• Italiano
  • English

Training program

The following foundation subjects make up the common core of the course

- Databases for bibliographic research (8 hours; first year)

- Statistical methods for analyzing economic and business data (36 hours; first year)

- Qualitative research methodology (20 hours; first year)

- Quantitative research methodology (28 hours; first year)

- Game theory (13 hours; first year)

- Big databases and economic analysis. An application to patent data (12 hours; first year)


Subsidiary courses

The following interdisciplinary and transversal skills are also covered:

- Languages

- IT

- Management of research and knowledge of European and international research systems

- Enhancement and dissemination of results, intellectual property and open access to research data and products

- Seminars

-  Laboratory activities

Course subjects

Databases for bibliographic research (Cristina ZERBINI; Laura L'EPISCOPO - University of Parma)

The course covers the processes and methods of publishing research tools for analysis of academic literature through databases.

The first part of the course focuses on the importance of academic publications for the advancement of international knowledge and for an academic career. It examines the different types of publication, the structure of an academic paper, choosing a journal and the review and publication processes. Students are invited to share personal experience.

The second part of the course focuses on the literature review: the choice of keywords, the use of the databases available through the University of Parma (e.g. Ebsco Host, Emerald, Essper) as well as other online resources (e.g. Researchgate, Academia.edu, Scienceopen, Iris, sole24ore), and reference management software (Zotero and Mendeley). There are practical exercises

Statistical methods for the analysis of economic and business data (Andrea CERIOLI; Fabrizio LAURINI - University of Parma)

The course consolidates knowledge of advanced statistical methodologies relevant to economics and business sciences and typically applicable to big data. The course consists of two modules.

The first module focuses on statistical models and algorithms for prediction and segmentation. It analyses the following methodologies: association measures and rules, and their applications to Market Basket Analysis; linear regression for classical inferential aspects and diagnosis of violations of basic assumptions; logistic regression for prediction of individual behaviour; classification trees, including overfitting and misclassification rates; Cluster Analysis and the non-hierarchical K-means segmentation algorithm. All methodologies are applied to economics or business examples using SPSS.

The second module focuses on theory and algorithms describing phenomena characterized by dynamics evolving over time. There are two aspects: the first extends multiple regression models to cases of seasonal data using ARIMA-type models. Basic assumptions are introduced, and inferential results and post-adaptation diagnostics are presented. The goal is to build models that can be used to provide reliable forecasts in the short and medium term. The regression model is subsequently extended to panel data. Practical exercises will verify student learning in the methodological sessions. There are two assignments at the end of the course.

Qualitative research methodology (Caterina CAVICCHI; Emidia VAGNONI - University of Ferrara)

The module presents research design and offers in depth focus on qualitative research methodologies. Content:

- Definition of the research topic;

- Identifying research questions;

- Literature analysis and the systematic literature review;

- Relationship between theory and research – basics of epistemology and ontology;

- Qualitative - rational research methods, strengths and weaknesses;

- Approaches to qualitative research - ethnography, grounded research, action research, narrative research, case studies;

- Qualitative case studies - research design and data collection methods;

- Action research - research design and management methods;

- Data collection methods - focus groups and qualitative interviews;

- Data analysis methods - text analysis and content analysis.

Quantitative research methodology (Beatrice LUCERI; Donata Tania VERGURA; Simone AIOLFI - University of Parma)

The course extends student knowledge on the collection and analysis of primary and secondary data through different quantitative research techniques.

The first part focuses on the use of quantitative research techniques for the collection of primary data. Two research techniques are studied. The structured questionnaire and the main methods of data analysis, with particular regard to validity and reliability measurement models are covered. The next focus is on the technique of experiments and quasi-experiments and construction process of the experimental protocol, the types of data collected and the basic analysis methodologies.

The second part delivers the theoretical knowledge and application tools to carry out quantitative systematic reviews of academic literature. The course focuses first on the use of SciMAT open source software for longitudinal bibliometric analyses for mapping the cognitive structure and thematic evolution over time of a line of research. The course also offers an overview of the theoretical and practical tools for systematically reviewing literature by means of meta-analyses and for critical reading meta-analyses.

The lectures will describe and discuss published academic papers,  as well as data analysis software (mainly SPSS).

At the end of the course, students have about two months to design their research project  on a topic of interest and to draft research questions and hypotheses, the methodology and the expected results. The projects are assessed individually, and strengths and weaknesses of the research designs are shared.

Game theory (Chiara LODI - University of Urbino)

 Game theory represents the strategic interaction between different agents and has become a key tool in economics studies. This course presents the basics of game theory and different real-world applications. The original forms of well-known games such as the Prisoner’s Dilemma, the Battle of the sexes and the Chicken game are presented in their original form and through examples of environmental economics. Mathematical tools and a basic introduction to sequential games and concepts of bounded rationality are provided.


- Basic concepts - rationality, payoff, classification and representation of games, strategies and Nash equilibrium, prisoner's dilemma (original game, from a monopoly to a duopoly; for example, common access to natural resources);

- Main games: Battle of the sexes (original game; mutually exclusive environmental project), the Chicken game (original game, cross-border species conservation policy), main-agent base model;

- Sequential games - Entrant-Incumbent game and Stackelberg model;

- Bounded rationality and adaptive dynamic models.

