Training and Research
PhD Programme Courses/classes - 2023/2024
Research organisation
Credits: 6
Language: Italian
Teacher: Ivan Russo, Cecilia Rossignoli, Alessandro Zardini, Ilenia Confente
Advanced quantitative research methods
Credits: 11
Language: Italian
Teacher: Elena Claire Ricci, Claudia Bazzani, Alessandro Zardini, Riccardo Scarpa
Classics in accounting
Credits: 4
Language: Italian
Teacher: Francesca Rossignoli, Alessandro Lai, Riccardo Stacchezzini, Cristina Florio
Classics in finance
Credits: 3
Language: Italian
Teacher: Laura Chiaramonte
Classics in supply chain management
Credits: 4
Language: Italian
Teacher: Ivan Russo, Barbara Gaudenzi
Qualitative research methods
Credits: 10,5
Language: Italian
Teacher: Sara Moggi, Lapo Mola, Alice Francesca Sproviero, Alessandro Lai, Riccardo Stacchezzini
Trending topics in accounting
Credits: 2
Language: Italian
Teacher: Stefano Landi
Trending topics in consumer market research for developing innovation
Credits: 2
Language: Italian
Teacher: Roberta Capitello, Elena Claire Ricci, Claudia Bazzani
Trending topics in finance
Credits: 2
Language: Italian
Teacher: Laura Chiaramonte
Trending topics in performance management
Credits: 2
Language: Italian
Teacher: Silvia Vernizzi, Silvia Cantele
Trending topics in supply chain management
Credits: 2
Language: Italian
Teacher: Silvia Blasi, Ilenia Confente, David D'Acunto
PhD school courses/classes - 2023/2024
Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).
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.
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, no confirmation e-mail will be sent after signing up.
Teaching Activities ex DM 226/2021: Linguistic Activities
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
Teacher: Dora Renna
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Law and Economics]
Credits: 2,5
Language: English
Teacher: Emanuela Tenca
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
Teacher: Jessica Jane Nocella
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
Teacher: Silvia Velardi
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
Teacher: Monica Antonello
ENGLISH FOR ACADEMIC PRESENTATION SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
Teacher: Geltrude Daniela Vescio
ENGLISH FOR ACADEMIC WRITING SKILLS [Arts and Humanities]
Credits: 2,5
Language: English
Teacher: Dora Renna
ENGLISH FOR ACADEMIC WRITING SKILLS [Law and Economics]
Credits: 2,5
Language: English
Teacher: Emanuela Tenca
ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 1 st Session]
Credits: 2,5
Language: English
Teacher: Jessica Jane Nocella
ENGLISH FOR ACADEMIC WRITING SKILLS [Life and Health Sciences - 2 nd Session]
Credits: 2,5
Language: English
Teacher: Silvia Velardi
ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-1st Session]
Credits: 2,5
Language: English
Teacher: Monica Antonello
ENGLISH FOR ACADEMIC WRITING SKILLS [Natural Sci. and Engineering-2nd Session]
Credits: 2,5
Language: English
Teacher: Geltrude Daniela Vescio
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
Teacher: Donatella Boni
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Law and Economics]
Credits: 2,5
Language: Italian
Teacher: Luisella Zocca
SEMINARIO AVANZATO SULLE RISORSE BIBLIOTECARIE PER LA RICERCA [Scientific Area]
Credits: 2,5
Language: Italian
Teacher: Elena Scanferla
Teaching Activities ex DM 226/2021: Statistics and Computer Sciences
INTRODUCTION TO PROBABILITY (MODULE I)
Credits: 1
Language: English
Teacher: Marco Minozzo
INTRODUCTION TO PROBABILITY (MODULE II)
Credits: 1
Language: English
Teacher: Marco Minozzo
BASIC LEVEL STATISTICS
Credits: 2,5
Language: English
