic objectives more than those with AMMs, highlighting that differences in policy choices likely stem from how trade-offs are interpreted rather than divergent fundamental values. Demonstrating strong consistency across contexts, these measures set the stage for subsequent empirical analyses.