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Course 2: Introduction to Structural Equation Modeling: Confirmatory Factor Analysis with Mplus
Prof. Dr. Jost Reinecke, Georg Kessler, Maximilian Wächter
Datum: 06.08 - 10.08.2018 ics-Datei
The course focuses on measurement models and their application within the Structural Equation Modeling (SEM) framework. We will show how a theoretical model, represented by measurement models, can be applied to empirical data and how to assess its fit to the data through the measurements' covariance matrix. Confirmatory Factor Analysis (CFA) is an important and basic aspect of the SEM-framework and its understanding and application to data is the core learning aspect of this course. Also, CFA is a necessary conceptual precondition to understand and apply the structural aspect of SEM, path modeling. Therefore, the course deals with concepts and applications of CFA such as assessing construct validity and reliability of a measurement model as well as the interpretation of calculated results. The topics addressed in the course include different modeling techniques of CFA such as single measurement models, simultaneous CFA (SCFA), the Multiple Group Comparison of the CFA (MGCFA), and the higher-order CFA. If time permits on the last day, we can peak into topics as CFA with categorical data, path-modeling, how to handle missing data, or longitudinal analysis. Throughout the course we will work on examples provided by the lecturers using the popular SEM software package Mplus. For data preparation we accommodate needs of SPSS- or Stata-users.
For a full length syllabus of this course, please click here.
Participants will find the course useful if they:
(On the level of their research questions)
- work with models that involve a complex structure of variables involving latent concepts and their relationships to each other;
- have a strong deductive framework and want to verify theoretical assumptions derived from substantive theories;
- need information on measurement quality (validity and reliability testing)
- want to apply SEM to their future analysis.
(On a more basic level)
- want to get an introduction into Structural Equation Model (SEM)-framework;
- have had prior experience with SEM, but no formal training;
- they have had prior training, but still find the whole matter rather complicated;
- they want to further their understanding of Mplus
While this course is introductory in nature, its theoretical input should be dense enough to help more advanced users to effectively brush up their knowledge.
Course and Learning Objectives:
By the end of the course participants will:
- know how to define a latent construct through a measurement model;
- comprehend the mathematical and statistical foundation of SEM;
- be able to read, understand, and interpret an Mplus output;
- transfer the theoretical knowledge to applied research projects;
- in general be enabled to acquire the set of skills they need for their individual projects.
- We strongly encourage participants to familiarize themselves with and have a conceptual/mathematical understanding of variance, covariance, correlation, standardization, hypothesis testing (t-test, chi-square), and regression analysis [for compact refreshing we recommend http://davidmlane.com/hyperstat/;
- basic knowledge of matrix notation [a short refresher can be found on https://www.youtube.com/watch?v=G16c2ZODcg8];
- Handling of system files (.sps; .dta; …) and transformation to portable or ASCII-data files (.dat; .csv; .txt; …) [for SPSS users: a good preparation is to import .txt-files into SPSS and use SPSS-syntax to get data; for Stata users: a good preparation is to use the stata2mplus ado in Stata to get Mplus input and data file simultaneously].
- As introductory reading we also recommend studying the chapters 1 to 3 of the Brown book (cited in the course literature).
- Basic familiarity with Mplus (can be acquired in the short course "Introduction to Data Analysis Using Mplus" in week 0) and familiarity with writing syntax (Mplus input - as taught in the class - is syntax only) [we recommend looking into chapter 5 of http://www.statmodel.com/ugexcerpts.shtml]