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: Darren George, Paul Mallery, IBM SPSS Statistics 27 Step by Step A Simple Guide and Reference

Lectures Computer-based sessions Workgroups

For Attending Students Assessment is divided into two components: Continuous Assessment (1/3 of the final grade - 33.3%) This component is mandatory for attending students and consists of graded deliverables distributed throughout the term. These may include practical applications of weekly topics and group projects. Continuous assessment grades cannot be refused. These grades are valid only for the exam sessions held at the end of the term in which the course is delivered. Final Examination (2/3 of the final grade - 66.7%) The final exam evaluates the knowledge and skills acquired during the course. It is a computer-based written exam consisting of case studies and applied questions. For Non-Attending or Non-Compliant Students Students who are exempt from compulsory attendance or do not meet attendance requirements will be assessed through a single final examination (100%). This comprehensive exam covers the entire course content and is designed to compensate for the missed continuous assessment activities. Grading Criteria All deliverables and examinations will be evaluated on a 30-point scale.

The possible assignment of the final thesis will be based on an overall assessment of the student's performance throughout the course. In particular, the following criteria will be considered: Motivation and active participation demonstrated during learning activities, including discussions, group work, and practical exercises; Interest in the course content, expressed through personal contributions to activities and independent exploration of the topics covered; Ability to develop a project consistent with the skills acquired during the course and relevant to the learning objectives of the course; Any demonstrated transversal skills, such as analytical ability, independent judgment, effective communication, and critical thinking. The assignment of the final thesis will be agreed upon with the instructor, who reserves the right to evaluate the consistency between the proposed topic and the student's academic profile.