Statistics, Economics, and Social Sciences

Educational objectives

Learning goals
Knowledge and comprehension of the basic concepts and techniques of linear algebra and of analytic geometry of the plane and the space and ability to apply them to the study and resolution of simple problems also in the context of other courses.

Knowledge and understanding.
Good theoretical and practical knowledge of matrices, linear systems and other fundamental notions of linear algebra and ability to understand these issues also in the context of other courses.

Applying knowledge and understanding.
Ability to use the acquired skills for solving simple problems on matrices, linear systems and other fundamental notions of linear algebra, also for their use required in other courses.

Making judgements.
Good ability to recognize, frame and set out the resolution of simple problems on matrices, linear systems and other fundamental notions of linear algebra, possibly selecting appropriately among the methods learned.

Communication skills.
Good presentation skills of basic concepts and techniques of linear algebra as well as solution methods to simple problems.

Learning skills.
Good learning ability of mathematical issues in other courses, by virtue of the comprehension of the logical-deductive character of the discipline.

Educational objectives

Learning goals
The learning goal of the Laboratory is the knowledge of the main functions and tools of Excel, with
particular attention to functionalities useful in the field of empirical social research. The aim is to
provide students with knowledge that enables them to work independently with Excel, becoming
familiar with the software interface and syntax, and acquiring the skills necessary to perform
research operations such as:
- Data storage and construction of a new matrix;
- Management of databases and differently constructed data matrices;
- Data cleaning and pre-processing of different type of information;
- Recoding of variables;
- Sampling procedures involving simple random extraction of cases;
- Calculation of descriptive statistics of cardinal variables;
- Mono and bivariate data analysis using specific functions and tools;
- Production of graphs and tables.
Students will acquire theoretical and methodological knowledge by implement what was explained
by the teacher, thanks to the alternation of lectures, practical activities (individual work and
teamwork) and moments of discussion in the classroom.

Knowledge and understanding
Students will face in practice the main phases of data cleaning, pre-processing, and statistical-
descriptive analysis. At the end of the laboratory, students will have learned the functions and tools
of Excel that allow them to manage databases and data matrices, perform recoding operations of
variables, pre-process unstructured textual data, perform mono and bivariate data analysis, as well
as develop graphical representations.

Applying knowledge and understanding
Through practical experience, students will learn: how to store and organize information in a matrix
built from scratch; how to handle and manage databases obtained through platforms for building
and compiling online questionnaires or exported from other statistical analysis software; how to
choose the most suitable tools and procedures to carry out specific data cleaning, processing, and
analysis operations; how to present the results of data analysis through the production of graphs and
tables.

Making judgements
Revisiting the various methodological phases of data processing and analysis, students acquire
judgment, decision-making and problem-solving skills thanks to an experience of cooperative
learning, which encourages constant discussion among peers and with the teacher.
Communication skills
Participation in group work and discussion in the classroom of the practical results obtained at the
end of each practical session enhances students' communication skills. In particular, these activities
allow improving communication strategies in peer-to-peer discussions and offer the opportunity to
practice public speaking.

Learning skills
The applied research activity allows students to broaden the theoretical knowledge already acquired
and to strengthen the theoretical and practical learning capacity of advanced approaches, methods
and techniques for analyzing social phenomena.

Educational objectives

Learning goals
The primary educational objective of the course is students' learning of the main applied aspects related to probability.
Knowledge and understanding
At the end of the course the students know and understand the main methods useful to solve the problems linked to the uncertainty.

Applying knowledge and understanding
At the end of the course students are able to formalize problems related to uncertainty in terms of probabilistic problems and to apply the specific methods of the probability to solve them. They are also able to model real phenomena through remarkable probabilistic structures.

Making judgements
Students develop critical skills through the application of theory to a wide range of probabilistic models. They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different methodological aspects.

Communication skills
Students, through the study and the practical exercises, acquire the technical-scientific language of the probability, which must be properly used in the oral test.

Learning skills
Students who pass the exam have learned the basic concepts of probability that allow them to deal with subsequent statistical area teaching (in particular the teaching of Statistical Inference).

