Statistics: Structural Equation Modeling
Course infoSchedule
Course moduleSOW-BS084
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. ing. W.M. van der Veld
Other course modules lecturer
Academic year2018
PER4  (15/04/2019 to 12/07/2019)
Starting block
Course mode
RemarksFor Behavioural Science RM students only, non-BSRM students interested in the course, please mail to
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
Upon successful completion of the course, students will be able to apply a variety of statistical techniques utilizing structural equation models (SEMs) to address specific research questions. Students will be able to recognize, evaluate and interpret various types of parameter estimates, and be able to judge the appropriateness of the use of SEMs presented in the scientific literature. Most importantly, students who successfully complete the course will be able to describe the theoretical underpinnings of structural equation models, report and interpret results of SEMs, and utilize these methods in their subsequent research.  
Structural equation modeling (SEM) consists of numerous analytic techniques that examine relationships between one or more exogenous variables (predictors) and one or more endogenous variables (outcomes). Exogenous and endogenous variables may be continuous or categorical. This methodology may be applied to concurrent and longitudinal data from experimental and non-experimental studies, and has several advantages over other methods, including the ability to account for missing values and measurement error (through the use of latent constructs). Structural equations describe how observed variables and/or latent constructs are related to one another. Relations between predictors and outcomes describe the structural portion of the model, whereas relationships between latent constructs and how well latent constructs are represented by observed variables represent the measurement portion of the model. That is, regression and confirmatory factor analyses may be integrated into a structural equation model. Due to its flexibility, SEM is commonly used by behavioral and social scientists. Theoretical issues involving SEM such as model identification and measurement invariance are discussed; as are commonly used techniques such as path models, cross-lagged panel models, and latent growth curve models.

Teaching methods: Lectures, computer lab sessions, and homework assignments.

Exam information
Course grade is based on the results of a take-home exam (50%) and a final exam (50%). The take-home exam requires students to perform the appropriate set of analyses, and write up the results in APA style. The final exam includes multiple choice and open-ended questions concerning theoretical and practical issues associated with structural equation modeling.
Assumed previous knowledge
Knowledge of and experience with the program R is required. It is highly recommended to follow the course: ‘Advanced Statistics in R (SOWBS86)' or a similar course.

Required materials
Instructors will provide selected journal articles and book chapters

Instructional modes
Computer Practicals


Home work assignments
Test weight0
OpportunitiesBlock HERT, Block PER4

Take home exam
Test weight5
Test typeExam
OpportunitiesBlock HERT, Block PER4

Written exam
Test weight5
Test typeExam
OpportunitiesBlock HERT, Block PER4