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Experimental and Correlational Research


Students of the Dutch bachelor’s programme, see Experimenteel en Correlationeel Onderzoek

Entry requirements

This course follows on from the courses Introduction to Methodology and Statistics course and Inferential Statistics, and presupposes the knowledge and skills taught there.


The course discusses basic methods used to analyse data from correlational and (quasi-)experimental research: correlation, simple and multiple regression analysis, one-way analysis of variance (ANOVA), planned comparisons, post hoc tests, and factorial ANOVA.

Course objectives

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

  • understand the terms, principles, and procedures of correlation, regression analysis and analysis of variance

  • calculate and test correlations, and to interpret and report the results

  • carry out simple and multiple regression analysis and to interpret and report the results

  • carry out one-way and two-way analysis of variance and to interpret and report the results

  • use statistical software for correlation, regression analysis, and analysis of variance


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. The exception here is that first-year bachelor students are assigned and registered for all components in the first semester or academic year by the administration of their bachelor programme. The programme will communicate to these students for which course components and for which period the registration applies.


You must register for each exam in My Studymap at least 10 days before the exam date. Don’t forget! For more information, see the enrolment procedure.
You cannot take an exam without a valid registration in My Studymap.

Carefully read all information about the procedures and deadlines for registering for courses and exams.

Students who take this course as part of a LDE minor or a premaster programme, exchange students and external guest students will be informed by the education administration about the current registration procedure.

Mode of instruction

8 2-hour lectures and 8 2-hour mandatory tutorial sessions.

The lectures

Each course week begins with a lecture to introduce and explain course material. The lectures also cover additional and new topics that are included in the examination. As preparation for the lectures students are required to study the chapters assigned for that week. The lectures can be attended in a lecture hall and via a live stream. The lectures will be made available as Weblectures.

The tutorials

The day after the lecture there is a tutorial session. In preparation for these sessions students have to complete a number of mandatory homework assignments, that will be checked by the teacher at the start. These sessions focus on applying the acquired knowledge in practice. Students also learn to work with R, a software package for statistical data analysis. The material covered in these work group sessions is also included in the examination. Students are required to bring the exercise book to every session. Attendance is mandatory.

Please note, second year students who have to resit this course will still work with the statistical software IBM SPSS Statistics (instead of R). For students retaking the SPSS exam, attendance is mandatory for the 4 SPSS practicals. These take place in weeks 4,6,7 and 8 of the course.

Assessment method

The assessment consists of two components:
1. A written examination consisting of multiple-choice questions, covering both theory and statistical calculations from the literature, the work group sessions, and the lectures (
2. An R skills test covering the various aspects of students’ skills in working with R as well as in interpreting and reporting the output.
The final grade is a weighted average of the examination grade (70%) and the grade for the R skills test (30%).

Please note, second year students who have to resit this course will take an SPSS skills test (instead of an R skills test).

The Institute of Psychology uses fixed rules for grade calculation and compulsory attendance. It also follows the policy of the Faculty of Social and Behavioural Sciences to systematically check student papers for plagiarism with the help of software. All students are required to take and pass the Scientific Integrity Test with a score of 100% in order to learn about the practice of integrity in scientific writing. Students are given access to the quiz via a module on Brightspace. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of these three policies.

Reading list

  • Howell, D.C. (2014/2021). Statistical Methods for Psychology (Leiden edition). Wadsworth: Cengage Learning. ISBN 978-1-4737-8791-9. E-book, can be ordered online:

  • R (open source software, download for free at

  • R Studio (open source software, download for free at

  • Exercise book Experimental and Correlational Research 2023-2024.

  • Lecture slides (provided on Brightspace).

  • Please note: Pre-Master students might need additional literature and/or software for using SPSS and will be informed about this by the study adviser or the course coordinator.

Contact information

Drs. Hemmo Smit