Educational objectives

1. Knowledge and understanding: what students should know on the course topics after having passed the exam
After passing the assessment, students have got useful knowledge for an easier entry in to the labour market. They are introduced to team building, problem solving, decision making, public speaking and communication techniques, as well as to the use of professional tools to realize valuable presentations and visualization of statistical data and reports (LaTeX and Beamer), to have a colloquium, to draft a CV, etc… Moreover, students gain some insights on topics of specific interest for the labour market.

2. Applying knowledge and understanding: what students should be able to do after having passed the exam
After passing the assessment, students are able to effectively interact with future colleagues, to solve possible conflicts, to manage his/her own work and that of other collaborators, to address problems and take decisions, to present results in a captivating style.

3. Making judgements: activities through which critical faculties should be developed.
Critical capabilities are expected to be developed engaging students in traineeships attended in public research institutions, or private companies linked to Sapienza with official agreements as well as offering them opportunities to attend specific seminar cycles. Actually, the teaching method aims at encouraging all students, individually or in group, to observe, to analyse, to critically comment, to interpret, and share ideas, in order to get through decision making, and problem solving about specific data analysis issues posed by the lecturer.

4. Communications skills and activities through which the ability to communicate what was learned is developed.
The ability to communicate is developed through individual and team presentations, according to the different type of activity chosen by the student/s.

5. Learning skills: ability to continue studying the topics.
The competences acquired should contribute to both strengthen the opportunities for students to entry the labour market, and improve their cross-sectional competencies and skills that are increasingly demanded by public research institutions and private companies.

Educational objectives

Learning goals
The primary educational objective of teaching is to show students how to apply the notions of descriptive and inferential Statistics to the specific issues of society. Furthermore, students must be able to manage a data base, deriving from a questionnaire survey, or from a secondary analysis on indicators, produced by an official statistical source, at national and international level.
Knowledge and understanding
After attending the course, the students will be able to understand the meaning and the role of the main statistical tests and the main strategies for the synthesis of univariate, bivariate and multivariate data.
Applying knowledge and understanding
At the end of the course the students will be able to apply the above tests and synthesis strategies on real data bases, taken as case studies.
Making judgements
Students will develop critical skills by comparing the results obtained with the application of different methods on the same real data bases. They will learn to contextualise the results obtained in the various social, economic and cultural spheres.
Communication skills
The students, through the study and the carrying out of practical exercises, acquire the technical-scientific language of the discipline, which will be opportunely used both in the exercises during the work, also in groups, and in the presentation of results of autonomous elaborations, both in the final oral exam.
Learning skills
Students who pass the exam learned a method of analysis that will allow them to understand the opportunities and limitations of the statistical approach to social, economic and cultural problems.

Educational objectives

GENERAL AIMS
The course aims to provide the student with a methodological framework for the quantitative assessment of cognitive, general and specific skills, and of the personality traits, both normal and pathological, through psychological tests, as well as deepening the statistical methods underlying the construction of these tests and the verification of their psychometric properties (reliability and validity). The lectures therefore have the purpose of integrating the conceptual aspects of psychometry (reliability, validity) with the psychology theories of intelligence and personality. The goal is to prepare the student to apply the methods of data analysis necessary to evaluate the psychometric properties of a test, and to learn how to choose a test, administer it correctly and interpret the scores obtained.

SPECIFIC AIMS

Knowledge and understanding
The student must demonstrate that he/she has acquired the basic knowledge and skills related to the construction, use and interpretation of tests in the profession and in psychological research.

Applying knowledge and understanding
The student must be able to interpret the data analysis necessary to demonstrate the reliability and validity of the tests. Moreover, under the supervision of a professional psychologist, he / she should be able to: consciously use a wide range of psycho-metric tools for psychological assessment; to know how to choose to administer, to interpret the main psychological tests for the evaluation of personality and intelligence.

Making judgements
The student must be able to assess critically and autonomously how to use different psychological tests, how to deal with ethical and deontological issues related to psychology evaluation, how to interpret the test results in a mindful way.

Communication skills
The student will be able to elaborate written materials and oral presentations able to communicate the knowledge (for example nature and use of psychological tests) to specialists and non-specialist stakeholders.

