Project Activities
This project expanded the work completed in a previous IES grant (Development of Accessible Methodologies and Software in Hierarchical Models with Missing Data) on software to handle missing data in univariate two- and three-level multilevel. This project expanded the existing univariate software to handle binary and ordinal missing data. In addition, researchers developed the multivariate software to handle binary and ordinal missing data.
The hierarchical models addressed by this project can have outcome variables defined at single or multiple levels that are discrete or continuous, or both. They may also involve auxiliary variables that are not of direct interest but are highly correlated with outcomes and covariates that could be missing data. Covariates and outcomes as well as auxiliary variables may be subject to missing data with a general missing pattern at any level of the model.
A key feature of the final software product is that it requires users only to know and to input the model they intend to analyze, with the rest of the analysis steps automated by the software.
People and institutions involved
IES program contact(s)
Project contributors
Products and publications
In addition to the software, the grant produced peer-reviewed journal articles, conference presentations, and workshops for training researchers on the use of the new software.
Book chapter
Shin, Y. (2013). Efficient Handling of Predictors and Outcomes Having Missing Values. In L., Rutkowski, M., von Davier, D. Rutkowski,(Eds.), A Handbook of International Large-Scale Assessment Data Analysis: Background, Technical Issues, and Methods of Data Analysis (pp. 451-479).
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Questions about this project?
To answer additional questions about this project or provide feedback, please contact the program officer.
