Courses

Below, you will find an overview of courses from the current academic year of this Master's. This overview is meant to give you an idea of what to expect. The course offer may change in the coming academic year.

Year 1, semester 1

Survey data analysis

TESTING AND COURSE AIMS

Two tests will be given in this course:
- An individual assignments (60%):
a Students develop knowledge and understanding of survey data collection methods (Knowledge and Understanding)
b. They can apply methods for the analysis of complex survey data in different settings (Applying)
c. They develop knowledge in designing survey research methods (Knowledge and Understanding)
d. They are able to investigate research hypothesis using survey methods (Applying)
e. They are capable of autonomous scholarly self-development (Learning skills)

- Presentation of a group assignment (40%)
a. Students are able to present research findings and to report informative insights (Communication)
b. They are able to use R software for survey data analysis (Applying)
c. They develop skills in designing and applying survey research methods (Applying)
d. They give proof of being a responsible and scholarly professional (Learning skills)

This course provides a solid foundation in the theory and methods of modern survey sampling. Statistical methods for analyzing survey data will be discussed from a design-based perspective, where the only source of random variation is induced by the sampling mechanism. The basic techniques of survey sampling will be discussed; simple random sampling, stratification, systematic sampling, cluster and multi-stage sampling, and probability proportional to size sampling. The course also covers methods of variance estimation for complex sample designs and several specialized topics. More advanced topics include model-assisted regression methods for continuous and categorical data, and ratio estimation. Lab meetings are organized to analyze survey data using R and the survey package. This course considers the nature of various general methods, the supporting statistical theory, but also practical applications.

Multivariate statistics for MSBBSS

In this course we will refresh and elaborate on multivariate statistics, like the analysis of variance model including
repeated measure analysis, and the regression model including dummy variables, interaction, and logistic regression. It is expected that students have encountered these models in their bachelor programme but in this course they will be dealt with at a more in-depth level. Furthermore, some special topics that are relevant for all applications of multivariate statistics will be treated. These include missing data and imputation methods, contrast testing, bootstrapping and a teaser on Bayesian methods. Also, several papers will be read and discussed in which recent developments or limitations in the area of statistics are presented including p-hacking, questionable research practices, the replication crisis, multiple testing, capitalization on chance and power issues.

Fundamentals of statistics

TESTING AND COURSE AIMS In this course, two exams are administered. The exams are used to test knowledge of the topics discussed and the ability to solve mathematical statistics problems. The grade for each test counts for 40% of the final grade. Each test must be passed (minimum 5.5). In addition, there will be an assignment each regular week. Students can work on the assignments together but the work should be handed in individually. Some of these assignments will be graded. Beforehand it is not known to the students which assignments will be graded. The mean of the grades for the assignments counts for 20% of the final grade.

This course provides an introduction to mathematical statistics that is relevant to empirical research. The main topics are: mathematical requirements (differentiation, integration, numerical procedures), counting techniques, probability theory, general properties of probability distributions and densities, special probability distributions and densities, expectation, moments, sampling theory, point estimation (properties of estimators, method of moments, least squares estimation, maximum likelihood estimation, Bayesian estimation), hypothesis testing theory and applications (small sample techniques, likelihood ratio test, Wald test), analysis of variance, and regression.

Computational inference with R

Statistical inference based on intensive computation or simulation is an important part of the armamentarium of a statistician. Computational statistics concerns the development, implementation and study of computationally intensive statistical methods. Such methods are often used e.g. in the fields of data visualization, the analysis of large datasets, Monte Carlo simulation, resampling methods such as the bootstrap, permutation methods and various numerical methods of equation solving such as the EM algorithm and Newton-Raphson iteration. This course will present essential methods in computational statistics in a practical manner, using real-world datasets and statistical problems. In addition to a basic introduction to R, it will include evaluating and comparing the performance of different statistical techniques in a specific setting using simulation and implementing the bootstrap to obtain a standard error estimate which is not available in closed-form. We will also develop more advanced R programming skills. At of the end of the course, the student: is able to implement and use basic computational methods for statistical inference, as well as more advanced ones such as the bootstrap and permutation test; will have developed fundamental and computationally efficient R programming skills; is able to conduct and report on simulation studies, comparing the performance of statistical methods in specific settings; is familiar with some widely used numerical methods; will be able to translate new statistical methods from the literature into a usable R program.

Year 1, semester 2

Psychometrics

TESTING

  • Written examination of psychometric theory, including classical test theory, generalizability theory, (Bayesian) item response theory and latent regression, Bayesian psychometric modeling. Furthermore, two home assignments based on the computer lab are also graded. The final grade is determined by the written exam (60%) and the two home assignments (20% each).
  • Practical using R to conduct G- and D- study and interpret and report results.
  • Practical using R (including IRT-software) to conduct a linking and dif study and interpret and report results.
  • Practical using R and Jags to conduct IRT-latent regression analysis interpret and report results.
  • Practical using R and Jags to conduct multilevel IRT-latent regression, Bayesian psychometric modeling, analysis interpret and report results.