Learning skills
Through the lectures the student will acquire learning skills that can be spent in the specific context of psychometrics applied in different disciplinary fields, and in the more general autonomy in the reading of advanced scientific texts, which will be addressed during the studies, and in particular in the preparation of the "laurea" degree dissertation, as well as necessary to address the master's degree courses.

Educational objectives

The main objective of the course is s to offer the knowledge and skills useful for carrying out projects, research and evaluations in the field of public policies with a focus on social innovation, in a pluralist and democratic perspective of evaluation.
The student will acquire the ability to carry out functions of planning, coordination and implementation of evaluation research in the sectors of social innovation, sustainability and gender policies.

The course includes the following specific training objectives:

1) knowledge of the main design and evaluation approaches for social innovation and territorial development in terms of their application in terms of empirical research.

2) ability to design, coordinate and apply design and evaluation methodologies in the social innovation sector.

3) development of soft skills: problem solving, critical analysis and evaluation of public policies will be solicited in the theoretical lessons and in the workshop. A part of the theoretical lessons will be dedicated to illustration and discussion in class with students of projects for social innovation and evaluation surveys in order to stimulate students' meta-evaluation skills. In the laboratory, students will be divided into working groups for carrying out exercises on data design and analysis in class, in order to produce a final project work that will be evaluated with the peer evaluation methodology.

4) ability to correctly communicate the results of an evaluative research for the purpose of scientific dissemination and the usability of evaluation evidence in the field of design for social innovation. In the exercises and in the laboratory, students will be involved in activities of presentation and discussion in class of the results of the projects developed in the group work.

5) during the lectures, the participation of the students will be solicited, with the objective of ongoing assessment of learning, as well as for the accompaniment to the laboratory activities.
Specifically, the workshop part will allow the acquisition of practical skills that orientate to the planning an evaluative research focused on the social innovation.

Expected learning outcomes: at the end of the course, students will be able to carry out social innovation projects and empirical evaluative surveys using the different approaches to evaluation.
The students will acquire skills in the construction and application of evaluative research designs in the following phases: definition of the evaluative object, definition of the evaluation mandate with the client, selection of the evaluative approach to the context of the analysis, construction and selection of evaluative questions, design evaluation techniques and data collection, analysis of evaluative data with the support of the main software for empirical research.

Educational objectives

The course develops a systematic understanding of the key areas of international economics: trade, migration and international monetary economics, and their impact on each other. Theory will be applied to events, problems and trends in the international economy. Since international economics is dynamic in nature and influenced by real-world developments in the economic, political and financial spheres, the course coverage will be updated periodically to include recent developments/conditions in the real-world environment.

(a) knowledge and understanding;

After taking the course, students will be able to understand and define the main concepts, models and patterns of analysis of the three key areas of international economics: trade, migration and international monetary economics.

(b) Applying knowledge and understanding;

By the end of the course, students will be able to formalize real economic problems and apply discipline-specific methods to analyze them in detail. Students will acquire a theoretical background and, through the analysis of practical cases, the ability to critically study economic policies and models in the context of international trade.

(c) Making judgments

Students will increase not only their theoretical skills but also their critical curiosity in reading economic phenomena and economic models from an open economics perspective.

(d) Communication skills

Students/youth, through class discussions and exercises, will acquire tools for critical analysis and communication skills. They will acquire the technical and scientific language of the discipline; they will also learn how to structure and present a research report.

(e) Learning skills

Students who pass the exam will have learned notions, definitions, patterns of analysis and methods of analysis that will enable them to take other courses in economics and social sciences.

Educational objectives

The aim is to provide students with the interpretive spatial means functional to understanding the actions of the entities operating in the political and economic fields. They are necessary to grasp the complexity and the interrelations between the territory’s different geopolitical and geo-economic phenomena. The theoretical framework will be applied to actual instances of geopolitical instability on different levels of analysis.

Knowledge of basic notions in geography, as well as in the most recent developments in geopolitically relevant international matters, is required. In this regard, it is highly recommended that students prepare for the exam by making use not only of up to date atlases but also of specialized magazines. Visiting authoritative websites which focus on geopolitical themes is also advised.

