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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This course describes the stages involved in Bayesian analysis: specifying the prior and data models, deriving inference, model checking and refinement. We discuss prior and posterior predictive checking, and selecting a technique for sampling from a probability distribution. Other topics discussed are: approximate measurement invariance (a Bayesian method to assess comparability of data), evaluating hypotheses via the Bayes Factor and information criteria, and combining evidence from multiple studies addressing the same research question. Finally, we propose strategies for reproducibility and reporting standards, outlining the WAMBS-checklist (when to Worry and how to Avoid the Misuse of Bayesian Statistics).
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 HLM and Mplus 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. Categorical outcomes are part of the course Advanced Multilevel.
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 both SPSS and R. Topics include complex sampling, nonresponse adjustment, measurement error, analysis of incomplete data and advanced use of administrative data. Special attention will be given to the analysis of complex surveys that include weighting, stratification and design effects.
Changes in technology and society strongly influence modern survey research. This course covers the essentials of modern survey methodology, organised by the Department of Methodology and Statistics in collaboration with Statistics Netherlands (CBS). 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 to implementation, analysis and reporting will be discussed.
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. During the course, we do not discuss the mathematical details of these techniques.
This course in ‘advanced survey design’ takes students beyond the introductory courses and will discuss the state of the art in both the design and the analysis of survey data. We discuss new ways to analyse text data and sensor data generated by modern surveys. Course participants must be proficient working with statistical software (Stata, SPSS or R). Course materials have been developed in R, but most exercises on days 1-3 is in SPSS or STATA.
This three day course will teach you advanced topics in multilevel modelling. The three-day course 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. After taking this course, you should be able to analyse more complex multilevel models and to interpret and report the results.
How do meteorologists forecast the weather and climate? Is there a way to predict the profit from a wind farm? These are some of the questions modern science addresses by using data assimilation. Many research institutes and companies (e.g. KNMI, Shell, US-NCAR or UK MetOffice) develop and employ data assimilation and the demand for trained personnel is constantly growing. The school will describe the theoretical foundation of data assimilation together with numerical tutorials, all the way to state-of-the-art methods, including modern machine learning approaches and their combination with data assimilation.
This course is a hands-on course on GIS: 2 weeks of practicals in the GIS Lab of the faculty of Geosciences. The following topics will be covered: Working with ArcGIS Pro and Spatial Analyst, Modelbuilder and Python, automatic DEM extraction of stereo aerial photographs using Erdas Imagine eATE and Agisoft, Mobile GIS / GPS data collection, local/global datasets and datatypes, and poster making.
The accelerating datafication of society constitutes challenges and opportunities for humanities research. Participants of this course get acquainted with some of the (methodological) fundamentals of data practices in the Digital humanities. These will include data collection, data preparation, data visualisation, critical data and algorithm studies, network analysis, and an introduction to programming in Python. After training these skills for three days, the participants work in small teams on a hands-on case.