and
second level university master
Academic Year 2013/14
QoLexity
Measuring, Monitoring and Analysis of Quality of Life and its
Complexity
in cooperation with
Eurostat
DIDACTIC ORGANIZATION
A.Concepts and tools
In Florence (except when indicated)
Total: 7 credits – 42 hours
A.1 Towards a common language: general concepts and their meanings
Alberto Peruzzi – Filosofia Teoretica M-FIL/01
city
3 credits (18 hours)
A.1.1
Complexity
Alberto Peruzzi
2
FI
A.1.2
Quality and quantity
Alberto Peruzzi
2
FI
A.1.3
Multidimensionality
Alberto Peruzzi
2
FI
A.1.4
Explanation and forecasting (I)
Alberto Peruzzi
2
FI
A.1.5
Explanation and forecasting (II)
Alberto Peruzzi
2
FI
A.1.6
Stability and change
Alberto Peruzzi
2
FI
A.1.7
Progress and growth
Alberto Peruzzi
2
FI
A.1.8
Subjective and inter-subjective experience: different levels and dimensions (I)
Rocco Sacconaghi
2
FI
A.1.9
Subjective and inter-subjective experience: different levels and dimensions (II)
Rocco Sacconaghi
2
FI
A.2 Quality of life and related concepts
Giampaolo Nuvolati – Sociologia dell’Ambiente e del territorio SPS/10
city
3 credits (18 hours)
Quality of life and its definition
A.2.1
Quality of Life: review of the main concepts and domains
Giampaolo Nuvolati
2
FI
A.2.2
Measures of wellbeing: historical review
Tommaso Rondinella
2
FI
A.2.3
Quality of life and sustainability: introduction to basic concepts
Fabiola Riccardini
2
FI
A.2.4
Quality of life and sustainability: methods and measures
Fabiola Riccardini
2
FI
A.2.5
Quality of life and sustainability: empirical evidences
Fabiola Riccardini
2
FI
A.2.6
Quality of life and its relationship with equity, polarization, inequality
Marco Fattore
2
FI
A.2.7
Quality of life and urban development
Giampaolo Nuvolati
2
FI
A.2.8
Statistical knowledge and policy decision
Enrico Giovannini
4
FI§
Additional lessons
A.2.9
Quality of life and the capabilities approach
Mario Biggeri
2
FI
A.2.10
Wellbeing and identity
Anna Loretoni
2
FI
§ Aula Magna - Polo Didattico "La Crocetta" - Università di Firenze (via Laura 48)
A.3 Get prepared: acquiring technical tools
Daniele Vignoli – Demografia SECS-S/04
city
1 credit (6 hours)
A.3.1
Using Statistical packages: introduction to STATA
Daniele Vignoli (UNIFI)
2
FI
A.3.2
Using R: introduction
Emanuela Dreassi (UNIFI)
2
FI
A.3.3
Using R: spatial statistics
Emanuela Dreassi(UNIFI)
2
FI
B. Data
In Rome (except as indicated)
Total: 12 credits – 72 hours
B.1 Quality-of-life data1
Linda Laura Sabbadini – Statistica Sociale SECS-S/05
city
6 credits (36 hours)
General issues
B.1.1
Social changes and official statistics changes
Linda Laura Sabbadini
4
FI
B.1.2
Social monitoring and reporting
Heinz-Herbert Noll
4
FI
B.1.3
GDP & beyond: the European strategy (I)
Marleen De Smedt
2
RM
B.1.4
GDP & beyond: the European strategy (II)
Donatella Fazio
2
RM
B.1.5
Social statistics for social policies
Chiara Saraceno
4
RM
Focus on particular measuring domains
B.1.7
Demographic trends
Valerio Terra Abrami
2
RM
B.1.8
Education and culture
Maria Pia Sorvillo
2
RM
B.1.9
Family and social networks – Social participation
Romina Fraboni
2
RM
B.1.10
Life styles
Lidia Gargiulo
2
RM
B.1.11
A gender approach to data analysis
Sara Demofonti
2
RM
B.1.12
Crime
Maria Giuseppina Muratore
2
RM
B.1.13
Measuring time use
Tania Cappadozzi
2
RM
B.1.14
Work
Federica Pintaldi
2
RM
B.1.15
Environment and landscape
Angela Ferruzza
2
RM
1
This module aims at exploring (i) the role of official statistics in following social changes, and (ii) the possibility to build a narrative concerning social change through data. In
particular, it will be described how observation of social phenomena through official statistics develops according to the evolution of social themes.
