An NCME Instructional Module on Introduction to Structural Equation Modeling: Issues and Practical Considerations Pui-Wa Lei and Qiong Wu, The Pennsylvania State University Structural equation modeling (SEM) is a versatile statistical modeling tool. Its estimation techniques, modeling capacities, and breadth of applications are expanding rapidly. This module introduces some common terminologies. General steps of SEM are discussed along with important considerations in each step. Simple examples are provided to illustrate some of the ideas for beginners. In addition, several popular specialized SEM software programs are briefly discussed with regard to their features and availability. The intent of this module is to focus on foundational issues to inform readers of the potentials as well as the limitations of SEM. Interested readers are encouraged to consult additional references for advanced model types and more application examples. Keywords: structural equation modeling, path model, measurement model S tructural equation modeling (SEM) has gained popular- ity across many disciplines in the past two decades due perhaps to its generality and flexibility. As a statistical mod- eling tool, its development and expansion are rapid and Pui-Wa Lei is an assistant professor, Department of Educational and School Psychology and Special Education, The Pennsylvania State University, 230 CEDAR Building, University Park, PA 16802; puiwa@psu.edu. Her primary research interests include structural equation modeling and item response theory. Qiong Wu is a doctoral student in the Department of Educational and School Psychology and Special Education, The Pennsylvania State University, 230 CEDAR Building, University Park, PA 16802; qiong@psu.edu. Her interests are measurement, statistical modeling, and high-stakes testing. Series Information ITEMS is a series of units designed to facilitate instruction in ed- ucational measurement. These units are published by the National Council on Measurement in Education. This module may be photo- copied without permission if reproduced in its entirety and used for instructional purposes. Information regarding the development of new ITEMS modules should be addressed to: Dr. Mark Gierl, Canada Re- search Chair in Educational Measurement and Director, Centre for Research in Applied Measurement and Evaluation, Department of Ed- ucational Psychology, 6-110 Education North, University of Alberta, Edmonton, Alberta, Canada T6G 2G5. ongoing. With advances in estimation techniques, basic mod- els, such as measurement models, path models, and their integration into a general covariance structure SEM anal- ysis framework have been expanded to include, but are by no means limited to, the modeling of mean structures, in- teraction or nonlinear relations, and multilevel problems. The purpose of this module is to introduce the foundations of SEM modeling with the basic covariance structure mod- els to new SEM researchers. Readers are assumed to have basic statistical knowledge in multiple regression and anal- ysis of variance (ANOVA). References and other resources on current developments of more sophisticated models are provided for interested readers. What is Structural Equation Modeling? Structural equation modeling is a general term that has been used to describe a large number of statistical models used to evaluate the validity of substantive theories with empirical data. Statistically, it represents an extension of general linear modeling (GLM) procedures, such as the ANOVA and multiple regression analysis. One of the pri- mary advantages of SEM (vs. other applications of GLM) is that it can be used to study the relationships among la- tent constructs that are indicated by multiple measures. It is also applicable to both experimental and non-experimental data, as well as cross-sectional and longitudinal data. SEM takes a confirmatory (hypothesis testing) approach to the Fall 2007 33