Insegnamento a.a. 2016-2017

Go to class group/s: 15 - 16 - 17 - 18

BIEM (6 credits - I sem. - OB  |  SECS-P/06)

Course Director:
ARNSTEIN AASSVE

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, finance, 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. The course gives support for the use of the software program SPSS, a widely used software package in the social sciences, though students are free to use other softwares.

Course Content Summary

• Introduction to applied research, research design, research question, causality.
• Sampling and data sources, finding data for research projects.
• Regression analysis: the simple one regressor case, multivariate regression, assumptions and properties, violation of assumptions and remedies, time series analysis and seasonality.
• One and two factors ANOVA.
• Factor analysis: model, extraction, rotation, interpretation.
• Scale construction and evaluation: reliability analysis and composite scores.
• Cluster Analysis.
• Regression analysis revisited: regression analysis in combination with factor analysis and cluster analysis, binary response models.

Detailed Description of Assessment Methods

The written exam makes up 70% of the grade and the project 30%.
The exam is written and lasts two hours. It is possible to take the written exam in two partial parts. In this case, the first partial must be passed (18/30) to became eligible to do the second partial.
The exam and the project are valid until the end of the academic year.

Textbooks

• P. Newbold, W.L. Carlson, B. Thorne, Statistics for Business and Economics and Students cd, Prentice Hall, (International Edition), 2007, 6th Ed. For regression and ANOVA.
• Lecture notes (sampling, data sources, research design, factor analysis, scale reliability and cluster analysis).

Exam textbooks & Online Articles (check availability at the Library)

Prerequisites

Statistics, Mathematics, Computer skills for Economics.

Last change 12/04/2016 15:12