This chapter introduces the reader to the problem of policy prioritisation and why quantitative/computational analytic frameworks are much needed. We explain the various academic- and policy-oriented motivations for developing the Policy Priority Inference research programme. We apply this computational framework in the study of the SDGs and the feasibility of the 2030 Agenda of sustainable development.

High-quality data are necessary for drawing valid research conclusions, yet errors can occur during data collection and processing. These errors can compromise the validity and generalizability of findings. To achieve high data quality, one must approach data collection and management anticipating the errors that can occur and establishing procedures to address errors. This chapter presents best practices for data cleaning to minimize errors during data collection and to identify and address errors in the resulting data sets. Data cleaning begins during the early stages of study design, when data quality procedures are set in place. During data collection, the focus is on preventing errors. When entering, managing, and analyzing data, it is important to be vigilant in identifying and reconciling errors. During manuscript development, reporting, and presentation of results, all data cleaning steps taken should be documented and reported. With these steps, we can ensure the validity, reliability, and representative nature of the results of our research.

The agricultural research for development (AR4D) domain is becoming increasingly complex, and theory of change (ToC) approaches can provide critical guidance through the maze of transformation concerning engagement, partnership, and research. Most of the major benefits that accrue through the use of ToCs relate to internal learning within project teams. Finding the balance between applying a ToC that is both useful and time- and resource-smart is challenging and may need iteration to get right. Quantitative impact assessment methods must be blended with qualitative methods in ToC-based AR4D, so that evaluation becomes about both the process and numbers, while new methods require developing for blended evaluation. The evidence base concerning the efficiency, efficacy, and failings of ToC-based AR4D urgently requires further development and synthesis, and the lessons must be applied broadly.

In this chapter I examine the statistical effect of industrialization on ethnic change. I first take Soviet-era cross-national data measuring ethnic diversity by country in 1961 and 1985 and regress the change in ethnic diversity across these twenty-four years on change in carbon emissions per capita over the same time period. The results demonstrate a strong relationship between decreasing ethnic diversity and increasing levels of industrialization, a result which is robust to the inclusion of several control variables and the use of various sub-samples, as well as alternative measures of industrialization such as cement production and urbanization. I also show that carbon emissions are robustly correlated with change in the percentage identifying with the largest ethnic group per state. I then use an alternative original dataset consisting of individual country censuses between 1960 and 2019, and show that the same effect holds, both as regards the effect of carbon emissions on ethnic fractionalization as well as robustness checks and multiple alternative measures such as electricity consumption and the share of labour in both agriculture and industry.

Local government in China is largely responsible for funding social policy and has significant control over the specifics of program design and implementation. Therefore, the same policy can look quite different across provinces and even across counties within the same province. What accounts for local variation in social policy provision? This chapter provides a framework of provincial policy styles and demonstrates how these distinct ways of governing help explain variation in social policy implementation. First, the chapter presents an index of policy styles to classify Chinese provinces based on their dominant policy style: pragmatist, paternalist, or mixed. Then, the chapter examines how provinces diverge in their social policy priorities using provincial social policy spending to measure social policy priorities. The analysis finds that pragmatist provinces are more likely to prioritize education and healthcare, while paternalist provinces are more likely to prioritize poverty alleviation and housing.

This chapter examines the significance of attitudinal research in understanding the dynamics of language contact situations in multilingual societies from a cross-disciplinary perspective. This chapter provides practical guidance for the study of language attitudes and ideologies in multilingual communities by discussing issues relating to research planning and design (e.g. identifying which languages are to be explored, whose language attitudes are to be examined in the community), as well as data analysis and interpretation (e.g. quantitative data collected through questionnaires or matched-guise techniques, or qualitative data through interviews or ethnographies). Other important considerations for attitudinal research in multilingual communities are also covered (e.g. the mismatch between positive attitudes towards a language or languages and language use, or links between language policies and language attitudes in language revitalisation projects). The main points made in the chapter are illustrated by means of two case studies. The first relates to language attitudes in multilingual classrooms and the second focuses on language attitudes and ideologies amongst new speakers of minority languages with a focus on Galician in the Autonomous Community of Galicia in north-western Spain.

Recent developments in the experimental syntax program have challenged some of the standard practices for collecting and analyzing linguistic evidence. In doing so, the methodological and theoretical gap between other areas of language science has begun to close. It is more common than ever before for research in theoretical syntax to incorporate multiple methodologies in the same study. Online elicitation methods, adopted from psycholinguistics, have been the most visible new addition to the theoretical syntactician’s toolbox. Yet observational data, in the form of corpora, has begun to play a larger role in contemporary syntactic investigation. The aim of this chapter is to contextualize the evolving role of corpus studies in syntactic investigation as a methodology that can be used to externally validate results from other methods as well as generate hypotheses. I highlight theoretical and practical advantages of employing corpora in tandem with other methods and point to future directions where gains can still be made.

This chapter considers the importance of evaluation in understanding the effectiveness of your health promotion program and highlights the value of knowing why something does or does not work. It outlines basic evaluation methods used in health promotion, and considers the benefits and weaknesses of qualitative and quantitative methods respectively.

Behaviour is the actions and reactions of an organism or group of organisms. Living organisms, robots and virtual agents all exhibit measurable forms of behaviour. Measuring behaviour involves assigning numbers to direct observations of behaviour using specified rules. Direct observation means collecting data that relates directly to the performance of the behaviour pattern in question. Measuring behaviour accurately and reliably is important because behaviour is central to answering many questions in the biological and social sciences. Measuring behaviour is challenging because behaviour has a temporal component, does not always occur in discrete bouts, is generally complicated, can be influenced by stimuli undetectable to humans and varies both within and between individuals. Studying behaviour can be broken down into a series of steps that starts with asking a question and ends with communicating findings.