Structural Equation Modeling (SEM) is a popular and flexible statistical modeling technique that allows causal relationships among psychological variables to be estimated. Whether you’re a social-scientist working in higher-education or
Structural Equation Modeling (SEM) is a popular and flexible statistical modeling technique that allows causal relationships among psychological variables to be estimated. Whether you’re a social-scientist working in higher-education or industry, a data analyst, a practicing psychologist, or an educator with an interest in quantitative methods, this workshop will provide you with the knowledge and skills to begin implementing this general yet extremely useful technique.
This one-day course assumes no prior experience with SEM, and is intended as both a theoretical and practical introduction. An understanding of SEM will be developed by relating it to participants’ previous knowledge of multiple linear regression, and then by expanding it to allow for correlated and causally related latent constructs. We will start with path analysis among measured variables, move into confirmatory factor models, then structural models involving latent causality, and finally a preview of more advanced topics. Examples from a variety of disciplines will be accompanied by code and output from the Mplus software package.
By the end of the course, you should be able to:
- Understand how SEM builds upon and extends the principles and practice of multiple regression.
- Understand how to assess data-model fit of structural equation models.
- Understand how to specify and interpret measured variable path models, and their extensions.
- Understand how to specify and interpret confirmatory factor models, and their extensions.
- Understand how to specify and interpret latent variable path models, and their extensions.
- Read and interpret applied literature that contains structural equation models, and frame theories from your own domain of study as a structural equation model.
This course offers 6 PD hours
Thur., 9:00am–noon; 1pm-4pm, August 2, 2018
Location: Room KRH 305, Katherine Ruffato Hall, DU campus
Gregory R. Hancock is Professor, Distinguished Scholar-Teacher, and Director of the Measurement, Statistics and Evaluation program in the Department of Human Development and Quantitative Methodology at the University of Maryland, College Park, and Director of the Center for Integrated Latent Variable Research (CILVR). He is past chair of the SEM special interest group of the American Educational Research Association (three terms), serves on the editorial board of a number of journals including Psychological Methods and Structural Equation Modeling: A Multidisciplinary Journal, and has taught over 100 methodological workshops in the United States, Canada, and abroad. He is a Fellow of the American Educational Research Association, the American Psychological Association, and the Association for Psychological Science, and received the 2011 Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring by the American Psychological Association.
Denis Dumas is Assistant Professor of Research Methods and Statistics at the University of Denver. In general, his research focuses on understanding student learning, cognition, and creativity through the application of latent variable methods, especially multidimensional item-response theory and non-linear growth models. He believes deeply in the power of quantitative research such as this for improving the field’s current understanding of learning, and supporting the academic development of all students. His research has appeared in such peer-reviewed journals as Educational Researcher, Journal of Educational Psychology, Psychological Assessment, PLoS ONE, and Academic Medicine. He also serves on the editorial board of Contemporary Educational Psychology, and is a recipient of the American Educational Research Association Excellence in Early Career Research Deeper-Learning Fellowship.
(Thursday) 9:00 am - 4:00 pm mst
1999 E. Evans Ave.
Center for Professional Development303firstname.lastname@example.org
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