Statistics: Analyzing in R
Course infoSchedule
Course moduleSOW-BS082
Credits (ECTS)4
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Behavioural Science;
dr. W.J. Burk
Other course modules lecturer
dr. W.J. Burk
Other course modules lecturer
Contactperson for the course
dr. W.J. Burk
Other course modules lecturer
dr. B.C. Figner
Other course modules lecturer
Academic year2017
PER2  (13/11/2017 to 04/02/2018)
Starting block
Course mode
RemarksFor external (PhD) students, see
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
Upon successful completion of the course, students will be conversant with the R programming language, and familiar with several statistical packages. Specifically, students will be able to manage different types of variables and data structures, perform the analytic techniques described in the course Multivariate Analysis, graphically display descriptive and inferential results, and interpret various output. Students who successfully complete the course will be able to write R scripts to appropriately test specific research questions and model assumptions; as well as interpret and present these results.
This course introduces R as a statistical platform for the analysis and graphical representation of multivariate statistical techniques. The flexibility of this program offers students a general statistical “solution” to analyzing simple and complex data structures, in that, the packages available in this program allow for a wealth of analytic functionality. The course begins with an overview of the R language. Data management and issues associated with pre-processing of data are initially described. Specific attention is paid to the visualization of data, both in terms of descriptive and inferential results. Lectures then expand upon the concepts and analytic techniques described in Multivariate Statistics (Stats I) and require students to translate this knowledge into the R “statistical” language. This course is meant to introduce students to a statistical program that may be used to perform a wide range of statistical analyses. R will be featured in the Mixed-effects Modeling and Structural Equation Modeling statistical courses, and is compatible with the Python program, which is described in the programming skills course: Python. R is free, open-source, platform independent, and continuously maintained, ensuring that students will be able to use their newly mastered skills in any future context.
Teaching methods: Lectures and computer-laboratory.
Test information
Student evaluations are based on results from five 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, which requires students to perform the appropriate analyses, and write up the results 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 functionality, terminology used in R.

Required materials
Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics using R. London: Sage.
To be announced
Instructors will provide additional reading materials.

Instructional modes
Computer Practicals

Attendance MandatoryYes

Test weight1
OpportunitiesBlock PER2, Block PER3