HI 5353 Health Informatics Data Analysis
This course provides the student the opportunity to know when and how to use state of the art data analysis computer software to perform each of a comprehensive set of the most important and frequently used data analysis techniques for research and evaluation in health informatics. The student will choose the most appropriate data analysis tools, to perform qualitative, descriptive, inferential, parametric, non-parametric, multifactor and multivariate techniques as well as graphical data modeling analytic techniques using the computer. Qualitative data analysis and related software will demonstrate alternate methods for data collection and reduction.
Consent of instructor, high-speed Internet, personal computer.
After completing this course, students should be able to:
- Appropriately choose, conduct, interpret, and report computer data analyses involving important and frequently used high level data analysis procedures for health and behavioral science research (e.g., descriptive and inferential, univariate and multivariate, parametric and nonparametric, qualitative and quantitative procedures) including graphical outputs, multivariate analysis of variance, covariance and regression, canonical correlation, multivariate repeated measures analysis, hierarchical log linear regression, additional nonparametric and other procedures, as implemented by standard high level computer data analysis procedures.
- Identify when a multivariate analysis procedure is appropriate for decision-making regarding research or evaluation hypotheses.
- Use SPSS GLM, MANOVA and other procedures to perform multivariate and other multivariable analyses.
- Present results of a multivariate data analysis conducted with real data in a format suitable for submission and presentation as an AMIA paper/poster paper.