Project summary is created to provide information on an IES funded project (R305D210023) to develop methods and software for network data and text data analysis. This project is also supported by Notre Dame International at the University of Notre Dame.

New types of data, such as network data and text data, are increasingly collected in many fields of research, business, and government. For example, to study student behaviors, it is important to understand the context of behaviors because students are not independent entities but are typically connected with one another, which naturally leads to the collection and analysis of network data. For teaching evaluation, narrative comments on different aspects of teaching can provide teachers rich information and valuable feedback over and beyond numerical ratings.

This project proposes to combine structural equation modeling (SEM) techniques and data science methods to model network data and text data. The project tackles the complex problems of network data and text data analysis by treating both network data and text data as new types of variables in SEM. By doing so, it not only helps researchers quickly adopt new techniques for network and text data analysis through SEM methodology that researchers are already familiar with, but also allows researchers to address complex, realistic, and interesting research questions in education research. The project also develops easy-to-use software BigSEM to implement the proposed methods for analyzing network data and text data. BigSEM is developed as both (1) an R package to allow future growth in capability and (2) a web application so that one can conduct complex data analysis online. The performance of the methods and software is evaluated through simulation studies, and their applications are illustrated by real data analyses.

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