Training and Research
PhD school courses/classes - 2025/2026
Cose che devi assolutamente sapere
1. I dottorandi devono ottenere un determinato numero di CFU ogni anno frequentando le attività didattiche offerte dal proprio corso di dottorato e dalla Scuola di Dottorato. Per ulteriori informazioni, consulta il Vademecum del Dottorando: https://www.univr.it/phd-vademecum
2. Non è richiesta l'iscrizione ai corsi, a meno che non sia esplicitamente indicato; si prega di consultare le informazioni sul corso per verificare se l'iscrizione è richiesta o meno. Quando l'iscrizione è effettivamente richiesta, non verrà inviata alcuna e-mail di conferma dopo la registrazione. Si prega di non inviare richieste di conferma: se sono state inserite le informazioni richieste, l'iscrizione è andata a buon fine.
3. Quando i link Zoom delle lezioni non sono esplicitamente indicati, si intende che i corsi sono organizzati per essere erogati solo in presenza. Non è quindi possibile in questi casi richiedere un link Zoom.
4. Tutte le informazioni sui corsi in nostro possesso vengono pubblicate qui. Si prega di non richiedere informazioni mancanti o link Zoom: non appena riceveremo nuove informazioni, le pubblicheremo e comunicheremo tempestivamente.
Teaching Activities ex DM 226/2021: Linguistic Activities
Teaching Activities ex DM 226/2021: Research management and Enhancement
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
STATISTICAL ANALYSIS WITH R - MODULE 1
Credits: 1
Language: English
Teacher: Michele Scandola
INTRODUCTION TO STATISTICAL INFERENCE
Credits: 1
Language: English
Teacher: Marco Minozzo
BASIC LEVEL STATISTICS
Credits: 2.5
Language: English
Teacher: Margherita Brondino
GENERALIZED LINEAR MODELS: LOGISTIC REGRESSION, LOGLINEAR MODEL, POISSON MODEL
Credits: 1
Language: English
Teacher: Lucia Cazzoletti
Calcolo della numerosità campionaria in funzione di una precisione o potenza statistica prefissata
Credits: 0.5
Language: inglese
Teacher: Giuseppe Verlato
ANALISI DI SOPRAVVIVENZA: TEST LOG-RANK, CURVE DI SOPRAVVIVENZA DI KAPLAN-MEIER, MODELLO DI REGRESSIONE DI COX
Credits: 1.5
Language: English
Teacher: Simone Accordini
STATISTICAL ANALYSIS WITH R - MODULE 2
Credits: 0.8
Language: English
Teacher: Alessandro Mantovani
INTERMEDIATE LEVEL STATISTICS
Credits: 2.5
Language: Inglese
Teacher: Margherita Brondino
INTERMEDIATE LEVEL STATISTICS (2025/2026)
Academic staff
Referent
Credits
2.5
Language
Inglese
Class attendance
Free Choice
Location
VERONA
Learning objectives
The course aims to provide participants with theoretical knowledge and practical skills in the field of advanced statistical methodologies applied to research in the humanities and social sciences. At the end of the course, participants will be able to:
1. Understand and apply ANOVA in different experimental setups, including: One-way ANOVA, Factorial designs, Repeated measures designs, Mixed designs
2. Know the theoretical foundations of meta-analysis and acquire operational skills related to: systematic literature review, data collection and organisation, database construction for analysis
3. Apply meta-analysis critically and consciously to studies and research in the social sciences and humanities.
4. Conduct factor analyses: Exploratory (EFA), to identify latent structures in the data; Confirmatory (CFA), to test predefined theoretical models
5. Analyse causal relationships between variables through techniques of: Path analysis, mediation analysis, moderation analysis
6. Interpret statistical results and integrate them into the theoretical and methodological reflection of the research.
Prerequisites and basic notions
In order to participate in the course, you must have passed the final examination of the basic statistics course.
Program
1. ANOVA: One-way ANOVA, factorial ANOVA, repeated-measures ANOVA and mixed-measures ANOVA
2. Introduction to meta-analysis, with focus on research in the humanities: Literature review, Data collection, Database construction
3. Application of meta-analysis in the social sciences and humanities
4. Factor Analysis: Exploratory Factor Analysis, Confirmatory Factor Analysis
5. Path Analysis: Moderation and Mediation Analysis
Bibliography
Didactic methods
The course will be conducted online. An interactive mode will be used with exercises using statistical software such as Jamovi.
Learning assessment procedures
Written exam that will require the execution and interpretation of 5 exercises
Assessment
To pass the test, you need to get a pass mark in at least 3 of the 5 exercises proposed.
Criteria for the composition of the final grade
There is no grade but a suitability requirement
