The summer school will be held in a mixed format. Most activities will be online, but those willing and able to attend in person will have an opportunity to take classes and socialize on the campus of the Higher School of Economics in Saint Petersburg, Russia. One workshop will be offline-only; an alternative online activity will be scheduled for the same time slot for those attending virtually.

We will ensure that both online and offline participants have equal opportunity to be involved in the discussion.

To apply, please submit the following Google Form. Applications not accompanied by a CV will not be considered. After reviewing the applications, we will notify the participants about the acceptance.

Payment link will be sent to the participants via email after the reviewing process is finished.

We use a triple-tiered fee structure based on the World Bank's indicators of Gross National Income. You can read more about tier system.

* Proof of enrollment during the Spring 2021 semester must be provided at the time of registration.

We will provide one merit-based scholarship in the amount of the participation fee to an applicant based in Russia who is currently enrolled as a full-time PhD student. All eligible applicants will be automatically considered for this tuition waiver, which will be awarded based on the evaluation of motivation letters.

• May 16 — application deadline.
• May 26 — notification of acceptance.
• June 16 — payment deadline.
• July 1 — cancellation deadline (refund — 50% of the payment).

If you are willing to participate in person and you need a visa to enter Russia, we strongly recommend applying for it as soon as possible since it might require up to three months for the paperwork to be processed. Furthermore, amid the pandemic-induced uncertainty there can be no assurance that travel will go back to normal by July. If you pay the participation fee but ultimately find yourself unable to travel to St. Petersburg, you will be automatically enrolled as an online participant. More on the mixed format of the School and travel.

Participants must be fluent in English, the working language of the School, and have a command of the basics of quantitative social science research. Please note that hands-on workshops may have individual coding prerequisites. Participation in the (offline-only) eye tracker tutorial requires proficiency with R, and those taking the O-Tree online tutorial are expected to be familiar with Python.

If you need to cancel your registration, send us an email (do not forget to include your full name). Please be aware of our refund policy:

• Refund until July 1: 50% of payment.
• Cancellation/withdrawals received after July 1 will not be eligible for a refund.

This course will cover the basics of experimental planning and execution, including theoretical questions, types of experimental designs and their comparative advantages and disadvantages, experimental validity, etc. When it comes to social sciences experimentation, a lot of difficult questions arise. How do I formulate the hypotheses of my experimental study? How do I choose the most appropriate design? What is the best way to define a sample? How to create the stimuli? Should I keep my experiment as simple as possible or should I try a complex design? We will review the aspects of experimental planning relevant for all social sciences, with the focus on studies in psychology.

This course will cover the design of online experiments, specifically focusing on the implementation of online treatments and measurement of attitudinal and behavioral variables online. We will discuss the advantages of web-based designs for collecting data on a large scale, as well as for implementing the lab-in-the-field and natural experiments. The course will use examples from the existing research, but also give students a chance to participate in a real online experiment. Furthermore, the course will cover common problems and limitations of online experimentations and strategies of dealing with them. Specifically, we will talk about possible issues with data quality and participant motivation. Best practices of online research will also be discussed.

This course will have a section introducing students to causal inference from experimental and quasi-experimental designs. Particularly, students will learn how to collect the data so as to establish that the relationship in question is causal.

Emotions are the essence of what makes us human. They can serve as foundations for decision-making, mechanisms through which other events occur, or as responses to external stimuli. In this course we will introduce basic theories of emotions and discuss experimental design strategies in conducting research examining emotions. Among them, we'll explore strategies and issues related to:

• Designing studies using biometric indicators
• Designing studies using self-report indicators
• Working with open-ended and qualitative approaches for triangulation

We will review these aspects and others, with a focus on studies in communication.

Social simulations in virtual research environments allow researchers to gain new insights into the complex ways that societies function. In this course, we will introduce agent-based modelling as a theory-construction tool for the social sciences. We will cover both theoretical and practical aspects of agent-based modelling: starting with an introduction to complex adaptive systems and foundational agent-based models, up to more advanced simulation experiments and calibration methods. We will focus on social phenomena such as residential segregation, epidemiological and social contagion, and opinion polarization. We will introduce the NetLogo programming language and learn by modifying existing models from its library. No prior programming background is required, but participants should be ready to see and write some code during the course.

The lecture will cover the basic assumptions and rationale of eye-tracking research of reading, introduce the students to its main experimental paradigms and the basic approaches to data collection and reduction.

Online experimentation has enabled relatively cheap and fast data collection on a massive scale, making familiarity with various toolkits for online experiments an indispensable skill for researchers. This tutorial offers an overview of one such platform, Pavlovia, which is based on the PsychoPy software package. Students will practice building their own experiments in PsychoPy and transferring them to Pavlovia.

From ‘Do we split the bills on our first date’ to ‘Should we offer a bribe to a road police officer’ our day-to-day behavior is largely driven by social norms. Social norms are our expectations about what others would do in a similar situation and what they expect us to do. Even if the norms are fundamental for understanding human behavior, studying how they appear, are maintained or die is a hard nut to crack. Standard observational methods such as surveys are often prone to social desirability bias and field observations are not always feasible. In this course we show how social norms can be studied by the way of behavioral experiments both in their statics (what kind of norms an individual follows in a specific context), and dynamics (what factors make the norms appear or fade out). Using the oTree platform we will learn how to design, program and conduct some pivotal experiments on social norms.

This workshop will consist of a series of hands-on sessions designed to help learners incorporate eye-tracking methodologies in their research. The first section is an introduction to eye tracking covering eye physiology, basic eye movements and oculomotor events measured. The second section will cover eye-tracking hardware, focusing on types of equipment and participant setup. The third practical training session will focus on recording eye movements of participants scanning social media webpages. The final session will cover data export and analysis.

Comparison of groups receiving different treatments is the core of experimental data analysis and is usually addressed with ANOVA. While the latter is derived from the same statistical framework as linear regression, in ANOVA hypothesis testing is relatively limited. Linear regression allows more specific hypothesis testing, but its specification and interpretation requires some understanding of the concept of contrasts. In this hands-on workshop, we will look at this concept and common contrast coding schemes more closely. The hypr package in R will aid our exploration of the link between statistical hypotheses and contrast codes. During the course, we will talk about different contrasts, how to validate the underlying statistical hypotheses of a given contrast coding scheme, and how to derive contrasts for testing our own hypotheses in a linear regression.