You want to uncover the effect of flexible work hours on employee retention: whether by giving employees more freedom to choose their work hours makes them more likely to stay with their employer. You can use observational data on firms from two years, and some firms introduced flexible work hours between those two years. How can you use this data to estimate the effect you are after?

You want to find out if the earnings of women and men tend to be different in your country in the occupation you are considering: market analysts. Analyzing data from a random sample of market analysts in the USA, you find that women earn less, by 11 percent on average. How much gender difference can you expect among all market analysts in the USA? In particular, is there a difference in the population, or is it just a chance event true in your data? And can you generalize these results to the future or to other countries?

Life expectancy at birth shows how long residents of a country live; it is a summary measure of their health. Residents of richer countries tend to live longer, but you want to know the strength of that pattern. You also want to identify countries where people live especially long for the income level of their country, to start thinking about what may cause their exceptional health. You download cross-country data on life expectancy and GDP per capita, and you want to uncover the pattern of association between them. How would you do that in a way that accommodates potentially nonlinear patterns and, at the same time, produces results that you can interpret?

You are considering investing in a company stock, and you want to know how risky that investment is. In finance, a relevant measure of risk relates returns on a company stock to market returns: a company stock is considered risky if it tends to move in the direction of the market, and the more it moves in that direction, the riskier it is. You have downloaded data on daily stock prices for many years. How should you define returns? How should you assess whether and to what extent returns on the company stock move together with market returns?

You want to know whether online and offline prices differ in your country for products that are sold in both ways. You have access to data on a sample of products with their online and offline prices. How would you use this data to establish whether prices tend to be different or the same for all products?

Identifying a good research question is a vital first step in any behavioural study because the question will focus the rest of the research cycle. Four logically distinct types of question can be asked about any behaviour. These concern its mechanisms, its development (or ontogeny), its function and its evolution (or phylogeny). The mechanisms underlying behaviour can be studied at many different levels, ranging from the social or physical environmental conditions that influence the behaviour down to the neural networks responsible for behavioural output. The nature of the research question will influence decisions about what species to study. Research questions are developed through a combination of approaches, including reading the literature, preliminary observations and exploratory data analysis. A research question leads to a set of hypotheses that need not be mutually exclusive but should all be testable. Each hypothesis should generate one or more specific predictions.

Statistical analysis is usually necessary to answer questions with behavioural data. Analysis should be planned and registered before collecting data. Once collected, a dataset should be formatted and permanently archived prior to analysis. Data is checked and visualised with descriptive statistics and graphs. Models representing hypotheses about the true effects present in the population from which the dataset is a sample are built and tested with inferential statistics. Many different hypotheses can be captured using a linear modelling framework in which an outcome variable is predicted with a combination of predictor variables and interactions. Sources of non-independence in datasets can be addressed with mixed models. The robustness of findings can be examined by comparing the results obtained when analysis is done in different ways using model selection and multiverse approaches. Confirmatory analysis designed to test preregistered hypotheses should be clearly differentiated from exploratory analysis that generates new hypotheses.

You work for a company that wants to quantify the benefits of its online advertising: how many people buy its product because they see an ad posted online. How can you translate this question into something you can uncover using actual data? What kind of data do you need to get a good answer to this question? What would be the most important issues to consider with that data?

Measuring behaviour means assigning numbers to observations of behaviour according to specified rules. Converting a stream of behaviour into behavioural metrics involves choosing and defining specific categories of behaviour that can be measured. Behavioural categories can be described in terms of their physical structure or their consequences. An ethogram is a catalogue of the species-typical behavioural categories displayed by a species in a specified environment. Descriptions of behavioural categories should be unambiguous and written down before data collection starts. Behavioural categories can be designated as either events (short duration) or states (longer duration). Behavioural categories are used to generate metrics such as latencies, frequencies, durations and intensities. Two or more metrics can be combined to form a composite metric. Metrics can be at different levels of measurement, ranging from nominal (weakest) to ratio (strongest).