Year 1, semester 1

MSBBSS01 Survey data analysis

TESTING AND COURSE AIMS Three tests will be given in this course:- Two 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.   
There are options to follow this course as a non-MSBBSS student (eg Elective student, PhD student etc). Please contact the teacher about these options on time, ie before July 15 (for courses in semester 1). You will need written approval from the teacher (an email is sufficient) in order to register for this course at the Faculty’s Student Desk. Note that for external parties, costs for participation may be involved.

MSBBSS02 Multivariate statistics

In this course we will refresh and elaborate on the analysis of variance model (including factorial designs, interaction effects, post-hoc testing, inclusion of covariates, multivariate models and repeated measures designs) and the regression model (including dummy variables, and logistic regression). It is expected that students have encountered these models in the bachelors 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: missing data and imputation methods, multiple testing, capitalization on chance and related power issues. Also, several statistical papers will be read and discussed, in which recent developments or limitations in the area of statistics are presented.  There are options to follow this course as a non-MSBBSS student (eg Elective student, PhD student etc). Please contact the teacher about these options on time, ie before July 15 (for courses in semester 1) / December 15 (for courses in semester 2). You will need written approval from the teacher (an email is sufficient) in order to register for this course at the Faculty’s Student Desk Note that for external parties, costs for participation may be involved.  

MSBBSS03 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. Half 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.  There are options to follow this course as a non-MSBBSS student (eg Elective student, PhD student etc). Please contact the teacher about these options on time, ie before July 15 (for courses in semester 1) / December 15 (for courses in semester 2). You will need written approval from the teacher (an email is sufficient) in order to register for this course at the Faculty’s Student Desk. Note that for external parties, costs for participation may be involved.

MSBBSS04 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.  There are options to follow this course as a non-MSBBSS student (eg Elective student, PhD student etc). Please contact the teacher about these options on time, ie before July 15 (for courses in semester 1) / December 15 (for courses in semester 2). You will need written approval from the teacher (an email is sufficient) in order to register for this course at the Faculty’s Student Desk . Note that for external parties, costs for participation may be involved.  Entry requirements Students will have completed some intermediate level statistical courses and will preferably also have followed a course dealing with multivariate analysis.

Year 1, semester 2

MSBBSS05 Psychometrics

TESTING  • Written examination of psychemetric theory, including classical test theory, generalizability thyory, item response theory and latent regression• Practical using SPSS to conduct G- and D- study and interpret and report results.• Practical using IRT-software to conduct a linking and dif study and interpret and report results.• Practical using OpenBugs to conduct IRT-latent regression analysis interpret and report results.• Practical using OpenBugs to conduct multilevel IRT-latent regression analysis interpret and report results.
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 marginal 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 generalizability theory and IRT, multidimensional IRT and multilevel IRT. 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 methods. In the lab meetings OpenBugs, OpenBUGS, and R will be used to analyze item response data using Bayesian inference.  There are options to follow this course as a non-MSBBSS student (eg Elective student, PhD student etc). Please contact the teacher about these options on time, ie before July 15 (for courses in semester 1) / December 15 (for courses in semester 2). You will need written approval from the teacher (an email is sufficient) in order to register for this course at the Faculty’s Student Desk . Note that for external parties, costs for participation may be involved. 

MSBBSS06 Introduction in multilevel and structural equation modelling

 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.  There are options to follow this course as a non-MSBBSS student (eg Elective student, PhD student etc). Please contact the teacher about these options on time, ie before July 15 (for courses in semester 1) / December 15 (for courses in semester 2). You will need written approval from the teacher (an email is sufficient) in order to register for this course at the Faculty’s Student Desk. Note that for external parties, costs for participation may be involved. 

MSBBSS07 Bayesian statistics

TESTING AND COURSE AIMS This course consists of two tests:• Handing in annotated computer code and an individual presentation based on analyses with OpenBUGS (Assignment 1, 25%) and  Handing in annotated computer code based on analyses with R and a poster  (Assignment 2,75%). Both these assignments test the same teaching goals: a) Knowing and understanding the core features of Bayesian statistic: prior distribution, density of the data, posterior distribution, Gibbs sampler, Metropolis sampler, Bayesian p-value, and Bayesian model selection using the DIC and the Bayes factor. Knowing and understanding the main differences between classical and Bayesian statistical inference.   (Knowledge and Understanding)b) Using OpenBugs (Assignment 1)/R (Assignment 2) to program Bayesian procedures. Using the resulting computer codes for data analysis. (Applying)c) Being able to judge when, which, and how Bayes procedures can be used to make inferences from data. Being able to formulate the relative advantages and disadvantages of classical and Bayesian inference.  (Judgment)d) Presenting annotated computer code such that a peer can understand what the code does. Working individually to construct a presentation about a data analysis executed using Bayesian procedures (Assignment 1). Working in a group to construct a presentation about a data analysis executed using Bayesian procedures (Assignment 1). Working individually to construct  a poster presentation about a data analysis executed using Bayesian procedures (Assignment 2). (Communication)e) The ability to master new Bayesian procedures, implement them in computer code, and use them for the analysis of data. (Learning skills) 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 OpenBugs and R will be used to analyze empirical data using the Bayesian approach.  
There are options to follow this course as a non-MSBBSS student (eg Elective student, PhD student etc). Please contact the teacher about these options on time, ie before July 15 (for courses in semester 1) / December 15 (for courses in semester 2). You will need written approval from the teacher (an email is sufficient) in order to register for this course at the Faculty’s Student Desk. Note that for external parties, costs for participation may be involved.

MSBBSS08 Introduction to Biomedical Statistics

  

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.