Quantitative data analysis is becoming increasingly important in the social sciences and in Strategic Management studies. The use of these methods (e.g. opinion polls, aggregate analysis of company data, evaluation of management strategies) is crucial nowadays not only in the field of academic research, but also in business and public institutions. The ability to properly understand and critically evaluate the results of quantitative analyzes is a key ingredient for social scientists as well as for professionals. The course introduces students to quantitative data analysis world with the aim of providing a solid conceptual and operational basis to learn how to read the data and apply statistical analysis models.

Knowledge and understanding At the end of the course, the students will be able to critically analyze quantitative data and to apply the main data analysis techniques. The level of mathematical formalization (use of complex formulas and calculations) will be reduced. Students will instead be required to be able to evaluate the main methodological complexities inherent in data analysis and to use the most appropriate empirical tools to evaluate Strategic Management choices. Applying knowledge and understanding The course will provide students with the tools necessary to apply the quantitative analysis techniques discussed during the course using statistical software. Making judgments The course aims to provide students with the tools to effectively evaluate and use the main techniques of quantitative data analysis. Furthermore, the course aims to stimulate the ability to read and interpret possible strategies through the use of statistical analysis. To this end, the course combines introductory lectures with computer-based sessions, group work and expert testimony. Communication skills One of the main objectives of the course is to provide students with the necessary skills to be able to communicate the results obtained from quantitative data analysis in an appropriate and effective way. Learning skills The course will be conducted mainly in seminar / workshop form. The activities will therefore be characterized by a practical and dynamic approach, to favor the development of technical skills that can be used both in the world of business and in the academic career.

The course will provide the necessary skills to properly use the main statistical techniques of data analysis. Alongside the main descriptive techniques (measures of central tendency and dispersion), the course will introduce simple techniques of bivariate analysis (cross-tabulation and correlation), clarifying the concepts of control variable and spurious relationships. Students will subsequently be introduced to the concepts of inference and statistical significance. The course will then focus on regression analysis, which forms the foundation for the application of more sophisticated statistical techniques. Finally, students will be introduced to the main data reduction techniques (i.e. cluster and factor analysis) The course will have a mainly practical orientation: students will be required not only to understand the main data analysis techniques, but to put them into practice through using main statistical software. The introductory sessions of the main data analysis techniques will be followed by sessions in which students will be asked to carry out quantitative analyzes.

Recommended: Alan Agresti, Barbara Finlay. Statistical methods for social sciences. Prentice Hall, 4th edition Suggested: SPSS

Lectures Computer-based sessions Workgroups

Student assessment is based on: 1) a group assignment (worth one-third of the final grade); 2) an individual written final exam (worth two-thirds of the final grade). The group assignment consists of developing and carrying out a small research project throughout the course, to be presented in class in the last week of the course. The final exam is a written test. Each exercise carries the same weight in the overall evaluation. The final exam accounts for two-thirds of the final grade. All activities will be graded on a 30-point scale

Criteria for accepting requests are:  1) Exam grade 2) Quality of the proposed project Thesis requests should be presented in the form of a one-page project, with a short reference lists and a suggested index. The project should briefly detail the research question and the specific case studies considered. Potentially acceptable requests will be discussed together by the instructors and the student.