Social Sciences Courses
Under the heading Social Sciences, Utrecht Summer School offers courses in the fields of Migration Studies, Communication, Psychology, Linguistics and Geosciences. Our offer also includes a large selection of Data Science courses, covering subjects such as Survey Research, Multilevel Analysis and Qualitative Statistics.
31 course(s) found
Eye Tracking Research Toolbox
Eye tracking is a powerful method to study the human mind and behaviour. This course will allow you to explore key concepts in eye tracking research and help you integrate it in your study. The course is divided into two components: (1) a conceptual framework to help you make better decisions when planning and executing a study, allowing you to turn eye tracking data into valuable insights; (2) a practical introduction to the challenges and…
Introduction to R
This workshop will introduce participants to the R statistical programming language. R is a completely free and open-source programming language and environment for statistical analysis. In this workshop, participants will learn what R is and how it differs from other statistical software packages and programming languages. They will learn the basics of data I/O, manipulation, and visualization in R. Workshop participants will practice what they…
Regression in R
Linear regression is one of the most ubiquitous statistical methods. Most statistical techniques can be viewed as either special cases of linear regression (e.g., t-tests, ANOVA) or generalizations of linear regression (e.g., multilevel modeling, SEM, neural networks, GLM, survival analysis). In this workshop, participants will learn how to apply linear regression techniques in R. We will cover (multiple) linear regression, categorical predictor…
Introduction to Python (online course)
If you are looking for a powerful programming language, you should learn Python, a language with a simple syntax and a powerful set of libraries. It is easy for beginners to learn Python and it is widely used in many scientific fields for data exploration. This online workshop is an introduction to the Python programming language and, in particular, is geared toward people who are new to the language and who have relatively little experience …
Missing Data in R
Missing data are ubiquitous in nearly every data analytic enterprise. Simple ad-hoc techniques for dealing with missing values such as deleting incomplete cases or replacing missing values with the item mean can cause a host of (hidden) problems. In this workshop, we will discuss principled methods for treating missing data and how to apply these methods in R. We will cover some basic missing data theory and two principled methods for treating…
Introduction to (Bayesian) Hypothesis Evaluation
In this one-day course, participants will be introduced to informative hypotheses, which are alternatives for the traditional null and alternative hypotheses, and to the AIC-type criterion GORIC and the Bayes factor, which are alternatives for the p-value. This course is applied, hands-on, and build around concepts and not formulas.
Text Analysis with Python (online course)
This online course introduces the basic concepts of text analysis in Python. In this course, participants will learn how to apply text mining methods on text data and analyse them in a pipeline with machine learning and natural language processing algorithms. The course has a strongly practical hands-on focus, and participants will gain experience in using text mining on real data.
Introduction to Network Inference and Network Learning in R: Markov random Fields and Bayesian Networks
Unlock the Power of Network Inference Methods in the Real World, All in One Exciting Day! In this introductory course, I will provide a brief overview of the two types of graphical models: undirected (Markov random fields) and directed (Bayesian networks). I will explain how these models can be applied in the real world and how to estimate (and interpret) the dependencies between variables of interest (in the form of a network) using various…
Modelling the Dynamics of Intensive Longitudinal Data (online course)
This online course covers how time series models can be used to model the dynamics of intensive longitudinal data (ILD). We focus on ILD from the behavioural sciences, typically collected using questionnaires with ambulatory assessments (AA), the experience sampling method (ESM), ecological momentary assessments (EMA), or daily diaries. We explain the basics of simple and more advanced modelling approaches, the philosophies behind them, and…
Structural Equation Modeling in R using lavaan (E-Learning Course)
Online course
In this e-learning course, we will cover the basics of structural equation modeling (SEM) using the R package lavaan. Participants will learn how to interact with the lavaan software and how to run common types of structural equation models (e.g., path models, confirmatory factor analyses, latent regression models, multiple group models) using lavaan. No prior knowledge of lavaan is necessary, but some experience with R (although not strictly…
Rhetoric Recrafted: Unleashing Your Superpower for the Modern Age
Online course
Are you surprised by your neighbour Karen's political support for the corrupted politician? Do you think Chad, your childhood friend, would really be happier if we all stuck to two genders, had no vaccines for children and only consumed cow milk?
