Скачать или смотреть Why Hierarchical Linear Modeling (HLM) for nested/clustered/multilevel data? An easy explanation

Why Hierarchical Linear Modelling?

This video explains why #HierarchicalLinearModeling (#HLM) should be used to analyze #NestedData? In nested/clustered/ #multilevel data, ordinary #regression provides erroneous and biased results. This video answers the question “Is there a (level 2) variable effect (e.g., teachers’ experience) on the (level 1) intercept of e.g., performance scores of students, which represents the mean score?” If there is a teachers’ experience effect, then ordinary regression methods will suffer from correlated error, and some form of linear mixed modeling is required.

In other words, nested data suffered from General Linear Regression model assumption of Autocorrelation.

To learn #CrossLevelModeling application, please read my article by clicking on following link or copy link in your browser:

https://www.cambridge.org/core/journa...

Article Citation:
Zhang, J., Ji, M., Anwar, C.M., Li, Q., & Fu, G. (2018). Cross level impact of team goal orientation and individual goal orientation on individual creativity. Journal of Management & Organization. 1-23. [Clarivate Analytics Impact Factor for 2017: 1.189].

More videos will be uploaded on Hierarchical Linear Modeling (HLM) soon...