Relatie tussen de toetsen en leerdoelen
The course gives a broad introduction to the field of psychometrics, followed by a number of advanced topics which give an impression of current developments. The introduction will cover classical test theory, generalizability theory and item response theory (IRT). As applications of IRT, the topics of test equating and differential item functioning will be presented and practiced. These applications will be presented in the framework of maximum likelihood estimation and model testing and the students will learn to use standard state of the art user software. The advanced topics are the combination of Bayesian psychometric methods and complex clustered data. These topics will be addressed in a Bayesian framework, and students will learn to build applications to analyze IRT models using Markov chain Monte Carlo estimation methods. In the lab meetings Jags and R will be used to analyze item response data using Bayesian inference.

Introduction in multilevel and structural equation modelling for MSBBSS

Two techniques that are often encountered are multilevel modeling (MLM) and structural equation modeling (SEM). MLM is appropriate for handling nested data, for instance, patients in hospitals, or occasions in people. MLM can be used to study the within cluster and the between cluster relationships between an outcome variable and predictors. In the lab meetings SPSS and HLM are used. SEM covers both factor analyses and path analyses. It can be used to investigate the underlying factor structure and compare this across groups (i.e., measurement invariance), more complex mediation models, longitudinal data, and to compare distinct theories. In the lab meetings Mplus is used.

Bayesian statistics

In this course the theory and practice of Bayesian data analysis will be introduced. Attention will be given to the difference between classical and Bayesian inference. The following topics will be subsequently be discussed: density of the data, prior and posterior distribution; classical and Bayesian p-values and their flaws; Bayesian estimation; model selection using the DIC; and, model selection using the Bayes factor. In the lab meetings JAGS and R will be used to analyze empirical data using the Bayesian approach.

Introduction to Biomedical Statistics

There is no content available for this course.

Year 2, semester 1

Elective courses and Research experience

Year 2 of the MS-programme includes an elective with 15 EC (credit points) = workload of two regular courses. You attend one or more relevant courses outside the M&S-programme. Options are for instance courses in one of the other Master's programmes in our Graduate school (e.g. a Development and Socialisation in Childhood and Adolescence or Social Health Psychology course). Also other areas (e.g., philosophy of science, mathematics, epidemiology) can be considered. It is also possible to take courses on Survey Methodology, Educational Measurement offered by University of Twente (course code 201500150), or from the track European Master in Official Statistics. Another option is to do a traineeship that is unrelated to the Master’s thesis project but relevant to the field of MS as such, so you get extra experience in conducting scientific research.

Research Seminar

This course runs along the Preparation for the Master's Thesis course (semester 1) and Master’s Thesis course (semester 2). The course mainly focuses on:

  • Structuring and writing skills (research reports, poster and slides)
  • Presentation skills
  • Peer review and feedback
  • Statistical consultation 

Preparation for the Master's Thesis

The preparation for the Master's thesis (15 EC) will take place in the first semester of the second year. You carry out a research project that prepares for the research project carried out in the Master's thesis . The preparation for the thesis runs along with the research seminar. It has the following aims:

  1. to master the state of the art in methodology and statistics with respect to the research topic chosen.
  2. to master research skills in the area of methodology and statistics with respect to the research topic chosen.
  3. to develop experience in writing.

Year 2, semester 2

Elective courses and Research experience

Year 2 of the MS-programme includes an elective with 15 EC (credit points) = workload of two regular courses. You attend one or more relevant courses outside the M&S-programme. Options are for instance courses in one of the other Master's programmes in our Graduate school (e.g. a Development and Socialisation in Childhood and Adolescence or Social Health Psychology course). Also other areas (e.g., philosophy of science, mathematics, epidemiology) can be considered. It is also possible to take courses on Survey Methodology, Educational Measurement offered by University of Twente (course code 201500150), or from the track European Master in Official Statistics. Another option is to do a traineeship that is unrelated to the Master’s thesis project but relevant to the field of MS as such, so you get extra experience in conducting scientific research.

Research Seminar

This course runs along the Preparation for the Master's Thesis course (semester 1) and Master’s Thesis course (semester 2). The course mainly focuses on:

  • Structuring and writing skills (research reports, poster and slides)
  • Presentation skills
  • Peer review and feedback
  • Statistical consultation 

Master's Thesis

Writing a Master's thesis is a major objective in the second year of the Methodology and Statistics programme. You write a thesis in the form of a scientific publication that can be submitted to a scientific journal. The course aims to learn you the skills to;

  • study the theory and available literature available in the research field of the thesis (Knowledge and Understanding);
  • formulate a research problem that is well embedded in the state of the art in the research field of the thesis (Applying);
  • use appropriate methods to investigate, evaluate, and address the chosen research problem (Judgment);
  • report theory, literature, research problem, research method, research outcomes and illustrations in the form of a scientific publication that can be submitted to a scientific journal (Communication).

Criteria for the evaluation of the Master's thesis are: 

  1. it is embedded in previous research and literature on the topic of the thesis.
  2. it includes a theoretical elaboration of the research problem, 
  3. it includes an appropriate approach to solve and provide an answer to the research problem.