Quantitative data analysis is becoming increasingly important in the social sciences and in Accounting and Finance studies. The use of these methods (e.g. aggregate analysis of accounting data, performance evaluation of company strategies and policy) 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 the impact of managerial choices and changing contextual factors on company performance. 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 performance effect of managerial choices and contextual conditions through the use of statistical analysis. To this end, the course combines introductory lectures with computer-based sessions and group works. 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. Linear programming methodologies and optimization techniques will also be covered with the related sensitivity analyses 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 Additional material on luiss.learn.it

Lectures Computer-based sessions Workgroups

The assessment will be done through: 1) a continuous assessment approach based on 2 deliverables; 2) analysis of the Accounting project developed during the course; 3) Final written exam. The assignment will consist of applications on topics done over the weeks. Each assignment will have a weight of 20% of the final grade (the total of the assignments will then be 40% of the final grade) The Accounting Project will have a weight of 40% of the final grade. The final exam will be written and will consist of exercises. It will have a weight of 20% of the final grade. All deliverables will be evaluated on a 30-point scale.

Criteria for assigning the thesis are: 1) Exam results 2) Quality of the research project The project must be presented in written form (one page) introducing the main research question to be addressed and presenting a list of bibliographic references and an hypothetical index.