This chapter focuses on the foundations of study design and statistical analysis in psychological research. It explores strategies for ensuring internal validity, such as randomization, control groups, and large sample sizes. Additionally, it addresses the complexity of human behavior by exploring multivariate experiments and the use of artificial intelligence and machine learning in neuroscience. The chapter also discusses the replication crisis and the emergence of open science practices, encouraging students to think critically about isolated scientific findings and offering tools for identifying credible research. Lastly, it critiques null hypothesis significance testing and p-values while providing an overview of key statistical topics like correlation coefficients, standardized mean differences, and regression.

The traditional case register involved assembling records of people with a given condition in order to support cohort studies to describe and investigate the course of their condition and other outcomes. This old design has been resurrected and revolutionised following the widespread implementation of fully electronic healthcare records over the past few decades, providing ‘big data’ resources that are both large and very detailed. These, in turn, are being further enhanced through linkages with complementary administrative data (both health and non-health) and through natural language processing generating structured meta-data from source text fields. This chapter provides an overview of this rapidly developing research infrastructure, considering and advising on some of the challenges faced by researchers planning studies using clinical data and by those considering future resource development.

This chapter explores the various steps involved in conducting research, including defining the research problem, formulating research questions, selecting an appropriate research design, choosing participants, employing data collection methods, processing and analyzing data, interpreting results, and writing research reports. Understanding these steps is important as it provides a structured framework for approaching research, making the entire process less daunting. Special emphasis is placed on determining the appropriate research design and selecting participants. Additionally, the chapter introduces various data collection methods, techniques, and tools used by applied linguistics researchers. The importance of data processing and analysis will also be highlighted. Moreover, you will explore how to develop purpose statements for both qualitative and quantitative research and learn to identify the strengths of a well-conducted study.

As we progress through this part and the next, you will be introduced to the different ways in which epidemiologists go about analysing the factors that are associated with people becoming ill or getting better. Each of these has a role to play in building up our knowledge about what influences human health. Our objective here is to provide an overview of the range of techniques that are available and to develop your understanding of which of these might be more appropriate in any given situation. One way to think about these techniques is as a set of tools for tackling a range of problems, much as a carpenter has a box full of tools for tackling different aspects of building a house. No one tool is useful in every situation, and some are more useful at certain stages of the construction process than others. Some even have features that make them useful in a variety of situations. Of course, context is everything, so even when a tool might not look like it’s the ‘right’ one in a particular situation, if the results are robust and reliable then that might be all that matters.

In order to examine our three questions, we need objective research methods. Estimating whether a treatment can work, does work, and has value requires a wide range of research strategies. Evidence establishing that a treatment works under controlled conditions does not necessarily assure benefits when the intervention is applied in clinical practice. This chapter considers the development of a research protocol, and biases that might be attributable to participant recruitment, enrollment, retention, and dissemination of findings. In practice, establishing the value of a treatment should consider an examination of the existing literature, development of thorough research plans, recognition of the strengths and weaknesses of the chosen research methods, and integration of study results within a wider body of knowledge. We challenge beliefs in a hierarchy of methods that assumes some methods, such as the RCT, are free from bias.

In this chapter, we look at the analytic studies that are our main tools for identifying the causes of disease and evaluating health interventions. Unlike descriptive epidemiology, analytic studies involve planned comparisons between people with and without disease, or between people with and without exposures thought to cause (or prevent) disease. They try to answer the questions, ‘Why do some people develop disease?’ and ‘How strong is the association between exposure and outcome?’. This group of studies includes the intervention, cohort and case–control studies that you met briefly in Chapter 1. Together, descriptive and analytic epidemiology provide information for all stages of health planning, from the identification of problems and their causes to the design, funding and implementation of public health solutions and the evaluation of whether these solutions really work and are cost-effective in practice.

The overarching goal of public health is to maximise the health of the population, and to achieve this we need evidence about what works and what does not work. Good studies are difficult to design and implement, and interpretation of their results and conclusions is not always as straightforward as we might hope. How, then, can we make the best use of this information? In the next three chapters we look at ways to identify, appraise, integrate and interpret the literature to generate the evidence we need to inform policy and practice. In this chapter we focus on interpreting the results from a single study, because if they are not valid they will be of limited value. The central question we have to answer when we read a study report is, ‘Are the results of the study valid?’

Confounding refers to a mixing or muddling of effects that can occur when the relationship we are interested in is confused by the effect of something else. It arises when the groups we are comparing are not completely exchangeable and so differ with respect to factors other than their exposure status. If one (or more) of these other factors is a cause of both the exposure and the outcome, then some or all of an observed association between the exposure and outcome may be due to that factor.

In the previous chapter we alluded to what is sometimes called ‘secondary’ prevention, where instead of trying to prevent disease from occurring, we try to detect it earlier, in the hope that this will enable more effective treatment and thus improved health outcomes. This is an aspect of public health that has great intuitive appeal, especially for serious conditions such as cancer, where the options for primary prevention can be very limited. However, screening programs are usually very costly exercises and they do not always deliver the expected benefits in terms of improved health outcomes. In this chapter we introduce you to the requirements for implementing a successful screening program and to some of the problems that we encounter when trying to determine whether such a program is actually beneficial in practice.

This chapter describes the basics of scientific figures. It provides tips for identifying different types of figures, such as experimental protocol figures, data figures, and summary figures. There is a description of ways to compare groups and of different types of variables. A short discussion of statistics is included, describing elements such as central tendency, dispersion, uncertainty, outliers, distributions, and statistical tests to assess differences. Following that is a short overview of a few of the more common graph types, such as bar graphs, boxplots, violin plots, and raincloud plots, describing the advantages that each provides. The end of the chapter is an “Understanding Graphs at a Glance” section which gives the reader a step-by-step outline for interpreting many of the graphs commonly used in neuroscience research, applicable independently of the methodology used to collect those data.

Network studies follow an explicit form, from framing questions and gathering data, to processing those data and drawing conclusions. And data processing leads to new questions, leading to new data and so forth. Network studies follow a repeating lifecycle. Yet along the way, many different choices will confront the researcher, who must be mindful of the choices they are making with their data and the choices of tools and techniques they are using to study their data. In this chapter, we describe how studies of networks begin and proceed, the life-cycle of a network study

At the core of epidemiology is the use of quantitative methods to study health, and how it may be improved, in populations. It is important to note that epidemiology concerns not only the study of diseases but also of all health-related events. Rational health-promoting public policies require a sound understanding of causation. The epidemiological analysis of a disease or activity from a population perspective is vital in order to be able to organize and monitor effective preventive, curative and rehabilitative services. All health professionals and health-service managers need an awareness of the principles of epidemiology. They need to go beyond questions relating to individuals to challenging fundamentals such as ‘Why did this person get this disease at this time?’, ‘Is the occurrence of the disease increasing and, if so, why?’ and ‘What are the causes or risk factors for this disease?’