All Courses
On this page you can find all the courses and tracks offered by the Utrecht Summer School. Check the course pages for more detailed information.
16 course(s) found
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.…
1
Molecular Epidemiology of the Exposome and Chronic Disease
This course will provide students with an introduction into using molecular epidemiology to study the exposome. There is a specific emphasis on the use of molecular markers to characterize relationships between exposures to environmental factors and chronic diseases.
1
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.
1
Advanced Multilevel Analysis
This three-day course will teach you advanced topics in multilevel modelling. It builds upon the contents of the other summer 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 programmes will be used. After taking this course, you should be able to analyse more complex multilevel models and interpret and report the…
1
An introduction to Qualitative Research Methods
This summer 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 Grounded…
1
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.
1
Global Maternal, Newborn and Child Health
This summer course will provide a comprehensive overview of maternal, newborn and child health globally. With an enthusiastic team of experts from all over the world, we will discuss the major challenges which women and children in the world are facing, and we will look at potential solutions. This programme is a collaboration between the University Medical Centre Utrecht (Julius Global Health, Wilhemina Children's Hospital), Amsterdam Centre…
1
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…
1
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,…
1
3D Printing and Biofabrication
Additive manufacturing (3D printing) uses a layer-by-layer principle for manufacturing. This one-week course will provide an insight into the opportunities of additive manufacturing technologies and 3D (bio)printing in biomedical applications. It will deliver the basics of 3D printing, including hands-on workshops with 3D printers, in which the students will interact with experts demonstrating the working principles and applications of multiple…
1
Survival Analysis
A one-week course in survival analysis for the life sciences. Concrete examples and case studies are used to apply the theory to practical situations. Survival data, or more generally, time-to-event data (where the 'event' can be death, disease, recovery, relapse or another outcome), is frequently encountered in the biomedical sciences. Censoring is a problem characteristic to most survival data, and requires special data analytic techniques.
1
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…
1
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.
1
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…
1
Biostatistics for Researchers
The course provides an introduction into statistical methodology for life sciences and it discusses a number of statistical techniques for practical data analysis. Concrete examples and case studies are used to apply the theory to practical situations. The statistical techniques covered are several t-tests, chi‐square tests, analysis of variance (ANOVA), (multiple) linear and logistic regression and survival analysis. The course ends with a…
1
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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