Lecturers: Prof. Dr. Jost Reinecke, Georg Kessler
Date: 6-10 August 2018
Time: 09:00-13:00, 14:00-16:00
Short Course Description:
The course focuses on measurement models and their application within the Structural Equation Modeling (SEM) framework. We will show how a theoretical model, represented through measurement models, can be applied to empirical data and how to assess its fit to the data. 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 and as such an excellent preparation for the course “Structural Equation Modeling with Panel Data” in the subsequent week (week 2, 14-18 August). 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, we will also address CFA with categorical data and how to handle missing data. Throughout the course we will work on examples provided by the lecturers using the popular SEM software package Mplus. For data preparation we will use mainly SPSS, but we will also be able to accommodate needs of Stata-users.
- 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];
- knowledge of what a linear equation system looks like and how it can be solved;
- 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]
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;
Organizational Structure of the Course:
The course is structured around four hours of classroom instruction (09:00-13:00) and two hours of assisted learning practice in a PC lab (14:00-16:00). During the two hours of assisted learning practice in the afternoon, we expect the participants to apply the covered material in their assignments and/or their own projects. Instructors will be present during these sessions. The class will be split so that individual coaching and class assignments will be tutored simultaneously. All the participants will have an opportunity to consult the instructors individually within the mentioned 2 hours or by appointment. The room for individual consultations will be announced in class.
Teaching will take place as a combination of lectures on the theory of CFA, getting acquainted with the program Mplus, and application of the theory in practice. To facilitate learning, participants are encouraged to bring and discuss their own projects (e.g., research questions) to get feedback from their peers and the instructors. The debate will enhance the transfer of theoretical learning to applied knowledge. We therefore expect all of the participants to take part in these 2 hours outside of the lecture to contribute to and benefit from the exchange and the extra practice.
Software and Hardware Requirements:
None. For the duration of this course, GESIS will provide participants with access to the required statistical software packages.