Survey research is a method commonly used to understand what members of a population think, feel, and do. This chapter uses the total survey error perspective and the fitness for use perspective to explore how biasing and variable errors occur in surveys. Coverage error and sample frames, nonprobability samples and web panels, sampling error, nonresponse rates and nonresponse bias, and sources of measurement error are discussed. Different pretesting methods and modes of data collection commonly used in surveys are described. The chapter concludes that survey research is a tool that social psychologists may use to improve the generalizability of studies, to evaluate how different populations react to different experimental conditions, and to understand patterns in outcomes that may vary over time, place, or people.

This chapter provides an overview of methods for data collection in Conversation Analysis and practical advice on collecting interactional data. We touch on several recurrent issues that researchers encounter in the process. These issues include accessing data; the use of existing data (including user-uploaded, like YouTube); navigating gatekeepers in accessing a setting; building trust with members of a setting; building ethnographic understanding of activities under examination; obtaining ethical approvals; protecting privacy of participants; methods and materials for informed consent (including with populations with diminished capacities); devising a recording schedule; deciding when/how often to record; selecting the right quantity and type of recording equipment; considerations of spatial and audio environments; the use of alternative technologies for recording; recording mediated interactions; procedures and check-lists for before recording; positioning and framing the camera; when to press record and when to press stop; navigating the presence of the researcher-recorder on site; and gathering supplementary documentation from the setting.

While the preceding chapters of the Handbook have focused on practical skills in CA research methods, this chapter looks towards the path ahead. A diverse group of conversation analysts were asked to outline possible projects, point readers toward un- or under-described interactional phenomena, and discuss persistent issues in the field. The contributions address future advances in data collection, specific interactional practices, the complex interplay between language and the body, and cross-cultural and crosslinguistic comparisons, among other issues. The chapter concludes with a concise reiteration of the bedrock principle that underpins all CA research methods.

Conversation-analytic (CA) research projects have begun to involve the collection of interaction data in laboratory settings, as opposed to field settings, not for the purpose of experimentation, but in order to systematically analyze interactional phenomena that are elusive, not in the sense of being rare (i.e., ‘seldom occurring’), but in the sense of not being reliably or validly detected by analysts in the field using relatively standard recording equipment. This chapter (1) describes two, CA, methodological mandates – ‘maintaining mundane realism’ and ‘capturing the entirety of settings’ features’ – and their tensions; (2) provides four examples of elusive phenomena that expose these tensions, including gaze orientation, blinking, phonetic features during overlapping talk, and inhaling; and (3) discusses analytic ramifications of elusive phenomena, and provides a resultant series of data collection recommendations for both field and lab settings.

Once the theory is specified and an operationalization has been chosen for the nodes and links, the next step is to acquire the data. This chapter goes deep into issues that arise when designing surveys to collect data. Although this is not the only method of data collection, it is one that illuminates issues that pertain to all others. This chapter covers the practical question of how to use surveys to elicit network information. The advice leans heavily on a well-formulated theory.

This chapter provides an overview of selected studies assessing technology-aided programs to promote independent leisure and communication or combinations of independent leisure, communication, and daily activities in people with mild to moderate intellectual disability often associated with sensory and/or motor impairments. The studies included in the overview offer an opportunity to describe the development of those programs, the technology solutions used to support them, and their outcomes in terms of participants’ independent performance. Following the presentation of the programs and their outcomes, the discussion focuses on three main issues: (a) effectiveness of the programs and methodological considerations, (b) accessibility and affordability of the programs, and (c) implications of the programs for professionals working in daily contexts. With regard to the last issue, an effort was made to examine ethical and moral questions that may accompany the possible decisions of professionals to adopt those programs in daily contexts.

In the previous chapters we have considered the ‘nuts and bolts’ of epidemiology. In this and the next few chapters we look at how epidemiology is used in practice to improve public health. We start with ‘surveillance’ because without timely information on emerging and changing health problems, public health action can be paralysed or, at best, inefficient. In this chapter we discuss the design and use of surveillance systems that enable health officials to detect new risks and diseases such as mpox promptly, track known diseases and health problems, and generate data needed for effective health planning and resource allocation.

This chapter describes lab verification and clinical validation of tests for the detection of SARS-CoV-2. As new SARS-CoV-2 tests were being developed early in the pandemic, extensive lab verification studies to “test the tests” were conducted at ACME POCT at Emory University. Initial testing was performed in a Biosafety Level 3 facility to determine if the assays could detect propagated SARS-CoV-2 in ideal conditions and evaluate the specificity of these tests. We then describe the establishment of a Biorepository to bank SARS-CoV-2 variant samples and use these samples to determine whether tests could detect new variants with equal sensitivity as the original wild-type virus. This chapter also describes the clinical validation of tests using samples collected from individuals at testing centers. The clinical validation core requires careful planning for staffing and personnel training, semi-permanent and mobile clinical sites, defining inclusion and exclusion parameters, and data collection and reporting. Our experience demonstrated the importance of developing strong relationships with academic and private partners to facilitate clinical site setup, marketing, and purchasing.

Chapter 3 shows why the contracts model doesn’t work: consent is absent in the information economy. Privacy harm can’t be seen as a risk that people accept in exchange for a service. Inferences, relational data, and de-identified data aren’t captured by consent provisions. Consent is unattainable in the information economy more broadly because the dynamic between corporations and users is plagued with uneven knowledge, inequality, and a lack of choices. Data harms are collective and unknowable, making individual choices to reduce them impossible. Worse, privacy has a moral hazard problem: corporations have incentives to behave against our best interests, creating profitable harms after obtaining agreements. Privacy’s moral hazard leads to informational exploitation. One manifestation of valid consent in the information economy are consent refusals. We can consider them by thinking of people’s data as part of them, as their bodies are.

This chapter describes the process of creating and annotating a corpus. This process involves, for instance, collecting data (speech and writing), transcribing recorded speech, and adding annotation, markup indicating in a conversation, for instance, when one person’s speech overlaps another speaker. While written texts are relatively easy to collect – most writing is readily available in digital formats – speech, especially spontaneous conversations, has to be transcribed, though voice recognition software has made progress in automating the transcription of certain kinds of speech, such as monologues. Other stages of building a corpus are also discussed, ranging from the administrative (keeping records of texts collected) to transcribing recordings of speech. The chapter concludes with a description of various kinds of textual markup and linguistic annotation that can be added to texts. Topics discussed include how to create a “header” for a particular text. Headers contain various kinds of information. For written texts, the header would include, for instance, the title of the text; the author(s); if published, where it was published. Other textual markup is internal to the text, and in a spoken text would include such information as speaker IDs, and the beginnings and ends of overlapping speech.