Big databases and economic analysis. Application to patent data (Ugo RIZZO; Nicolò BARBIERI; Marianna GILLI - University of Ferrara)

Given the importance of big data for decision making and policy making, the ability to analyze large databases has become increasingly important to researchers. The module develops student ability to manage and analyze large databases using the OECD patent database.

The data management employed is STATA and the course presents basic use.


- Opening datasets; importing datasets from other sources;

- Variables - types, commands for creation and replacement;

- Renaming and labelling variables; encoding variables;

- Operatiing if and looping commands (foreach, forvalues)

- Operatiing keep and drop commands; designing data subsets.

Use of STATA:

- Descriptive statistics;

- Construction of the main summary tables (Estout) and graphs (bidirectional command).

Introduction to patent databases, focusing on:

- OECD patent databases;

- Overview of the OECD REGPAT;

- Key aspects of patents (technological classes, citations, patent families, etc.);

 - Constructing key variables with patent data;

- Using patent data for the study of innovation.

The use of patent information for economic purposes, focusing on:

- Analysis of the economy of innovation with patent data;

- Deducing indicators from patent data;

- Application of the patent based indicator;

- Answering research questions based on patent data analysis (Examples).

  • Language module

The Doctoral School in Economic and Legal Sciences of the University of Parma offers a 36-hour study skills course in English for Academic Purposes at the University Language Centre. The course is delivered in English by a mother-tongue teacher and covers writing academic articles, skills for conference attendance and presentation skills. Students carry out tasks associated with a variety of academic roles, including listening to lectures / presentations and note-taking; writing short report from notes; reading and writing a summary / abstract / research paper / report / peer review; poster design; preparing and delivering presentations; describing processes; verbalizing data; describing information presented visually; writing a CV; drafting formal international correspondence; and grammar revision where appropriate. For the final mark, students are evaluated continuously through participation, task completion and level.

Credit-bearing courses in Italian for speakers of other languages are held at the University Language Centre of the University of Ferrara through Doctoral school IUSS-Ferrara (1391).

• IT module

The Doctoral School in Economic and Legal Sciences and the IUSS-Ferrara (1391) offer credit-bearing seminars and lessons for the acquisition of IT skills for the following:

- Science and technology: advanced IT and computing / simulation environments;

- Life sciences: widely used computer systems and dedicated databases;

- Humanities: computerized cataloguing and archiving, dissemination, EU databases and econometric software.

Introduction to Python (Pietro BATTISTON - University of Parma)

This course presents the basics of programming in Python and standard tools for data analysis. Specific applications will be based on students’ requirements and research interests. Sessions will be interactive and focus on the following topics:

- Introduction to syntax and fundamental data structures;

- Basics of object-oriented programming;

- Control flow, modules, input / output;

- Introduction to NumPy and Pandas for data manipulation;

- Examples of using statsmodels and scikit-learn for analysis in the presentation of individual or group projects.

Management of research and knowledge of European and international research systems

The Doctoral School in Economic and Legal Sciences and the IUSS-Ferrara (1391) hold credit bearing cycles of seminars and lectures on technology transfer and knowledge of research and financing systems. They aim to help doctoral students in scientific, legal and economic fields to meet the challenges of innovation and the renewal of Italy inside or outside academia.

Enhancement and dissemination of results, intellectual property rights and open access to research data and products

The Doctoral School in Economic and Legal Sciences and the IUSS-Ferrara (1391) hold cycles of seminars and lessons on the Protection of Intellectual Property, with particular attention to the writing of the final dissertation thesis.  


In-depth seminars on the research areas of the PhD program are given in all three years both by teaching staff of the courses and by external experts from Italian and overseas universities, who collaborate on an ongoing basis. The aim is to offer students specialist knowledge and ideas for innovative research.

Statistics for the social sciences

The Statistics course of the Parma University PhD in Neuroscience is open to students of the PhD in Economics and Management of Innovation and Sustainability. The course provides theoretical and applicative tools for statistical techniques in General Linear Model (GLM) and extensions most frequently used in psychobiological and cognitive neuroscience research. The lessons will be practical: for each topic ad hoc data frames will be provided, and the analysis conducted in the classroom using R Core Team (2020). There will be focus on the research areas of primary interest for current students.


- General Linear Models;

- Relationship models using quantitative variables: zero and higher order correlation, multiple linear regression, path analysis;

- Relationship models between continuous variables and nominal / categorical variables: Variance analysis (factorial designs, repeated measures, mixed models);

- Relationship models between categorical variables (Generalized Linear Model): Poisson and logistic regression (binary and multinomial);

Robust analysis and non-parametric tests.  

Laboratory activities

For research activities, students can use the laboratories and research centers of the university departments taking part in the course program. At Parma university, for example, they will find:

- Business Administration - LAM-Laboratory in Accounting and Management, SILAB-Social Impact Lab, University Bioethics Center;

- Finance, banking and insurance - Research laboratory in Governance and Internal Controls in banks (with Tor Vergata University (Rome);

- Marketing - Neuromarketing research laboratory, Fidelity Observatory, RetaiLab;

- Economics - Research Laboratory in Experimental Economics, Unintended Consequences Lab, LEIGIA-Laboratory on the Economics of Businesses in Italy, Germany and Austria.

Pubblicato Thursday, 11 August, 2022 - 16:38 | ultima modifica Monday, 5 September, 2022 - 13:32