Prospectus

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Preparatory Statistics

Course 2016-2017

Entry requirements

It is assumed that the student has a basic knowledge about basic statistical concepts (like mean, variance, correlation) and research methods (the difference between correlational/longitudinal studies and an experiment, the difference between a between-participants and a within-participants study). A basic understanding of SPSS is helpful but not required.

Description

The Preparatory Statistics Course teaches students statistical knowledge and practical skills at a conceptual level, useful for the quantitative research process used in most master theses. The topics cover part of the bachelor statistics program with specific attention for formulating research questions, data handling in SPSS, interpreting and reporting SPSS results (APA style), and drawing conclusions based on these results.

Course objectives

Upon completion of this course, students will:

  • have acquired statistical knowledge and practical skills for the independent completion of data analysis and reporting in the context of a psychology Master’s thesis; * have a basic understanding of the statistical techniques that are often used in empirical psychological research (t-test, ANOVA, linear and logistic regression); and
  • be able to apply these basic statistical techniques to empirical data by means of the IBM SPSS software.

Timetable

For the timetables of your lectures, workgroups, and exams, select your study programme.
Psychology timetables

The examination takes place on 7 June between 17 - 20 hrs in PDLC 1A46.

Registration

Course

Students need to enroll for lectures (and work group sessions). Please consult the Instructions registration.

Examination

Students are not automatically enrolled for an examination. They can register via uSis from 100 to 10 calendar days before the date; students who are not registered will not be permitted to take the examination.

Registering for exams

Mode of instruction

The course consists of 6 lectures (2 hours each) and 8 supervised work group sessions (computer practical, 2 hours each). Attendance of lectures and work group sessions is obligatory.

Assessment method

The final grade is based on – 1 SPSS exam (in week 3) which assesses the students’ practical skills in using IBM SPSS for data handling. – 6 take-home assignments (1 assignment per lecture topic, 1 assignment each 2 weeks) in which the statistical techniques discussed during the lectures are applied in the form of a short research report. – Final exam (at the end of the course, 3h). The final exam takes the same form as the practical assignments and covers all of the topics addressed during the lectures and the work group sessions

Students receive extensive feedback on both the assignments and the SPSS exam.

The final grade is determined by combining (each with a weight of 50%) (1) the average grade on the assignments/SPSS exam (the average grade across the 6 assignments and the SPSS exam) with (2) the grade of the final exam.

The students are required to attend all lectures and all work group sessions. For non-attendance, points are deduced from the grade of the SPSS exam and/or the assignments, except when the non-attendance is announced beforehand and justified (e.g., illness). The instructors decide whether a non-attendance is justified or not.

To pass the course students should have (1) an average grade on assignments/SPSS exam equal to or greater than 5.5 (the average grade across the 6 assignments and the SPSS exam) AND (2) a final grade equal to or greater than 5.5

The Faculty of Social and Behavioural Sciences has instituted that instructors use a software programme for the systematic detection of plagiarism in students’ written work. In case of fraud disciplinary actions will be taken. Please see the information concerning fraud.

Reading list

Andy Field (2012). Discovering Statistics using SPSS. Fourth Edition. Sage: London.
IBM SPSS 19/20. . A student version of IBM SPSS can be purchased through www.surfspot.nl.

Contact information

Dr. Tom F. Wilderjans
t.f.wilderjans@fsw.leidenuniv.nl