DATA PRODUCTION AND ANALYSIS

Attività formativa monodisciplinare
Codice dell'attività formativa: 
110010-ENG

Scheda dell'insegnamento

Per studenti immatricolati al 1° anno a.a.: 
2019/2020
Insegnamento (nome in italiano): 
DATA PRODUCTION AND ANALYSIS
Insegnamento (nome in inglese): 
Data Production and Analysis
Tipo di attività formativa: 
Attività formativa Caratterizzante
Tipo di insegnamento: 
Obbligatoria
Settore disciplinare: 
STATISTICA ECONOMICA (SECS-S/03)
Anno di corso: 
1
Anno accademico di offerta: 
2019/2020
Crediti: 
9
Responsabile della didattica: 
Altri docenti: 

Altre informazioni sull'insegnamento

Modalità di erogazione: 
Didattica Convenzionale
Lingua: 
Inglese
Ciclo: 
Annualità Singola
Obbligo di frequenza: 
No
Ore di attività frontale: 
72
Ore di studio individuale: 
153
Ambito: 
Statistico-matematico
Materiali didattici: 
Prerequisites

None

Educational goals

The course “Data production and analysis” offers a solid background for professional activities in the field of data production, collection and analysis. It helps students in research developed in the social and economic fields, mostly within the fast-changing official data production system and in the framework of the modernization and coordination phases of official statistics producers. On the one hand, the content of this course is useful for professional activities characterized by a strong need of awareness on the production and usage of official statistics (even integrating available sources). On the other hand, it suggests the most common data collection methods that can be used in various field (economic and social data, satisfaction data, survey data in general). Ability to apply adequate methods at different steps of the data collection process (quality check, missing data, imputation, etc.) is achieved through both course lectures and laboratories. Ability to process, analyze and model the data is learned using one of the most widely spread statistical software: SAS.

The course has the following specific objectives:
- Help students becoming able to design and manage data production processes in specific fields.
- Teach students how to evaluate and enhance data quality (taking into account the definition of the main dimensions of quality, learning how to monitor and evaluate them and applying suitable methods).
- Introduce students to the analysis of big data, or of data that have a hierarchical structure (multilevel modelling).
- Guide students in using different types of data sources (censuses, cross section or longitudinal sample surveys, administrative sources, big data) and in critically evaluating pros and cons of such kind of sources (link with different production models, implications on the results quality).
- Introduce students to the business concepts applied to official statistics (e.g. Generic Statistical Business Process Model, data archiving, metadata management, statistical standard classification, editing and imputation).

The course is fully coherent with the education aims of the EMOS (European Master in Official Statistics) label, of which it is a milestone, as well as for the Master course in Economic and Data Analysis.

Course content

The course is organized in two modules.

Module 1 (first semester; teacher: Daniele Toninelli):

• The GSBPM (Generic Statistical Business Process Model): how an official statistics business model is organized and how it operates for data production.
• Role of metadata, in general and within the framework of the statistical standard classification system, in enhancing the quality standards.
• Data editing and imputation method in practice: fixing the most common issues with collected raw data.
• Introduction to the SAS statistical software: how to program and use it.
• Multilevel modelling: when it is useful, how to use this technique with SAS, how to interpret and use the main output.
• Challenges of cross-country collected data and of multinational data integration.

Module 2 (second semester; teacher: Annamaria Bianchi):

• The steps in the data production process, the decisions due at each step. How to be aware of interactions between the different steps, possible adaptive design approaches, usage of paradata and problems in integration of different sources. Understand how to manage big data; pros and cons for statistical purposes.
• Theoretical knowledge and ability to select appropriate sampling methods, select samples, apply weighting and nonresponse adjustment and provide data imputation. Sampling methods, non-response adjustment, weighting, imputation.
• Errors and risks of errors and quality indicators; TSE (total survey error) perspective.
• The conceptual framework as important part in updating, streamlining and aligning the standards and production associated with official statistics at both national and international level.
• Being aware of pros and cons and of methodology of mixing models in surveys.
• Microdata: how to use and analyze them.
• Big data and sentiment analysis.

Textbooks and reading lists

- “Handbook on Methodology of Modern Business Statistics”, CBS-Eurostat.

- “Big data and social sciences” ed. Foster et al., selected sections.

- Further and more detailed suggestions provided during the course and within the course slides.
- On the eLearning webpage of the course: lectures’ slides, seminars materials, assessments, news for students.

Teaching methods

Lectures (with active discussions solicited).

Experts of national and international agencies invited during the course for the discussion and presentation of specific topics.

Individual cases or personal research projects developed by students.

Assessment and Evaluation

The course exam will be organized in two different parts, corresponding to the topics of Module 1 and 2.
The final evaluation is the sample average of the two scores.

Each part evaluation can be based on:
- A theoretical written exam.
- Assessments provided by the professors, including individual case studies, reports and ppt presentations.
- Oral discussion about the case studies/research results and about the course topics.

Further information

Since Eurostat is providing every year new innovative teaching material for EMOS labeled masters, in order to obtain high quality and innovative educational standards (recognized at international levels), the teaching activity will be constantly updated.