Course 2008-2009 a.y.

Course Objectives

The purpose of the course is to enable students to structure and conduct autonomously a research project based on the analysis of data sets concerning business, economics and in general the social sciences. The course presents a set of tools with an applied perspective, providing the methodological knowledge that is necessary to conduct such projects with a fair level of competence and with the ability to choose appropriate statistical methods for various problems. Lectures providing motivation, methods and examples alternate with applied workshops (to be held in the computer lab) in which students actively participate. The lectures supply the students with the basic concepts and techniques of multivariate data analysis, which are linked to applications and data sets relevant for BIEM students. Lectures and tutorials are also scheduled in order to introduce the students to the use of the widely-used package SPSS for the analysis of multivariate data.

Course Content Summary

• Introduction to applied research using multivariate techniques
• Multivariate data considerations
• Sources of data. Finding data for a research project
• Regression Analysis: the simple one regressor case.
• Regression Analysis: the multivariate case.
• One and Two factors ANOVA
• Principal components analysis: model, extraction, rotation, interpretation
• Factor Analysis
• Scale construction and evaluation: Reliability analysis and composite scores
• Cluster Analysis

Detailed Description of Assessment Methods

Assessment will be based on a project (30%) and either two short exams (one mid-term and one at the end of the course, 35% each) or a final exam (70%). Students who sit and fail the mid-term exam will not be permitted to submit the short exam at the end of the course, but will have to take the final exam. The project can be submitted once only. The maximum grade available to students who do not submit a project is 21/30.

Textbooks

• P. Newbold, W.L. Carlson, B. Thorne , Statistics for Business and Economics and Student CD, 6/E, Prentice Hall (International Edition)., 2007
• R. Warner, Applied Statistics: From Bivariate through Multivariate Techniques, Los Angeles, Sage.,Available from Library Course Reserve, 2008 (for Chapter 4)
• J.H. Hair, R.L. Tatham, R.E. Anderson, W. Black, Multivariate Data Analysis, (International Edition) (Paperback)  5^th or 6^th edition, Pearson Education, ISBN-10: 0139305874 (for Chapter 3-Factor Analysis and Chapter 8-Cluster Analysis)
• M.J. Norusis, SPSS 15.0 Statistical Procedures Companion, Upper Saddle River, New Jersey, Prentice Hall, 2006 (OPTIONAL).

Course Objectives

The purpose of the course is to enable students to structure and conduct autonomously a research project based on the analysis of data sets concerning business, economics and in general the social sciences. The course presents a set of tools with an applied perspective, providing the methodological knowledge that is necessary to conduct such projects with a fair level of competence and with the ability to choose appropriate statistical methods for various problems. Lectures providing motivation, methods and examples alternate with applied workshops (to be held in the computer lab) in which students actively participate. The lectures supply the students with the basic concepts and techniques of multivariate data analysis, which are linked to applications and data sets relevant for BIEM students. Lectures and tutorials are also scheduled in order to introduce the students to the use of the widely-used package SPSS for the analysis of multivariate data.

Course Content Summary

• Introduction to applied research using multivariate techniques
• Multivariate data considerations
• Sources of data. Finding data for a research project
• Regression Analysis: the simple one regressor case.
• Regression Analysis: the multivariate case.
• One and Two factors ANOVA
• Principal components analysis: model, extraction, rotation, interpretation
• Factor Analysis
• Scale construction and evaluation: Reliability analysis and composite scores
• Cluster Analysis

Detailed Description of Assessment Methods

Assessment will be based on a project (30%) and either two short exams (one mid-term and one at the end of the course, 35% each) or a final exam (70%). Students who sit and fail the mid-term exam will not be permitted to submit the short exam at the end of the course, but will have to take the final exam. The project can be submitted once only. The maximum grade available to students who do not submit a project is 21/30.

Textbooks

• P. Newbold, W.L. Carlson, B. Thorne , Statistics for Business and Economics and Student CD, 6/E, Prentice Hall (International Edition)., 2007
• R. Warner, Applied Statistics: From Bivariate through Multivariate Techniques, Los Angeles, Sage.,Available from Library Course Reserve, 2008 (for Chapter 4)
• J.H. Hair, R.L. Tatham, R.E. Anderson, W. Black, Multivariate Data Analysis, (International Edition) (Paperback)  5^th or 6^th edition, Pearson Education, ISBN-10: 0139305874 (for Chapter 3-Factor Analysis and Chapter 8-Cluster Analysis)
• M.J. Norusis, SPSS 15.0 Statistical Procedures Companion, Upper Saddle River, New Jersey, Prentice Hall, 2006 (OPTIONAL).