This course covers the methodology for statistical inference with data from surveys that are not based on a simple random sample from a population. Statistics of interest covered are: totals, functions of totals (e.g., means or ratios), quantiles, proportions of categorical variables and regression coefficients for linear and generalized linear models. The course is mainly focused on the estimation of sampling variances. Students will learn how complex sampling designs, unit-nonresponse, and survey weights affect the variance of estimators and how this variance can be estimated. A strong emphasis is given to practical examples where students will learn how to implement the taught methods in the statistical software R

The goal of research ethics is to protect human subjects from harm when they participate in a study. In the digital age, however, what constitutes “participation” has become blurry, especially with the rise of social media platforms and other online apps and services. Furthermore, new applications of big data raise important questions about how to protect consumers from harms, and what kinds of notice and consent should be obtained. This course provides and introduction and overview of research ethics in the 21st century and evaluates the many challenges to conducting ethical research.

This course introduces the student to a set of principles of survey and data science that are the basis of standard practices in these fields. The course exposes students to key terminology and concepts of collecting and analyzing data from surveys and other data sources to gain insights and to test hypotheses about the nature of human and social behavior and interaction. It will also present a framework that will allow the student to evaluate the influence of different error sources on the quality of data.

The amount of data generated as a by-product in society is growing fast, including data from satellites, sensors, transactions, social media and smartphones, just to name a few. Such data are often referred to as “big data”, and can be used to create value in different areas such as health and crime prevention, commerce and fraud detection. Big Data are often used for prediction and classification tasks. Both of which can be tackled with machine learning techniques. In this course we explore how Big Data concepts, processes and methods can be used within the context of Survey Research. Throughout this course we will illustrate key concepts using specific survey research examples including tailored survey designs and nonresponse adjustments and evaluation.

In this self-paced course, students will be familiarised with the DNA of official statistics, what quality means, how to achieve it, especially under the challenges of modern societies. In times of data revolution, artificial intelligence and a ubiquitous flood of permanently produced and consumed information, orientation and differentiation of good and less good information, of fact-checks, and of verification of statistical evidence is crucial. The public infrastructure called 'official statistics' plays an essential role in this respect.

This short course will provide an introduction to R, a language and environment for statistical computing and graphics.

The course will address methods to combine data on given entities (people, households, firms etc.) that are stored in different data sources (e.g., surveys, administrative records, social media). By showing the strengths of these methods and by showing how each of them are performed in practice using R, the course will demonstrate the various benefits of record linkage. Participants will also learn about potential challenges that record linkage projects may face.

Social scientists and survey researchers are confronted with an increasing number of new data sources such as apps and sensors that often result in (para)data structures that are difficult to handle with traditional modeling methods. At the same time, advances in the field of machine learning (ML) have created an array of flexible methods and tools that can be used to tackle a variety of modeling problems. Against this background, this course discusses advanced ML concepts such as cross validation, class imbalance, Boosting and Stacking as well as key approaches for facilitating model tuning and performing feature selection. In this course we also introduce additional machine learning methods including Support Vector Machines, Extra-Trees and LASSO among others. The course aims to illustrate these concepts, methods and approaches from a social science perspective. Furthermore, the course covers techniques for extracting patterns from unstructured data as well as interpreting and presenting results from machine learning algorithms. Code examples will be provided using the statistical programming language R.

This course will provide a detailed introduction to multiple imputation, a convenient strategy for dealing with (item) nonresponse in surveys. We will motivate the concept and illustrate why multiple imputation should generally be preferred over single imputation methods. The main focus of the course will be on strategies to generate (multiple) imputations and how to deal with common problems when applying the methods for large scale surveys. We will also discuss various options for assessing the quality of the imputations. All concepts will be demonstrated using software illustrations in R.

In this course, students will apply the core skills that they learned in the MDM program to address real-world problems. The course will provide experience with the steps involved in carrying out a data consulting project, such as discussing the problems to solve with a client, data handling, and communicating work in both written and oral forms. The project is completed in teams (3-4 students per team).

This course provides a brief overview of the basics of probability and statistics. Students will review basic probability concepts and probability distributions, the Central Limit Theorem and hypothesis testing, and linear and logistic regression. Throughout this course, students should develop and reinforce proper statistical intuition. This includes knowing how to identify a sample and a population and applying appropriate statistical methods such as hypothesis testing, as well as being able to identify different types of data and using the proper methods for each type of data. By the end of the course, students should have a strong foundation in statistics with which they can start their graduate coursework.

Sampling II is an applied statistics methods course, but differs from most statistics courses because it is concerned almost exclusively with the design of data collection. Little of the analysis of collected data will be discussed in the course. The course will concentrate on problems of applying sampling methods to human populations, since sampling human populations poses a number of particular problems not found in sampling of other types of units. The principles of sample selection, though, can be applied to many other types of populations.

This course introduces the concepts of usability and usability testing and why they are needed for survey research. The course provides a theoretical model for understanding the respondent-survey interaction and then provides practical methods for incorporating iterative user-centered design and testing into the survey development process. The course provides techniques and examples for designing, planning, conducting and analyzing usability studies on web or mobile surveys.