In this rapidly evolving landscape, individuals equipped with critical thinking, empathetic insight, public speaking skills and flexibility will distinguish themselves, safeguarding democratic…
Data Science: Multiple Imputation in Practice (Hybrid)
Online course
This 4-day hybrid course by the MICE developers teaches you the basics in solving your own missing data problems appropriately. Participants will learn how to form imputation models, how to combine data sets, how to model non-response, how to use diagnostics to inspect the imputed values, how to obtain valid inference on incomplete data and how to avoid many of the pitfalls associated with real-life missing data problems. While there will be…
Data Science: Statistical Programming with R
This course offers an elaborate introduction to statistical programming with R. Students learn to operate R, data manipulation and data visualisation, work with the (generalized) linear models, conduct simulation studies (e.g. bootstrap) and present results of data analyses in publication ready tables. We will work with the RStudio environment and Quarto. The focus will be on the tidyverse package.
Migration, Integration and Ethnic Relations
Migration has made European countries ethnically and culturally more diverse. While migrants try to find their way in a new and sometimes hostile environment, host populations also have to adapt to migrants and the new religious and cultural diversity they bring with them. Migrants and host populations face the challenge of acceptance and adaptation in spite of initial resistance, possible social exclusion and explicit political rejection by…
Data Science: Programming with Python
Python has become the dominant programming language used in data science. This course offers an introduction to computational thinking about data-related problems and the implementation of data analysis programmes with Python. It starts at the very basics and is explicitly intended for students who have no or only little programming experience.
An introduction to Qualitative Research Methods
This Summer School Course will introduce you to qualitative research methods. We will discuss the philosophical foundations of qualitative research and indicate how to assess its quality. You will receive an interview training, conduct field observations to collect data, and write a vignette – a form of writing common to ethnography. We will provide an overview of data analysis methods and you will practice with inductive coding, following a…
Introduction to Multilevel Analysis
This course will teach you the theoretical basics of multilevel modelling and some important methodological and statistical issues. You will also learn how to analyse multilevel data sets with the R and R Studio programmes, to interpret the output and to report the results. The benefits of multilevel analysis are discussed both in theory as with empirical examples. This course restricts to a quantitative (i.e. continuous) outcome variable.…
Data Science: Data Analysis
The course Data science: Data Analysis offers a range of techniques and algorithms from statistics, machine learning and data mining to make predictions about future events and to uncover hidden structures in data. The course has a strong practical focus; participants actively learn how to apply these techniques to real data and how to interpret their results. The course covers both classical and modern topics in data analysis, such as…
Advanced Multilevel Analysis
This three-day course will teach you advanced topics in multilevel modelling. It builds upon the contents of the other summer school course “Introduction to multilevel analysis”. It consists of three days with lectures in the morning and computer labs in the afternoon. During the computer labs the R and R studio programs will be used. After taking this course, you should be able to analyse more complex multilevel models and to interpret and…
Data Science: Network Science
How can networks help us understand and predict social systems? How to find important individuals and communities? How to predict unobserved connections between genes? How to learn the dependencies between interrelated entities? How can we stop disease or information spreading in networks? In this course, we provide participants with the conceptual and practical skills necessary to use network science tools to answer social, economic and…
Data Science: Applied Text Mining
This course introduces the basic and advanced concepts and ideas in text mining and natural language processing. In this course, students will learn how to apply text mining methods on text data and analyse them in a pipeline with machine learning and deep learning algorithms. The course has a strongly practical hands-on focus, and students will gain experience in using text mining on real data from social sciences, humanities, and healthcare…
