IEA Workshops announced for fall 2020

IEA is pleased to announce its upcoming workshops on the topics of Multilevel Linear Modeling (MLM) with Large-Scale Assessment and Structural Equation Modeling (SEM): Foundations and Multilevel Advancement in the fall of 2020.

Due to the ongoing health concerns caused by COVID-19 pandemic, IEA has decided to shift its upcoming workshops to the virtual format. We hope that this will encourage more researchers to attend the courses.

Workshop module 1: Multilevel Linear Modeling (MLM) with Large-Scale Assessment Data

Date: October 26-30th 2020

Workshop module 2: Structural Equation Modeling (SEM): Foundations and Multilevel Advancement

Date: November 23-27th 2020

The workshop contains two modules with the first focusing on MLM (October 26-30th 2020) and the second focusing on SEM and ML-SEM (November 23-27th 2020). Parties may participate in either one or both of the workshop modules. A prerequisite to follow the content of the ML-SEM part of the second module is the solid knowledge of MLM. The workshops will be conducted virtually by the IEA experts.

What do these workshops offer?

At this workshops participants will be introduced to the theory and application of both techniques, considering in particular features specific to large-scale assessment data. The workshop on the SEM module builds upon the knowledge from the MLM module presenting how to combine these two techniques in one joint multilevel structural equation model (ML-SEM) approach. Through hands-on-trainings, participants will gain practical experience in applying MLM, SEM, and ML-SEM to large-scale assessment data using Mplus. The workshop modules will be delivered in the virtual format. The workshop language is English.

Registration

Registration in the workshops will be opened in August 2020. Detailed agenda will follow.

Discounted rates will be available for participating in both workshop modules as well as for early bird registrants and students.

We invite you to save the date & disseminate the announcement among colleagues.

 

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