All Courses
On this page you can find all the courses and tracks offered by the Utrecht Summer School. Detailed information is published on all individual course pages.
12 course(s) found
Eye Tracking Research Toolbox
Eye tracking is a powerful method to study the human mind and behavior. 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 trade…
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Introduction to Structural Equation Modeling using Mplus
We offer a 5-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.
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Advanced longitudinal modeling in Mplus
This is a 5-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.
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Data Science: Statistical Programming with R
This course offers an elaborate introduction to statistical programming with R. Students learn to operate R, form pipelines for data analysis, make high quality graphics, fit, assess, and interpret a variety of statistical models, and do advanced statistical programming. The statistical theory in this course covers t-testing, regression models for linear, dichotomous, ordinal, and multivariate data, statistical inference, statistical learning,…
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Survey Research: Design, Implementation and Data Processing
Changes in technology and society strongly influence modern survey research. This course covers the essentials of modern survey methodology, organized by the Department of Methodology and Statistics. Central to the course is survey quality and the reduction of Total Survey Error (coverage, sampling, nonresponse, including questionnaire and mode effects), while balancing logistics and survey costs. Best practice guidelines for surveys from design…
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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…
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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 programs, 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.…
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Structural Equation Modelling in Mplus
If you expect to work with the software Mplus, this course can help you to get started! This course is a compact 1-day workshop introducing Mplus. We will focus on preparing data for Mplus, introducing common model syntax, avoiding common mistakes, interpreting output, and dealing with common error messages. Practice exercises demonstrate multiple regression and factor analysis models, which are the basis of structural equation models in Mplus.
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Introduction to Graphical Models (for Network Inference) in R: Markov random Fields and Bayesian Networks
Probabilistic graphical models (PGMs) represent the world in a simple way that humans can understand better.
They are graphical representations to understand the complex relationships between a set of random variables of interest (nodes). The edges that connect these nodes show the statistical dependencies between them.
PGMs have numerous applications in social and behavioral sciences, life sciences, economics, computer science, and many more…
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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.
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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…
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An introduction to Qualitative Research Methods
In this Summer School course, you will receive an introduction to qualitative research methods. We will discuss the philosophical grounds of qualitative research and indicate how to check the quality of qualitative research. You will receive an interview training and you will be working on a vignette – a form of writing/reporting common to ethnography. We will give an overview of the methods of data analysis, and you will practice with inductive…
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