B.1.16
Alessandra Ferrara
Environment and landscape
2
RM
B.2 Data collection2
Linda Laura Sabbadini – Statistica Sociale SECS-S/05
city
6 credits (36 hours)
General issues
B.2.1
Designing subjective questions: international experiences
Adolfo Morrone
2
RM
B.2.2
Data quality and observation errors
Marina Signore
2
RM
B.2.3
The art of designing a questionnaire. I
Sante Orsini
2
RM
B.2.4
The art of designing a questionnaire. II
Silvia Montecolle
2
RM
B.2.5
Designing a survey: the sample design
Claudio Ceccarelli
4
RM
B.2.6
Clelia Romano
Saverio Gazzelloni
4
RM
B.2.7
Designing a survey: the choice of data collection technique
The new frontiers of data collection: integration of administrative data, sample surveys, census I
2
RM
B.2.8
The new frontiers of data collection: integration of administrative data, sample surveys, census II
Giuseppe Garofalo
2
RM
Collecting data on particular social groups and topics
B.2.9
Integration of sources in measuring migrants’ quality-of-life I
Cinzia Conti
2
RM
B.2.10
Integration of sources in measuring migrants’ quality-of-life II
Monica Perez
2
RM
B.2.11
How to measure discrimination and stereotypes
Clelia Romano
2
RM
B.2.12
How to measure new emerging forms of households
Cristina Freguja
2
RM
B.2.13
Health-related quality of life
Viviana Egidi
2
RM
B.1.14
How to measure health and mortality
Roberta Crialesi
2
RM
2
This module aims at exploring the methodologies (survey design, questionnaire construction, data collection techniques) aimed at measuring social phenomena with particular
reference to official statistics.
B.1.15
How to measure income and poverty
Nicoletta Pannuzi
2
RM
B.1.16
How to measure homelessness
Cristina Freguja
2
RM
C. Analytical approaches
Total 15 credits – 90 hours
C.1 Indicators: from reality to reading the reality
Filomena Maggino – Statistica Sociale SECS-S/05
city
3 credits (18 hours)
How to make indicators a knowledge instrument
C.1.1
The conceptual and logical process in defining indicators
Filomena Maggino
2
FI
C.1.2
Choosing the future through indicators
Enrico Giovannini
4
FI
C.1.3
Systems of social indicators
Heinz-Herbert Noll
4
FI
C.1.4
How to relate them: composite or dashboard, synthesis and aggregation
Marco Fattore
2
FI
Applications
C.1.5
Fuzzy approach
Achille Lemmi
4
FI
C.1.6
Relational mapping
Marco Fattore
2
FI
C.2 Indicators: analytical tools and strategies
Filomena Maggino – Statistica Sociale SECS-S/05
city
3 credits (18 hours)
Aggregative approach: composite indicators
C.2.1
Constructing complex indicators: technical issues. I
Filomena Maggino
2
FI
C.2.2
Constructing complex indicators: technical issues: composite indicators
Matteo Mazziotta
4
FI
C.2.3
Constructing complex indicators: technical issues: counting approach
Marco Fattore
2
FI
Non aggregative approach: the case of ordinal data
C.2.4
Constructing complex indicators: the partial order approach
Marco Fattore
4
FI
C.2.5
Subjective indicators: specific issues
Filomena Maggino
2
FI
Matteo Mazziotta
2
FI
Assessing complex indicators
C.2.6
Sensitivity analysis
C.3 Quality-of-life data analysis: pathways to modelling
Alessandra Petrucci – Statistica SECS-S/01
city
3 credits (18 hours)
C.3.1
Reducing complexity of data: dimensional analysis
Filomena Maggino
2
FI
C.3.2
Geographical orientation of Quality of Life data
Alessandra Petrucci
2
FI
C.3.3
Narratives and data structure: the role of statistical information systems
2
FI
C.3.4
Investigating data structure: social mining (I)
Cristina Martelli
Dino Pedreschi
2
FI
C.3.5
Investigating data structure: social mining (II)
Fosca Giannotti
2
FI
C.3.6
From large survey data to subpopulations: small area estimation methods (I)
