Basic Structural Equation Modeling with IBM SPSS Amos
Structural equation modeling (covariance structure analysis, latent variable analysis, and causal modeling), in its fullest form, is a marriage between path analysis and factor analysis, facilitating the investigation of causal relations among both measured and latent variables. The particular advantage of methods involving latent variables is that causal theories may be investigated as they pertain directly to the underlying constructs of interest, rather than to the measured variables whose observed relations are often attenuated by error of measurement.
This intensive, two‐day workshop deals with the principles, assumptions, strengths, limitations, and applications of the family of techniques known as structural equation modeling (SEM). Basic SEM techniques, including path analysis, confirmatory factor analysis (CFA), and full structural‐regression models, will be taught. Some familiarity with basic statistical techniques, such as multiple regressions, is assumed. The presentation of topics will be conceptually‐ rather than mathematically‐oriented, and examples of the application of SEM to different kinds of actual research problems will be considered.
Dr Timothy Teo is Associate Professor at the University of Auckland. Prior to that, Timothy has taught at the primary, secondary, polytechnic, and university levels in Singapore. To date, he has published 91 peer-reviewed papers, alongside many book chapters and conference papers, on topics relating to technology acceptance, computer attitudes, meta-cognition, and teachers’ and students’ beliefs about teaching and learning using multivariate statistics and statistical modeling. Timothy sits on the editorial boards of 22 international journals, out of which four as an Associate editor.
To find out more information or to register for this course, please email firstname.lastname@example.org
Some familiarity with basic statistical techniques, such as multiple regressions, is assumed. The presentation of topics will be conceptually‐ rather than mathematically‐oriented, and examples of the application of SEM to different kinds of actual research problems will be considered. This course assumes no prior experience with SEM, and is intended as both a theoretical and practical introduction.
Widely used by researchers and marketers, Structural Equation Modeling (SEM) enables you to use both latent and observed variables to detect hidden relationships that traditional analytical techniques are unable to capture. Learn how you can apply the SEM techniques in your customer, patient and student data to improve your program development and campaign effectiveness significantly.
During the two-day course, you will learn useful skills for application in:
• Market Research to model how customer behaviour and attitudes impact product sales
• Medical and Healthcare Research to identify which factors best predict a person’s propensity towards addictions (drugs or alcoholic) so as to design effective intervention programs
• Academic Program Evaluation to examine student data such as test scores and student feedback for determining program effectiveness
• Introduction & Admin
• Basics of SEM
• Introduction to AMOS Graphics
• Path modeling
• EFA vs CFA
• Modeling CFA
• Full SEM
• Application of SEM in scale development
Participants will receive individual consultation time for up to half an hour depending on the class size.