Training and Research
PhD Programme Courses/classes - 2024/2025
This page shows the PhD course's training activities for the academic year 2024/2025. Further activities will be added during the year. Please check regularly for updates!
Research organisation
Credits: 3
Language: English.
Teacher: Gianluca Veronesi, Ivan Russo, Ilenia Confente
Qualitative research methods
Credits: 6
Language: English
Teacher: Sara Moggi, Lapo Mola, Alessandro Lai, Daniela Pianezzi, Riccardo Stacchezzini, Lorenzo Bruno Prataviera
Advanced quantitative research methods
Credits: 6,8
Language: English
Teacher: Valentina Mazzoli, Elena Claire Ricci, Fabio Cassia, Claudia Bazzani, David D'Acunto, Riccardo Scarpa
Advanced regression techniques (models for single binary outcomes, glm, glmm)
Credits: 1
Language: English
Teacher: Laura Rizzi
Basic regression techniques (linear model, multivariate regression, fixed and random effects)
Credits: 1
Language: English
Teacher: Laura Rizzi
Bayesian inference, Monte Carlo Markov Chains
Credits: 0,8
Language: English
Teacher: Luca Grassetti
Business strategy
Credits: 0,5
Language: English
Teacher: Andrea Moretti
Capital structure theories
Credits: 0,5
Language: English
Teacher: Josanco Floreani
Classics in Accounting
Credits: 4
Language: English
Teacher: Francesca Rossignoli, Alessandro Lai, Riccardo Stacchezzini, Cristina Florio
Classics in finance
Credits: 3
Language: English
Teacher: Laura Chiaramonte
Classics in supply chain management
Credits: 4
Language: English
Teacher: Ivan Russo, Barbara Gaudenzi
Consumer behaviour - advanced
Credits: 0,3
Language: English
Teacher: Michela Mason
Consumer behaviour - basics
Credits: 0,3
Language: English
Teacher: Francesco Raggiotto
Data categories and Exploratory Data Analysis
Credits: 0,8
Language: English
Teacher: Laura Pagani
Data reduction methods: cluster analysis, PCA, factor analysis
Credits: 1,5
Language: English
Teacher: Laura Pagani
ESG measurement systems
Credits: 0,5
Language: English
Teacher: Giulio Corazza, Filippo Zanin
Family business
Credits: 0,3
Language: English
Teacher: Daniel Pittino
Fundamentals of project management. PM areas, scope, WBS
Credits: 0,8
Language: English
Teacher: Cinzia Battistella
International business
Credits: 0,5
Language: English
Teacher: Maria Chiarvesio
Marketing
Credits: 0,5
Language: English
Teacher: Francesco Raggiotto
Organisation
Credits: 0,5
Language: English
Teacher: Giancarlo Lauto
Organisation and management of a project proposal: call finding and logical framework
Credits: 0,8
Language: English
Teacher: Luca Brusati
Organisation and management of a project proposal: Gantt and budget
Credits: 0,8
Language: English
Teacher: Luca Brusati
Qualitative comparative analysis
Credits: 0,8
Language: English
Teacher: Francesca Visintin
Review of basic Probability and Statistics, frequentist methods of inference
Credits: 0,8
Language: English
Teacher: Luca Grassetti
Servitisation
Credits: 0,3
Language: English
Teacher: Raffaella Tabacco
Strategic-performance measurement systems
Credits: 0,8
Language: English
Teacher: Filippo Zanin
Time management, Project Cycle Management
Credits: 0,8
Language: English
Teacher: Cinzia Battistella
Trending topics in accounting
Credits: 1
Language: English
Teacher: Stefano Landi
Trending topics in consumer market research for developing innovation
Credits: 2
Language: English
Teacher: Roberta Capitello, Elena Claire Ricci, Claudia Bazzani
Trending topics in finance
Credits: 2
Language: English
Teacher: Laura Chiaramonte
Trending topics in performance management
Credits: 3
Language: English
Teacher: Silvia Vernizzi, Silvia Cantele
Trending topics in supply chain management
Credits: 2
Language: English
Teacher: Silvia Blasi, Ilenia Confente, David D'Acunto
Value-based management systems
Credits: 0,8
Language: English
Teacher: Giulio Corazza, Eugenio Comuzzi
Advanced quantitative research methods (2024/2025)
Academic staff
Riccardo Scarpa, Claudia Bazzani, Fabio Cassia, David D'Acunto, Valentina Mazzoli, Elena Claire Ricci
Referent
Credits
6.75
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
This course aims to cover the main ideas and theoretical results employed in applied business economics research that employs “Advanced quantitative research methods”. The first part of the course is based on Stata, and it focuses on the econometrics of micro data. This part follows closely the textbook by Cameron and Trivedi “Microeconometric Using Stata”, 2010 edition by Stata Press (here abbreviated as MUS). Students are strongly encouraged to browse the content of the Stata YouTube channel with screencast tutorials to be ready for classes.
