Structural equation modelling in social sciences 1600-SZD-ID-MRS
The aim of the course is to familiarize the participants with a variety of structural equation models (SEM). More specifically, the following topics will be discussed during the course, starting with relatively simple ones and progressing towards more advanced ones:I. Path analysisII. Confirmatory Factor Analysis (First and Second Order)III. Confirmatory Factor Analysis with invariance analysis IV. Multigroup Confirmatory Factor Analysis V. Basic SEM models with observable variables VI. SEM models with latent variablesVII. Moderation and mediation in SEM. Additional “small topics” such as e.g., randomness of missing data (MCAR, MAR), means of data replacement, bootstrapping, and controlling for sociodemographic variables in SEM models might be added if possible within given time frames. This course is designed as an introduction to a more advanced course entitled “Methodology of longitudinal studies”.
Course coordinators
Learning outcomes
Knowledge | The graduate knows and understands:
WG_02 - the main development trends in the disciplines of the social sciences in which the education is provided
Skills | The graduate is able to:
UW_01 – make use of knowledge from various fields of science, in particular the social sciences in order to creatively identify, formulate and innovatively solve complex problems or perform tasks of a research nature, and in particular to: define the purpose and object of scientific research in the field of the social sciences, formulate a research hypothesis; develop research methods, techniques and tools and apply them creatively; make inferences based on scientific findings
UK_04 - participating in scientific discourse in the field of the social sciences
Social competences | The graduate is ready to
KK_01 - critically evaluating achievements within a given scientific discipline in the field of the social sciences
And others: As a result of attending the proposed course, PhD students will be able to :•understand, characterize and explain basic concepts underneath SEM •distinguish between various types of SEM models •being able to apply an adequate SEM model to the given research problem •analyze the data within the SEM framework using statistical software (SPSS, Amos, R) •evaluate and describe the results in the impacted alike publication form
Assessment criteria
Description of requirements related to participation in classes, including the
permitted number of explained absences: • requirements at the entrance: knowledge of English, basic idea of statistical software (SPSS, Amos and R) highly recommended •Permitted number of explained absences: 1 meeting out of 5
Principles for passing the classes and the subject (including resit session): active participation in the course & preparation of one larger project at the end of the course
Methods for the verification of learning outcomes: preparation of a one larger project which will require data analysis using the techniques taught during the course with adequate description of the results in the form of a results chapter (such the ones which are usually prepared for scientific data dissemination in journals with impact factor)
Evaluation criteria: adequacy of choice of structural equation model to the given problem, correctness of statistical analysis, correctness of results interpretation, clarity and quality of results description
Practical placement
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Bibliography
(1) Byrne, B. M. (2016). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming. Routledge: New York.
(2) Byrne, B. M. (2004). Testing for multigroup invariance using AMOS graphics: A road less traveled. Structural equation modeling, 11(2), 272-300
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Term 2024Z:
(1) Byrne, B. M. (2016). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming. Routledge: New York. |
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: