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Structural Equation Modelling (RSS1.11) - Confirmed

To learn structural equation modeling (SEM), you need to apply it under the guidance of an expert. In this week-long summer course, you will learn SEM by doing it. After this course, you will be able to perform SEM analyses confidently.

    General

     

    The application deadline has passed, applying is no longer possible

    Complicated issues such as estimating and testing are explained intuitively with clear instructions. Interpreting results in SEM receives extensive attention.

    The course begins with an intuitive approach to structural equation modeling (SEM) where you learn what SEM entails. You will learn about estimation procedures & assumptions, model evaluation & testing, in an intuitive way, with as little statistics as possible.

    Then you'll learn about measurement and factor analysis. Measurement is the basis of any empirical research, but how do you know if your measurement scales are good (enough)? You will learn how to evaluate (and develop) measurement scales using factor analysis. The main advantage of SEM is that relationships between latent variables can be analyzed, reducing the influence of a variety of measurement problems. Models that include latent variables are preferable, but not always feasible. You will learn how to simplify an "ideal" model and understand how simplification affects the conclusions you can draw. Next, you will learn about causal modeling. The topic of causality is often avoided in courses on SEM, we don't. You will learn to develop causal models and test them in a (non-)experimental context. Once you see how causal modeling works, you will understand the importance of study design. In this context, you'll learn how to test for mediation and moderation (including using multigroup analysis).

    Finally, we briefly discuss some advanced techniques, such as measurement invariance, cross-lagged panel models, post-hoc power analysis, and dealing with non-normal data (e.g., ordinal data or dichotomous data).

    In this course we will use R-Studio and the package lavane (both free).

    Collaboration

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    Cannot join us this year? 

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    Starting date

    17 June 2024, 9 am
    Educational method
    On-site
    Main Language
    English
    Sessions
    17 June 2024, 9 am - 21 June 2024, 5 pm
    Teacher(s)
    William van der Veld
    Unique code
    RSS1.11

    Factsheet

    Type of education
    Course
    Entry requirements
    See the requirements in cost and admission
    Study load (ECTS)
    2
    Result
    Certificate
    Organisation
    Radboud Summer School

    Total Package & Social Events

    William van der Veld

    William van der Veld
     

    William M. van der Veld is a research methodologist. He works at Radboud University in the Behavioral Science Institute (BSI) and in the Department of Psychology. He received his PhD (cum laude) from the University of Amsterdam in 2006 for his dissertation on opinion/attitude instability and measurement quality. He has published more than 40 articles and book chapters on the development and validation of psychological scales, on the effectiveness of psycho-therapeutic interventions and educational interventions, and on structural equation model-ing. Since the early 2000s, he has taught methodology and statistics at universities at home and abroad and in industry.

    The application deadline has passed, applying is no longer possible

     

    Costs

    • Regular: €1049 (application deadline 13th of May)
    • Student & PhD's: €699 (application deadline 13th of May)

    Includes: your course, short morning and late afternoon courses, coffee and tea during breaks, a warm lunch every day, Official Opening, MethodsNET Café (including some drinks and snacks) Official Closing (with some drinks and snacks) and a 1-year (2024 calendar year) free membership as MethodsNET regular member.

    Excludes: transport, accommodation, social events and other costs. 

    Discounts and Scholarships

    Admission


    Level of the participant: 

    • Master
    • PhD
    • Postdoc
    • Professional


    Admission requirements 

    This course requires basic knowledge of regression analysis.

    Admission documents: 

    None