The second part of the course is modular, and it deals with specific methods, such as process model and process analysis, structural equation modeling, and experimental design, with specific replications of case studies.
Program
Class 1 (MUS ch3) (4 L + 1 P)
Linear models and testing.
Class 2 (MUS ch5 part 1) (4 L + 1 P)
GLS, FGLS, WLS.
Class 3 (MUS ch5 part 2) (4 L + 1 P)
Systems of linear regressions, Survey Data.
Class 4 (MUS ch6) (4 L + 1 P)
Endogeneity & Linear instrumental variables.
Class 5 (MUS ch8) (4 L + 1 P)
Panel data (Part 1)
Class 6 (MUS ch9) (8 L + 1 P)
Panel data (Part 2)
Class 7 (MUS ch10-11) (4 L + 1 P)
NLReg, optimization and testing.
Class 8 (MUS ch14-15) (4 L + 1 P)
Binary and multinomial models.
Class 9 (6 h)
Process model & process analysis. Case study replication
Class 10 (6 h)
Structural equation modeling
(CB-SEM & PLS-SEM)
Class 11 (6 h)
Experimental design and analysis.
Didactic methods
This course is designed to cover in some depth and with practical applications the main topics of a standard advanced graduate course in micreconometrics for business studies and to lead students to the mastering of use of Stata and other software packages used in quantitative business research. Students need to study these topics before attending lectures dealing with each topic.
Classes will be both theoretical and practical. Practice will be given using specific software products, which are dominant in business economics research and microeconometric analysis. Specifically, Stata is a programmable software, which is fully supported by a wide community of researchers meeting regularly in regional conferences (e.g. Stata conferences) and by a periodical journal where new specialized packages are being discussed and made available to users with examples. Furthermore, the Stata software, like R-markdown, for R (see this if you are into R) has its own version of markdown called MarkStat, which enables the creation of replicable data analysis and dynamic documents. These make the documentation of quantitive work much easier, so that researchers can easily communicate to co-authors, collaborators and research students. Several screencasts are available from the official Stata site which can help you have a broad idea of the commands and purpose of the tools, although you will need to download the manuals from the Stata website to have a better understanding and a set of references to the seminal papers. Some universities also provide free Stata resources, such as UCLA. There is a basic introduction to new users on Medium here.
Finally, some good research students have provided dedicated YouTube channels (note: these are not peer-reviewed, so they may contain errors), such as Felix Larry Essilfie, who has some videos on data management and logistic regression, and Bob Wen, who has put together a few playlists with some videos on difference-in-difference, SEM and even solutions to the problem sets in MUS. A good introduction is also available here. James Gaskins’ channel provides a comprehensive series of videos on SEM software applications, specifically SPSS (Amos)
Learning assessment procedures
GRADE COMPONENTS:
1. Class Preparation, Participation, (40%)
2. Assignments & Final take home exam, (60%)
Assessment
GRADING SCALE:
A. Excellent
B. Very good
C. Good
D. Sufficient
E. Not sufficient.
IN-CLASS ACTIVITIES:
Presentations, open discussion, and some practice.