Monica Pratesi
2
FI
C.3.7
From large survey data to subpopulations: small area estimation methods (II)
Caterina Giusti
2
FI
C.3.8
From large survey data to subpopulations: small area estimation methods (III)
Monica Pratesi
4
FI
C.4 Quality-of-life data analysis: looking for explanations
Carla Rampichini – Statistica SECS-S/01
city
3 credits (18 hours)
C.4.1
Relating micro and macro quality of life aspects: the multilevel approach I
Carla Rampichini
4
FI
C.4.2
Relating micro and macro quality of life aspects: the multilevel approach II
Leonardo Grilli
2
FI
C.4.3
Approaches in analyzing change
Filomena Maggino
2
FI
C.4.4
Evaluating individual changes: analysis of panel quality of life data
Arnstein Aassve
4
FI
C.4.5
Measuring latent, manifest and longitudinal poverty and quality of life
Gianni Betti
4
FI
C.4.6
Life course analysis
Daniele Vignoli
2
FI
C.5 Making all concrete3
Daniele Vignoli – Demografia SECS-S/04
city
3 credits (18 hours)
C.5.1
Relationship between quality-of-life and demographic events
Letizia Mencarini
2
FI
C.5.2
Explaining life satisfaction across Europe
Daniele Vignoli
2
FI
C.5.3
Territorial representation of quality-of-life data
Marco Mauri
4
FI
C.5.4
Measuring wellbeing at local level: from individuals to communities
Michela Guerini
2
RM
C.5.5
Urban development and quality of life
Michela Guerini
2
RM
C.5.6
Measuring vulnerability
Adolfo Morrone
2
RM
C.5.7
Latent class approaches: the case of social exclusion
Elena Pirani
2
FI
C.5.8
Technology and quality of life
Katarzyna Wac
2
FI
3
Selected case studies as well as different and specific themes will be presented through a multidisciplinary approach and completed with lab-sections.
D. Representing and communicating
This module has been adopted as one of the activities managed by OECD in the ambit of the Web-COSI project
(http://www.webcosi.eu/)
In Florence (except as indicated)
8 credits – 48 hours
D.1: Representing quality-of-life data and results
Carlo Sorrentino – Sociologia dei Processi Culturali e Comunicativi SPS/08
city
3 credits (18 hours)
D.1.1
Representing the complexity: data visualization issues
Paolo Ciuccarelli
4
RM
D.1.2
Graphics as an instrument for telling stories. Dashboards
Stefano de Francisci
4
RM
D.1.3
Communication aspects of statistical graphics: how to use graphs to underline the message
Marco Trapani
2
FI
D.1.4
How to prepare a successful presentation
Marco Trapani
4
FI
D.1.5
How to lie with quality-of-life statistics
Marco Trapani
2
FI
D.1.6
Shiny
Stefano Barberis
2
FI
D.2 Communicating quality-of-life results
Carlo Sorrentino – Sociologia dei Processi Culturali e Comunicativi SPS/08
city
3 credits (18 hours)
D.2.1
Public communication: the case of quality of life results
Carlo Sorrentino
2
FI
D.2.2
Strategies in communicating information on QoL
Data, media and public opinion: problems and strategies
Donato Speroni
6
FI
D.2.3
Statistics and political communication: theories and methods
Silvano Cacciari
4
FI
D.2.4
How knowledge can be transferred into policy
Katherine Scrivens
2
FI
D.2.5
Communication and policy use of indicators
Donatella Fazio & Marina Signore
2
RM
D.2.6
Politics, policy and quality of democracy
Leonardo Morlino
2
FI
D.3 Putting all together
Daniele Vignoli – Demografia SECS-S/04
city
2 credits (6x2 = 12 hours)
D.3.1
Start up
QoLexity team
2
FI
D.3.2
Wiki training
Marco Trapani
2
FI
D.3.3
Selecting a dataset and defining an analytical project on the selected dataset
Daniele Vignoli
4
FI
D.3.4
Preparing a presentation of the results and delivering it in a public presentation (tutoring)
Marco Trapani
4
FI
Meeting
QoLexity team
4
FI
Presentation of the theses
QoLexity Committee
FI