Data with Hierarchical Structure: Impact of Intraclass Correlation and Sample Size on Type-I Error

@inproceedings{Musca2011DataWH,
  title={Data with Hierarchical Structure: Impact of Intraclass Correlation and Sample Size on Type-I Error},
  author={Serban C. Musca and Rodolphe Kamiejski and Armelle Nugier and Alain M{\'e}ot and Abdelatif Er-rafiy and Markus Brauer},
  booktitle={Front. Psychology},
  year={2011}
}
Least squares analyses (e.g., ANOVAs, linear regressions) of hierarchical data leads to Type-I error rates that depart severely from the nominal Type-I error rate assumed. Thus, when least squares methods are used to analyze hierarchical data coming from designs in which some groups are assigned to the treatment condition, and others to the control condition (i.e., the widely used "groups nested under treatment" experimental design), the Type-I error rate is seriously inflated, leading too… CONTINUE READING