SOW-BS081
Statistics: Multivariate Analysis
Course infoSchedule
Course moduleSOW-BS081
Credits (ECTS)4
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Behavioural Science;
Lecturer(s)
Coordinator
dr. W.J. Burk
Other course modules lecturer
Examiner
dr. W.J. Burk
Other course modules lecturer
Contactperson for the course
dr. W.J. Burk
Other course modules lecturer
Lecturer
prof. dr. A.H.N. Cillessen
Other course modules lecturer
Academic year2017
Period
PER1  (04/09/2017 to 12/11/2017)
Starting block
PER1
Course mode
full-time
RemarksFor external (PhD) students, see www.ru.nl/BS/enrolment
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
Upon successful completion of the course, students will be able to describe and identify appropriate multivariate statistical techniques that address a variety of research questions. Students will also be able to evaluate and judge the appropriateness of the use of various analytic techniques presented in the scientific literature. Most importantly, students who successfully complete the course will be able to describe the theoretical underpinnings of general and generalized linear models, report and interpret results of these analyses, and be able to choose and utilize appropriate statistical techniques in their subsequent research.  
Content
This course provides an overview to multivariate analytic methods, and represents the foundation of the statistical knowledge and skills required for subsequent statistical courses. Multivariate statistical techniques are the basic tools for experimental and non-experimental research in the psychological and educational sciences. These techniques examine linear (and curvilinear) relationships between at least two independent variables (predictors) and one or more dependent variables (outcomes). This course begins with a brief review of the basic principles of hypothesis testing, research design, and bivariate analysis. Numerous multivariate statistical techniques are then elaborated in terms of the types of research questions they address. These techniques include, but are not limited to: linear and logistic regression, hierarchical loglinear analysis, repeated measures analyses of variance and covariance, and discriminant function analysis. This course provides students with the fundamental tools needed for their statistical toolbox.
Teaching methods: Lectures and computer-laboratory.
Test information
Student evaluations are based on results from five homework assignments (10%) and the final exams (90%). Assignments require students to address research questions by performing appropriate statistical analyses, and describing/interpreting the results. Assignments will be graded as pass/fail. The final exam will consist of two parts: (1) the take-home portion requires students to perform the appropriate analyses, and write up the results of a research question in APA style; and (2) the theory-based portion, which will be completed at a designated time and place, includes multiple choice and open-ended questions concerning theoretical issues associated with multivariate methods and issues specifically addressed in the lectures.

Required materials
Book
Tabachnick, B.G., & Fidell, L.S. (2012, 6th ed.). Using multivariate statistics. Boston: Pearson.

Instructional modes
Computer Practicals

Lectures

Tests
Examination
Test weight1
OpportunitiesBlock PER1, Block PER2