Structural Equation Modeling (SEM) and Propensity Score Matching (PSM) are widely used by researchers in the social, behavioral, educational, and business sciences. SEM is a combination of psychometrics and econometrics. On the psychometric side, SEM allows for latent variables with multiple indicators. On the econometric side, SEM allows for multiple equations. Structural equation models (SEM) allow both confirmatory and exploratory modeling, meaning they are well suited to both theory testing and theory development.
In the statistical analysis of observational data, Propensity Score Analysis is a statistical technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. It attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among participants that received the treatment versus to those that did not.
The seminar uses R to demonstrate the implementation of propensity score analysis and structural equation modelling. The R software can be downloaded for free.
The course is run by Dr Daniel Boduszek who has used propensity score matching and structural modelling in his numerous psychological, social science, and medical research publications. The Quantitative Research Methods Training Unit (QRM-TU) will also invite Associate Members to lead the training.
The course is designed for researchers and postgraduate students who are engaged in research with large data sets. The prerequisite for taking this seminar is basic knowledge of regression analysis. Researchers from economics, public health, epidemiology, psychology, sociology, social work, medical research, education, and similar disciplines are welcome.
The Quantitative Research Methods Training Unit (QRM-TU), Ramsden Building, University of Huddersfield.
To find out when the next training session will be held, please visit our online store where you can also book your place.