Skip Navigation
Funding Opportunities | Search Funded Research Grants and Contracts

IES Grant

Title: A General Framework for Statistical Power Analysis with Non-normal and Missing Data through Monte Carlo Simulation
Center: NCER Year: 2014
Principal Investigator: Zhang, Zhiyong Awardee: University of Notre Dame
Program: Statistical and Research Methodology in Education      [Program Details]
Award Period: 3 years (7/1/146/30/17) Award Amount: $573,097
Goal: Methodological Innovation Award Number: R305D140037
Description:

Co-Principal Investigator: Ke-Hai Yuan (Notre Dame)

The increasing complexity of education research poses great challenges on existing techniques used to estimate statistical power. For example, education research often involves longitudinal and multilevel designs as well as advanced techniques such as structural equation and multilevel models. Furthermore, practical data in education are often not normally distributed and are incomplete. Without careful consideration of the complexity of study designs and the impact of non-normal data and missing data on power estimation, the validity of education research can be weakened.

In this project, researchers will develop a general method to enable the specification of models including structural equation models and multilevel models as well missing data mechanisms and non-normality of data through drawing path diagrams. The researchers will then develop methods for simulating data based on those models and methods for calculating statistical power based on repeated simulations, with the resulting methods being robust to non-normal data and missing data. Researchers will also develop software called MCpower to conduct power analysis via the proposed framework. MCpower will run as a web application and can be used locally on a personal computer or remotely on a Web server within a web browser. The project is expected to offer the community of education researchers an easy-to-use and general-purpose tool to conduct sophisticated statistical power analysis for structural equation and multilevel models with non-normal and missing data.


Back