Scheduled Lessons
| When | Classroom | Teacher | topics |
|---|---|---|---|
|
Monday 17 March 2025 14:00 - 19:00 Duration: 4:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | Elena Claire Ricci | Advanced Quantitative Research Methods (AQRM) |
|
Tuesday 18 March 2025 09:00 - 15:00 Duration: 4:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | Elena Claire Ricci | Advanced Quantitative Research Methods (AQRM) |
|
Monday 24 March 2025 14:00 - 19:00 Duration: 4:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | Claudia Bazzani | Advanced Quantitative Research Methods (AQRM) |
|
Tuesday 25 March 2025 09:00 - 15:00 Duration: 4:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | Claudia Bazzani | Advanced Quantitative Research Methods (AQRM) |
|
Monday 31 March 2025 14:00 - 19:00 Duration: 4:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | Riccardo Scarpa | Advanced Quantitative Research Methods (AQRM) |
|
Tuesday 01 April 2025 09:00 - 13:00 Duration: 4:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Veronaonline | Riccardo Scarpa | Advanced Quantitative Research Methods (AQRM) |
|
Tuesday 01 April 2025 14:00 - 19:00 Duration: 4:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | Riccardo Scarpa | Advanced Quantitative Research Methods (AQRM) |
|
Monday 07 April 2025 14:00 - 19:00 Duration: 4:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | Riccardo Scarpa | Advanced Quantitative Research Methods (AQRM) |
|
Tuesday 08 April 2025 09:00 - 15:00 Duration: 4:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | Riccardo Scarpa | Advanced Quantitative Research Methods (AQRM) |
|
Monday 14 April 2025 09:45 - 16:45 Duration: 6:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | David D'Acunto | Advanced Quantitative Research Methods (AQRM) |
|
Wednesday 23 April 2025 09:45 - 16:45 Duration: 6:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | Fabio Cassia | Advanced Quantitative Research Methods (AQRM) |
|
Wednesday 30 April 2025 09:45 - 16:45 Duration: 6:00 AM |
Aula 2.13 Laboratorio del Dipartimento di Management - Polo Santa Marta, via Cantarane 24 - 37129 Verona | Valentina Mazzoli | Advanced Quantitative Research Methods (AQRM) |
PhD school courses/classes - 2024/2025
Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).
1. PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.
More information regarding CFUs is found in the Handbook for PhD Students: https://www.univr.it/phd-vademecum
2. Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, instructions will be sent well in advance. No confirmation e-mail will be sent after signing up. Please do not enquiry: if you entered the requested information, then registration was silently successful.
3. When Zoom links are not explicitly indicated, courses are delivered in presence only.
4. All information we have is published here. Please do not enquiry for missing information or Zoom links: if the information you need is not there, then it means that we don't have it yet. As soon as we get new information, we will promptly publish it on this page.
Teaching Activities ex DM 226/2021: Linguistic Activities
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Law and Economics]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Law and Economics]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
Teaching Activities ex DM 226/2021: Research management and Enhancement
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Arts and Humanities]
Credits: 2,5
Language: Italian
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Law and Economics]
Credits: 2,5
Language: Italian
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Scientific Area]
Credits: 2,5
Language: Italian
Teaching Activities ex DM 226/2021: Statistics and Computer Sciences
CORSO STATISTICA - LIVELLO BASE
Credits: 2,5
Language: English
Validità e affidabilità delle misure e dei test diagnostici
Credits: 0,5
Language: English