INTRODUCTION TO STATISTICAL INFERENCE
Credits: 1
Language: English
Validità e affidabilità delle misure e dei test diagnostici
Credits: 0,5
Language: English
Teacher: Alessandro Marcon
BASIC LEVEL STATISTICS
Credits: 2,5
Language: Italian
Statistical analysis with R - module I
Credits: 1
Language: Italian
Teacher: Erica Secchettin
Generalized linear models: logistic regression, loglinear model, Poisson model
Credits: 2
Language: English
Teacher: Lucia Cazzoletti
Disegno dello studio nella ricerca osservazionale e sperimentale
Credits: 1,5
Language: English
Teacher: Alessandro Marcon
Calcolo della numerosità campionaria in funzione di una precisione o potenza statistica prefissata
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Introduzione alla meta-analisi per la ricerca biomedica (revisione della letteratura, raccolta dei dati, costruzione del database)
Credits: 1
Language: English
Teacher: Giuseppe Verlato
Applicazioni della meta-analisi in campo epidemiologico e medico
Credits: 1
Language: Inglese
Teacher: Giuseppe Verlato
Survival analysis: log-rank test, Kaplan-Meier survival curves, Cox regression model
Credits: 1,5
Language: Inglese - English
Teacher: Simone Accordini
INTERMEDIATE STATISTICS [Recommended for Human Sciences]
Credits: 2,5
Language: English
INTERMEDIATE STATISTICS [Tutti i corsi di studio]
Credits: 2,5
Language: English
Statistical analysis with R - module II
Credits: 2
Language: Italian
Teacher: Erica Secchettin
Teaching Activities: Free choice
PROTECTING PSYCHOLOGICAL WELL-BEING IN THE PHD PROGRAM: WHAT DO WE NEED TO CONSIDER FOR BEING A GOOD SCIENTIST: BEST PRACTICE AND THE ETHICS OF SCIENCE
Credits: 1
Language: inglese
Teacher: Paola Cesari
QUANDO LA RICERCA SI FA ETICA (PERCORSO ORGANIZZATO E FINANZIATO DAL TEACHING AND LEARNING CENTER DI UNIVR)
Credits: 2
Language: Italian
Teacher: Roberta Silva
INTRODUCTION TO WEB MARKETING: LEVERAGING GOOGLE BUSINESS PROFILE FOR EFFECTIVE LOCAL MARKETING
Credits: 1,5
Language: English
LA COMUNICAZIONE UMANISTICA: OPPORTUNITA' E RISCHI
Credits: 1
Language: Italiano
Italian Poetry abroad
Credits: 1
Language: Italiano
Teacher: Massimo Natale
BUSINESS MODEL CANVAS PILL
Credits: 1,5
Language: English
APPROCCI E METODOLOGIE PARTECIPATIVE NELLA RICERCA CON GLI ATTORI DEL TERRITORIO
Credits: 1,5
Language: Italian
Teacher: Cristiana Zara
DOING INTERVIEWS IN QUALITATIVE RESEARCH
Credits: 1,5
Language: English
Teacher: Chiara Sità
DIFFERENTIAL DIAGNOSIS OF DEMYELINATING DISEASES OF THE CENTRAL NERVOUS SYSTEM
Credits: 2
Language: English
Teacher: Alberto Gajofatto
IL SONNO E I SUOI DISTURBI: FOCUS SULLE PARASONNIE E I DISTURBI DEL MOVIMENTO IN SONNO
Credits: 1
Language: English
Teacher: Elena Antelmi
IMAGING TECHNIQUES FOR BODY COMPOSITION ANALYSIS
Credits: 1
Language: English
Teacher: Carlo Zancanaro
OPEN SCIENCE: THE MIGHTY STICK AGAINST "BAD" SCIENCE
Credits: 2
Language: English
Teacher: Alberto Scandola
THE EMPIRICAL PHENOMENOLOGICAL METHOD (EPM): THEORETICAL FOUNDATION AND EMPIRICAL APPLICATION IN EDUCATIONAL AND HEALTHCARE FIELDS
Credits: 2
Language: English
THE PATHWAY OF OXYGEN: CAUSE OF HYPOXEMIA
Credits: 1
Language: English
Teacher: Carlo Capelli
Generalized linear models: logistic regression, loglinear model, Poisson model (2023/2024)
Teacher
Referent
Credits
2
Language
English
Class attendance
Free Choice
Location
VERONA
Learning objectives
An introduction to the fundamentals of generalized regression models will be given in this course, with a focus on models for count, binary, and categorical data. These types of response variables are widely used in industrial applications as well as observational and experimental research.
