HPE 803 Advanced Quantitative Research Method in HPE

This course offers an in-depth study of multiple linear regression based on the content of HPE 505 as well as a basic introduction to a few other advanced quantitative data analysis approaches commonly used in health professions education literature. These include hierarchical linear modeling (HLM), multivariate analysis of variance (MANOVA), exploratory factor analysis (EFA), and structural equation modeling (SEM). Except the session on SEM, all other sessions will use SPSS for data analysis demonstration and practice purposes.

Credits

2

Prerequisite

HPE 505

Learning Objectives

1. Conduct and interpret analysis to test assumptions of multiple linear regression model.
2. Determine the need for transformation of the dependent or independent variables in regression models.
3. Compare sub-models of multiple linear regression.
4. Describe the research context in which multinomial logistic regression/log-linear analysis is appropriate and conduct the analysis.
5. Describe the research context in which HLM approach is appropriate, identify fixed and random coefficients, set up the data structure for HLM analysis, and assess the model fit and compare models.
6. Recognize the MANOVA test statistics and interpret the output.
7. Explain the role of factor structure in reliability analysis, identify situations where EFA is appropriate and important limitations, and conduct EFA.
8. Explain basic concepts related to SEM and understand core SEM techniques.
9. Critically appraise literature that adopted the above statistical modeling techniques.