Year of Study:	3 - 4
Credit Units:	3
Duration:	45hours
Prerequisites:	AMS2320 Business Regression Analysis or with the Instructor’s permission and upon endorsement of the relevant Chairperson or Programme Director.

Missing data are ubiquitous throughout the social, behavioral and medical sciences. This
module aims to provide an accessible and user-friendly introduction to missing data
analyses, with an emphasis on maximum likelihood and multiple imputation. In addition,
students will learn fundamental data audit and management techniques to improve data
quality. Some examples and applications of data audit and imputation are provided. Students
are also required to present their findings to their peers and professors in a professional
manner.

Upon completion of this module, students should be able to:

1. understand the basic concepts, theories and impacts of missing data analysis and data audit;


2. apply estimation and multiple imputation techniques as well as selection and sophisticated models to handle missing data problems;


3. understand the fundamental principles of data audit and management; and


4. apply data audit and imputation skills and techniques with/without the use
   of computer software (SAS, SPSS and Matlab, etc) to solve a range of
   problems in different areas, and interpret the solution and convey the
   analytical results appropriately.