Fundamentals of Coaching: Theory and Practice in Experiential learning
"Ever thought about entering into the world of coaching? This course focuses on the basics of coaching - perfect for those eager to kickstart their learning journey as coach! Whether you're a newbie or just curious, this course is designed for you. Dive into the coaching essentials, learn hands-on, and set off on the path to becoming a certified coach. No prior experience needed just bring your enthusiasm! Throughout the course, discover the…
Transformational coaching - mastering personal and professional development
Transformational coaching is about supporting others to live their full potential. This course is for students and professionals that want to develop their coaching skills. It is also for individuals that want to reflect deeply on who they are and what matters most to them. And it is for leaders that want to reinforce their coaching skills in order to strengthen their leadership. In every session you will learn and practice with powerful…
A gentle introduction to Bayesian Estimation
This course introduces all the essential ingredients needed to start Bayesian estimation and inference. We discuss specifying priors, obtaining the posterior, prior/posterior predictive checking, sensitivity analyses, and the usefulness of a specific class of priors called shrinkage priors. We propose strategies for reproducibility and reporting standards, outlining the WAMBS-checklist (when to Worry and how to Avoid the Misuse of Bayesian…
Introduction to Structural Equation Modeling using Mplus
We offer a five-day course on how to perform basic SEM analyses using Mplus. The main objective of this course is to learn how to analyse several models with Mplus (e.g., path models, multiple group models, mediation and moderation, confirmatory factor analysis, and longitudinal models). No previous knowledge of Mplus is assumed, but prior knowledge of SEM, although not mandatory, will make this course more useful.
Applied Multivariate Analysis
The summer school course 'Applied Multivariate Analysis' offers hands-on experience using SPSS for the most frequently encountered multivariate statistical techniques in the social and behavioural sciences. The emphasis is on applying multivariate techniques using the computer programme SPSS, and on how to interpret the SPSS output in substantive terms.
Beyond null-hypothesis testing: Transparent and informative methods in three statistical frameworks
This 3-day course teaches skills for informative and transparent evaluation of theory-based hypotheses using p-values, information criteria, and Bayes factors. Participants will learn to apply open-science principles and cutting-edge statistical techniques that ensure maximally informative analyses. Contemporary issues - such as publication bias, questionable research practices, the statistical evaluation of (non-null) hypotheses, and methods…
Advanced longitudinal modeling in Mplus
This is a five-day course on structural equation modeling (SEM) using Mplus. In this course, SEM experts will teach you about the fundamentals of SEM and various types of longitudinal data analysis techniques, such as growth curves analysis, cross-lagged panel models, and dynamic structural equation modeling (DSEM). The course consists of in-depth lectures and computer lab meeting on the fundamentals of Mplus and on advanced longitudinal models.
Survey Research: Statistical Analysis and Estimation
The course is based on a total survey error perspective and discusses the major sources of survey error. Participants will be presented with tools for detection and adjustment of such errors. Analysis methods are introduced using R statistical software. Knowledge of R is beneficial but not required if you have worked with SPSS, Stata, SAS or other statistical software already. Topics include coverage errors, complex sampling, nonresponse…
Data Science: Text Mining with R
Applications of text mining are everywhere: social media, web search, advertising, emails, customer service, healthcare, marketing, etc. In this course, students will learn how to apply text mining methods on text data and analyse them in a pipeline with statistical learning algorithms. The course has a strongly practical hands-on focus, and students will gain experience in using and interpreting text mining on data examples from humanities,…
AI-Aided Systematic Reviewing (online course)
Online course
More and more researchers rely upon Systematic Reviews: attempts to synthesize the state of the art in a particular scientific field. However, the scientific output of the world doubles every nine years. In this tsunami of new knowledge, there is not enough time to read everything – resulting in costly, abandoned, or error-prone work. Using the latest methods from the field of Artificial Intelligence (AI), you can reduce the number of papers to…