Ga naar de startpagina
Zoek leermateriaalKennis & expertiseCommunities
NL|EN
Privacy policy
Creative CommonsCreative Commons AttributionSURFFunded by the European Union

Filteren

Zoekresultaten (70)

…

HBO/WO

MBO

3 maart 2026

Quick and dirty multilevel regression with SPSS syntax assignment

SPSS syntax assignment for teaching the basics of multilevel modeling. For when you need to teach or learn the basics but don't have a lot of time for it. Covers pooled OLS, fixed-effects and random effects (null model, intraclass-correlation, random intercept models with covariates at different levels, random slope models, and cross-level interactions). Uses European Social Survey data. Includes questions. Intended to be used in conjunction with a preceeding lecture.

Document

Alex Lehr

15 december 2025

Systems Thinking Pressure Cooker

What – This pressure cooker is an introduction to systems thinking. This pressure cooker can be used to quickly familiarize a group of people with what the characteristics of a system are and have them experience various examples of using systems thinking to explore a topic or question. For whom – The format can be used (and adjusted) for a team of teachers that are not yet familiar with systems thinking, as well as a group of students (all levels; any group size). Why – Guided training in integration of systems thinking in a research project. How long – Approximately 2-2,5 hours in class and a 10 minutes pre-class video.

Presentatie

Programma Interdisciplinair Onderwijs

6 juni 2025

Multilevel modeling for social scientists

The course builds on the knowledge and skills acquired in the previous methods courses. The course will introduce students to multivariate analyses of nested data using multilevel modeling techniques. The course focuses on the theory and application of fixed and, in particular, random effects models. Using data from the European Social Survey, the students will learn how to estimate and interpret such as models using Stata.

Document

Alex Lehr

6 juni 2025

How To Write A Quantitative Research Paper

So, you need to write a ‘quantitative’ research paper, and you want to know: what is the correct way of writing such a paper? The answer is, unfortunately: it depends … For one, there is no universally accepted, ‘correct’ way of writing any paper in the social sciences in general. Moreover, even just within the so-called ‘quantitative’ research tradition specifically, there exist different viewpoints on what makes for a good research paper. To muddy things up even further, how to write a paper will also depend on the specific research question(s) that it addresses. Hence, even the same scientist might opt for varying approaches in different papers. Having said that, it is possible to identify some common rules and practices that are often followed, as well as a number of practices that should generally be avoided. Based on these common rules and practices, this document is meant to help you on your way with writing a ‘quantitative’ research paper by offering a number of tips, tricks and rules-of-thumb. None of these rules and practices are set in stone, and you (or your teacher/supervisor) may have excellent arguments to deviate from them. However, in general, following them will greatly increase the quality of your paper.

Document

Alex Lehr, Roderick Sluiter

10 maart 2025

EPIFFANI Project Results

These are the combined results for the EPIFFANI Project by Prof. Nicolas Mangin. This project was funded buy SURF's Stimuleringsregeling open en online onderwijs. The aim of the project was to build and test a set of four open source applications for R/integration into the LMS, which allowed teachers to easily automate formative testing through IT solutions. With these applications, teachers can easily and quickly create automate the processing of making tests with personalized feedback for students. The results have three elements: A full project report, which provides context for the creation and creation process A link to Youtube video demonstration of the tools, made for the University of Groningen's education festival 2022 A link to the Github page of project lead Nicolas Mangin. On this page, all the packages can be downloaded, and documentation and instruction are available All tools are licensed under a GPL-3 license, but as this license is not available, it has been noted as CC BY-SA here.

Document

Nicolas Mangin

15 oktober 2024

The Delft Method for System Dynamics - Modelling Exercises

These modelling exercises are part of the TU Delft Open textbook "The Delft Method for System Dynamics". The Delft Method for System Dynamics (SD) is a proven method of learning basic System Dynamics. The method focusses on learning by doing: first you try to make an exercise, and if you do not understand something or do not know something, then you can look it up in the theory part of the book. The book contains theory exercises on topics like causal loop diagramming, delays, and how to motivate why SD is appropriate. The book also contains modelling exercises that show students how to build low to medium complexity models, and how to use these quantitative models for scenario or policy analysis. The theory chapters cover all phases of the modelling cycle: problem articulation, conceptualisation, formulation, evaluation (including validation and scenario analysis), and policy analysis. This book is intended for students and educators in large or small System Dynamics courses, and for motivated students that want to learn SD in their own pace.

Document

Willem Auping, Floortje d’Hont, Merla Kubli, Patrick Steinmann, Jill Slinger, Floris van der Heijde, Erik Pruyt, Els van Daalen, Wil Thissen

23 augustus 2023

Lecture: Decision Networks

Naive Bayesian networks Bayesian networks Decision networks Students can define a multivariate joint distribution: multinomial and Gaussian Students can explain the difference between association versus causation Students can define observational, causal, and counterfactual inference Students can define fairness using observational and counterfactual reasoning

Website

HCAIM Consortium

23 augustus 2023

Tutorial: Decision Networks

Naive Bayesian networks Bayesian networks Decision networks Students can define a multivariate joint distribution: multinomial and Gaussian Students can explain the difference between association versus causation Students can define observational, causal, and counterfactual inference Students can define fairness using observational and counterfactual reasoning

Website

HCAIM Consortium

23 augustus 2023

Lecture: Exploratory Data Analysis

Naive Bayesian networks Bayesian networks Decision networks Students can define a multivariate joint distribution: multinomial and Gaussian Students can explain the difference between association versus causation Students can define observational, causal, and counterfactual inference Students can define fairness using observational and counterfactual reasoning

Website

HCAIM Consortium

23 augustus 2023

Tutorial: Exploratory Data Analysis

Naive Bayesian networks Bayesian networks Decision networks Students can define a multivariate joint distribution: multinomial and Gaussian Students can explain the difference between association versus causation Students can define observational, causal, and counterfactual inference Students can define fairness using observational and counterfactual reasoning

Website

HCAIM Consortium