This chapter outlines a comprehensive multimethod approach that integrates ethnography and quantitative data analysis to explore the concept of exit. Building on Hirschman’s exit–voice–loyalty theory, the chapter delineates two distinct forms of exit: permanent exit, characterized by the death of voters, and partial exit, which can be either forced or voluntary and does not always involve physical migration. The latter includes phenomena such as migration-related remittances, which symbolize loyalty from emigrants to those who remain. The chapter highlights how partial exit can manifest through voter attrition, often attributed to pandemic fatigue. The narrative indicates that the government did not instigate this exit, but it later discovered covert methods to leverage it for political gain. The chapter introduces the reader to the voter exit premium, the additional votes bolstering ZANU PF due to voter exit. Exit premium is calculated in Chapters 3 and 4.

In this chapter, the chronological and geographical distribution of the probate inventories under examination are addressed, and they are classified as to key variables, like the occupation of the deceased, their gender, and the reason for production of the inventory. Some of this information – particularly the reason for the making of the lists – will be used to assess the existence of biases of wealth and age. The argument of this chapter is that Valencian inventories overcome most of the problems that have been identified for their quantitative use in other countries. As far as the late medieval period is concerned, Valencian lists of goods provide, in terms of their abundance, exhaustiveness, and precision, some of the best sets of inventories for Europe as a whole.

This chapter discusses mixing qualitative and quantitative methods as both a tradition in psychological anthropology and an essential strategy to produce important findings. Mixed-methods designs are research question-driven strategies, which contrast with those strategies that begin with a preferred data collection approach and then formulate a question to suit the chosen methodology. Mixed-methods strategies are used to study beliefs and behaviors in context across levels of analysis to represent the world dynamically and holistically. Despite the popularity of qualitative ethnographic methods in anthropology for the past five decades, psychological anthropologists have persisted in using mixed methods. There are four critical reasons for the continuing use of mixed methods. Mixed methods allow greater explanatory depth, mixed-methods research can become more inclusive, mixed methods allow for surprising insights, and mixed methods allow for productive collaboration across disciplinary boundaries. The final section of the chapter reviews recent well-funded and successful research projects that successfully use mixed methods across a wide range of research topics.

This chapter discusses the development of methods in cognitive anthropology. It documents how these methods developed from a focus on documenting shared cultural knowledge to a period where the person returned as a primary locus of cultural experience. The chapter’s discussion is organized into three overlapping historical periods. The ethnoscience period involved strategies for the elicitation of cultural domain taxonomies, componential analyses, and methods that allowed the identification of prototypical members of a category or subcategory. The cognitive schemas period used more structured data collection methods to document cultural schemas that organize items in a cognitive domain and statistical methods for modeling their interrelations. Cognitive anthropologists also developed ways to document cultural schemas in everyday talk, mainly using the method of semi- and unstructured extended interviews and life histories. The cultural models period used structured and unstructured data collection methods and quantitative and qualitative data analysis from the cultural schemas research period. These methods were used to connect culture to variations in individual experience.

In this chapter, we review approaches to model climate-related migration including the multiple goals of modeling efforts and why modeling climate-related migration is of interest to researchers, commonly used sources of climate and migration data and data-related challenges, and various modeling methods used. The chapter is not meant to be an exhaustive inventory of approaches to modeling climate-related migration, but rather is intended to present the reader with an overview of the most common approaches and possible pitfalls associated with those approaches. We end the chapter with a discussion of some of the future directions and opportunities for data and modeling of climate-related migration.

Tomi Laamanen, Emmanuelle Reuter, Markus Schimmer, Florian Ueberbacher and Xena Welch argue that even though most work in strategy as practice research has been qualitative in nature, there are also great opportunities for studying strategy practices quantitatively. The authors first review the use of quantitative research methods in closely related strategy research streams (top management teams, middle management, strategic decision-making, strategic consensus and strategic initiatives). Next, the authors synthesize lessons learned from research based on quantitative methods and introduce established quantitative research methods (e.g., computer-aided content analysis, topic modelling and machine learning, network analysis, sequence analysis, event history analysis and event study methodology) as well as novel sources of quantitative data such as email data and press release data streams extracted from different news sources. Overall, they introduce each method and highlight possible avenues that a quantitative researcher interested in strategy practices could utilize.

This chapter examines the conceptualization and measurement of contact phenomena in the context of bilingualism across various languages. The goal of the chapter is to account for various phonetic contact phenomena in sociolinguistic analysis, as well as providing context for elaborating on quantitative methodologies in sociophonetic contact linguistics. More specifically, the chapter provides a detailed account of global phenomena in modern natural speech contexts, as well as an up-to-date examination of quantitative methods in the field of sociolinguistics. The first section provides a background of theoretical concepts important to the understanding of sociophonetic contact in the formation of sound systems. The following sections focus on several key social factors that play a major part in the sociolinguistic approach to bilingual phonetics and phonology, including language dominance and age of acquisition at the segmental and the suprasegmental levels, as well as topics of language attitudes and perception, and typical quantitative methods used in sociolinguistics.

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.