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Applied Data Analysis


Entry requirements

Open to all master students in Psychology, except for students of the Methodology and Statistics specialisation and MSc Psychology (Research) students, because of overlap with other courses. Knowledge of (psychology) bachelor level statistics (MVDA or equivalent) is required.


Analysis of real data requires additional skills beyond those taught in most statistics courses. Real data are usually a lot messier than the examples that you have encountered during your study. Data are not in the right format, data might be missing, or need to be combined from different sources. How do you tackle this? What to do when your assumptions are not met, or your data require more tailored statistical models than the usual suspects (e.g., when having multiple time measures, or in reaction time studies)? And how do you properly convey your results to your audience?

The Applied Data analysis course takes a β€˜hands-on’ approach in tackling these issues, and can be a perfect preparation for your thesis, especially when your project requires more than standard statistical analyses.

The following topics will be covered:

  • Data entry, cleaning, and checking.

  • Descriptive statistics and visualisation.

  • Flexible and robust statistical modelling using the General Linear Model (GLM): Modelling mean differences; Modelling associations between variables; Correcting for external variables/confounds; Modelling interactions (moderation); Modelling dependence (within-subjects designs); Resampling approaches (cross-validation, permutations).

  • Checking model applicability and validity/robustness of results.

  • Interpreting analysis results and correctly (and clearly) reporting results.

The basic statistical methods covered in this course you have all encountered during your bachelor or your pre-master track. We will not go into the mathematical details of the statistical methods (almost no formulas) but provide a more applied approach using real data examples. And we will expand on these methods, showing you how you can adapt them to your specific research questions.

Course objectives

The general objective of the course is to prepare students to independently perform all steps needed to write the results section of their master thesis. This includes preparing and checking data, performing appropriate statistical analyses, and clearly reporting and visualising results.

At the end of the course the student is able to:

  • Read in, clean and check messy data.

  • Describe and visualise important aspects of a dataset.

  • Highlight assumptions, limitations, and proper applications of the GLM.

  • Select an appropriate statistical model for the question at hand.

  • Perform the appropriate statistical analysis using the GLM.

  • Interpret results of GLM analyses.

  • Report results of GLM analyses, visually and in words.


The course is offered in the first semester and runs the whole semester (block 1 and 2).
For the timetable of this course please refer to MyTimetable



Students must register themselves for all course components (lectures, tutorials and practicals) they wish to follow. You can register up to 5 days prior to the start of the course.


It is mandatory for all students to register for each exam and to confirm registration for each exam in My Studymap. This is possible up to and including 10 calendar days prior to the examination. You cannot take an exam without a valid pre-registration and confirmation in My Studymap. Carefully read all information about the procedures and deadlines for registering for courses and exams.

Exchange students and external guest students will be informed by the education administration about the current registration procedure.

Mode of instruction

7 interactive lectures (a mix of theory and practical examples using real research problems).
5 interactive workgroups with the emphasis on analysing data and reporting results.

Assessment method

The final grade is based on:

A written exam, consisting of essay questions (60%); 5 homework assignments (35%), and 5 workgroup exercises (5%).

The Institute of Psychology follows the policy of the Faculty of Social and Behavioural Sciences to systematically check student papers for plagiarism with the help of software. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of this fraud policy.

Reading list

  • Miscellaneous book chapters and articles, available online. Definitive list will be made available at the start of the course.

  • Lecture slides.

  • Optional: Field, A. (2018). Discovering statistics using IBM SPSS statistics. Fifth Edition. London: Sage. ISBN (paperback): 9781526419521. You can still also use the fourth edition of the book.

Contact information

Dr. Wouter Weeda