Teacher: Alessandro Marcon
Introduction to Probability (module I)
Credits: 1
Language: English
Teacher: Marco Minozzo
Introduction to Probability (module II)
Credits: 1
Language: English
Teacher: Marco Minozzo
Introduction to Statistical Inference
Credits: 1
Language: English
Teacher: Marco Minozzo
USO DI R PER L'ANALISI STATISTICA - LIVELLO BASE
Credits: 1
Language: English
Disegno dello studio nella ricerca osservazionale e sperimentale
Credits: 1
Language: English
Teacher: Alessandro Marcon
Survival analysis: log-rank test, Kaplan-Meier survival curves, Cox regression model
Credits: 1,5
Language: English - Inglese
Teacher: Simone Accordini
APPLICATION OF META-ANALYSIS TO THE EPIDEMIOLOGICAL OR MEDICAL FIELD
Credits: 1
Language: Inglese
Teacher: Giuseppe Verlato
CORSO STATISTICA - LIVELLO INTERMEDIO
Credits: 2,5
Language: English
DETERMINATION OF SAMPLE SIZE TO ACHIEVE A PREDEFINED PRECISION OR POWER
Credits: 0,5
Language: inglese
Teacher: Giuseppe Verlato
GENERALIZED LINEAR MODELS: LOGISTIC REGRESSION, LOGLINEAR MODEL, POISSON MODEL
Credits: 1
Language: English
Teacher: Lucia Cazzoletti
USO DI R PER L'ANALISI STATISTICA - LIVELLO INTERMEDIO
Credits: 0,8
Language: English
Teacher: Alessandro Mantovani
Teaching Activities: Free choice
DOING INTERVIEWS IN QUALITATIVE RESEARCH
Credits: 2
Language: English
Teacher: Chiara Sita'
PROGETTAZIONE E CONDUZIONE DI FOCUS GROUP
Credits: 2
Language: Italian
Teacher: Alessia Maria Aurora Bevilacqua
PROTECTING PSYCHOLOGICAL WELL-BEING IN THE PHD PROGRAM. DEVELOPMENT AND ENHANCEMENT OF PERSONAL STRATEGIES AND ATTITUDES PREDISPOSING TO PROFESSIONAL SATISFACTION AND ETHICAL COLLABORATION
Credits: 1
Language: Italian
Teacher: Michela Rimondini
QUANDO LA RICERCA SI FA ETICA (PERCORSO ORGANIZZATO E FINANZIATO DAL TEACHING AND LEARNING CENTER DI UNIVR)
Credits: 2
Language: English
Teacher: Roberta Silva
Workshop /Participatory approaches and methods in doing research with local actors
Credits: 1,5
Language: English
Teacher: Antonietta De Vita
...E SE VOLESSI FARE IMPRESA
Credits: 1
Language: Italian
IMPARA IL MARKETING DIGITALE
Credits: 1
Language: Italian
LA POESIA ITALIANA ALL'ESTERO
Credits: 1
Language: Italian
Teacher: Massimo Natale
THE EMPIRICAL PHENOMENOLOGICAL METHOD (EPM): THEORETICAL FOUNDATION AND EMPIRICAL APPLICATION IN EDUCATIONAL AND HEALTHCARE FIELDS
Credits: 2
Language: English
Teacher: Luigina Mortari
DOTTORATO E MERCATO DEL LAVORO: WORKSHOP FORMATIVI PER DOTTORANDI E NEO-DOTTORI DI RICERCA
Credits: 4
Language: Italian
ARE YOU SURE YOU CAN DEFEAT A CHATBOT?
Credits: 1
Language: Italian
MEETING UKRAINE: THE IMPACT OF WAR AND FUTURE OPPORTUNITIES
Credits: 1
Language: Italian
EMOTIONS, BELIEFS, AND SKILLS TO FACE CLIMATE CHANGE AND EMBRACE CLIMATE ACTION
Credits: 0,5
Language: English
OPEN SCIENCE: THE MIGHTY STICK AGAINST "BAD" SCIENCE
Credits: 2
Language: English
Teacher: Michele Scandola
COMPUTATIONAL MECHANISMS UNDERLYING SENSORIMOTOR LEARNING
Credits: 4,5
Language: English
Teacher: Matteo Bertucco
CSF DYNAMICS: ANATOMICAL AND FUNCTIONAL FEATURES
Credits: 0,5
Language: English
Teacher: Alberto Feletti
Differential diagnosis of demyelinating diseases of the central nervous system
Credits: 2
Language: English
Teacher: Alberto Gajofatto
sleep related disoders: focus on REM and NREM parasomnia and SR movement disorders
Credits: 1,5
Language: italiano o inglese
Teacher: Elena Antelmi
Tecniche di immagine per l'analisi della composizione corporea
Credits: 1
Language: Inglese/English
Teacher: Carlo Zancanaro
Tecniche di ricerca in neuroscienze: misurare e modulare l'attività neuronale
Credits: 2,3
Language: non prevista
Teacher: Giuseppe Busetto
PhD students
alexia.ciroi@univr.it
fabrizio.facchinetti@univr.it
ernestpaapakwaku.kyereh@univr.it
gianluca.maistri@univr.it
nicolas.maltauro@univr.it
simone.novello@univr.it
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Guidelines for PhD students
Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.