Upon successful completion of the course, students will be able to:
• Describe the general structure of a GLM and similarities and differences with linear models
• Estimate and interpret a logistic regression model
• Estimate and interpret a Poisson regression model
• Know of issues and some strategies for dealing with overdispersion in some generalised linear models (GLMs)
Prerequisites and basic notions
This course assumes a good understanding of probability and mid-level knowledge of linear regression theory.
Program
The course covers methods for regression analysis of responses that do not follow the normal distribution, especially of discrete responses. We will learn to understand some of the common statistical methods for fitting regression models to such data. In particular, we will consider logistic regression, Poisson regression and log-linear models. The lecture focuses on the development, theoretical justification, and interpretation of these methods.
When and where
Teaching forms mainly consist of lectures (8h) and exercises proposed by the teacher. The teaching material (slides of the theoretical lessons) is made available to the students on the e-learning web page of the course (Moodle platform). Lessons will be delivered via Zoom. Full attendance is required.
Learning assessment procedures
There is no exam
Assessment
There is no exam, hence there is also no definition of the evaluation criteria.
Criteria for the composition of the final grade
There is no grade because there is no exam.
Scheduled Lessons
| When | Classroom | Teacher | topics |
|---|---|---|---|
|
Tuesday 19 March 2024 14:30 - 16:30 Duration: 2:00 AM |
To be defined | Lucia Cazzoletti | Introduction to generalised linear models. Review of the general linear model: assumptions of the linear model (independence of observations, homoskedasticity of errors, linearity of coeffiicents) , least squares estimation and maximum likelihood estimation. Main features of the genralised linear model: i) the probability distribution function of the random component of the response variable belongs to the exponential family, ii) a differentiable and monotonic link function relates the mean of the response variable to the linear predictor, a linear combination of coefficients and explanatory variables. |
|
Tuesday 26 March 2024 14:30 - 16:30 Duration: 2:00 AM |
To be defined | Lucia Cazzoletti | Introduction to the theoretical basis of logistic regression model, commonly used for binary (proportion/percentage) data, as a generalised linear model. Binomial distribution for the outcome binary variable. The link between probability and logodds. Maximum likelihood estimation of the coefficients of the model. Interpretation of the meaning of the regression coefficients and their statistical significance. |
|
Wednesday 03 April 2024 14:30 - 16:30 Duration: 2:00 AM |
To be defined | Lucia Cazzoletti | Introduction to the theoretical basis of Poisson regression model, commonly used for count data, as a generalised linear model. Poisson distribution for the outcome variable. Maximum likelihood estimation of the coefficients of the model. Interpretation of the meaning of the regression coefficients and their statistical significance. Use of the offset to take into account the different exposure of subjects. Extensions of the Poisson Regression Model: Negative binomial regression model (NBRM), Zero-inflated poisson (ZIP) model, Zero-truncated count data model. |
|
Wednesday 24 April 2024 14:30 - 16:30 Duration: 2:00 AM |
Aula virtuale - Lezione online | Lucia Cazzoletti | Using Deviances to Compare Models for Logistic and for Poisson Regression Models. Use of the Likelihood Ratio Test to assess the presence of overdispersion. Some hints about the log-linear model in the presence of contingency tables |
Faculty
laura.chiaramonte@univr.it
francesca.rossignoli@univr.it
0444 393941 (Ufficio Vicenza) 0458028261 (Ufficio Verona)
riccardo.scarpa@univr.it
gianluca.veronesi@univr.it
PhD students
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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.