Educational objectives

Learning goals
The main educational objective of the course is the learning of the linear model analysis in its theoretical, methodological and applicative aspects.
Students must master language and the principles of statistical analysis in the experimental field.

Knowledge and understanding.
After having attended the course the students know and know how to apply the methods of analysis of the Linear Model, in the various experimental, observational and quasi-experimental situations.

Applying knowledge and understanding.
At the end of the course the students are able to identify which types of situations can be analyzed with the linear model tools, and to formalize them in terms of parametric statistical models.
They are also able to formulate substantive questions in parametric terms, in different situations, and answer to these questions with the tools of statistical analysis.

Making judgements.
Students develop critical skills through the application of inferential methodologies to a wide range of situations that can be represented in the linear model family.
They also develop the critical sense through the selection, estimation and validation procedure of the statistical model in different situations related to real data.

Communication skills.
Particular attention is paid to the technical-scientific language of the discipline, which must be used correctly in the final test.

Learning skills.
Students who pass the exam have acquired the fundamentals of the parametric models that allow them to face the study of more complex models.

Educational objectives

Learning goals.
An introduction to the basic skills of empirical economic analysis

Knowledge and understanding.
After taking the course the students know and understand the main problems in measuring economic variables and the methods to be used to solve them.

Applying knowledge and understanding.
After taking the course the students know how to solve the main problems in measuring economic variables.

Making judgements.
Students develop their critical skills through the analysis of real datasets.

Communication skills.
Students acquire the technical language, which must be used both in the written exam and in the individual project.

Learning skills.
Students passing the exam have acquired the ability to read and realise basic empirical economic studies.

Educational objectives

Learning goals.
The aim of the lectures is to provide an exhaustive discussion of the main topics concerning the linear model (OLS, MLE, IV, asymptotic theory and inference) for cross-section analysis and a brief introduction to the analysis of discrete data.
Students must understand the analytical problems of these methods and be able to apply them to concrete situations.

Knowledge and understanding.
After attending the course the students know and understand the main problems related to the linear regression model (for example: absence of exogeneity) and the main methods to be used to solve such problems (for example: IV estimator).

Applying knowledge and understanding.
At the end of the course the students are able to formalize real problems in terms of linear regression models and to apply the methods specific to the discipline to solve them.
They are also able to apply the methods to concrete situations and to interpret the results.

Making judgements.
Students develop a knowledge of the analytical properties of the presented methodologies and the ability to build programs for their implementation.
They also learn to critically interpret the results obtained by applying the procedures to concrete situations.

Communication skills.
Students acquire the technical-scientific language of the discipline, which it must be used appropriately in both the intermediate and final written tests and in the oral tests.
Communication skills are also developed through group activities.

Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle the study of analytical properties in more complex modeling contexts in subsequent quantitative area teachings.

Educational objectives

Learning goals.
The main learning goal of the Laboratory concerns the definition and the development of the whole process of a social research from its design and planning up to the field-work, the data analysis and their representation in a statistical report.
The lessons and the practical activities of the teaching programme are well balanced in order to enable students to apply the theoretical concepts learned during the lessons.
Knowledge and understanding.
At the end of the Laboratory students know all the phases of the process design and planning as well as the correspondent specific activities.
Students learn the main techniques
a) to conceptualize research problems;
b) to build up data collection tools (paper and electronic questionnaires);
c) to gather data.
Moreover, students learn how to interpret and comment survey data, reporting the contents in an effective way. Applying knowledge and understanding.
The practical experience of applied research enable students to formulate research questions, objectives and hypothesis; to identify the most appropriate data collection tools; to build up a data collection tool (paper and electronic questionnaire); to monitor the data collection activity; to analyse and interpret data.
Students experience the redaction of a report summarizing the main results of the survey they have conducted.
Making judgements.
The students working in group are constantly encouraged to discuss and share ideas, in order to acquire capacity of decision and problem solving with respect to the choices related to the realization of a social research. Moreover, the students improve their ability to interpret data.
Communication skills.
The working group and the presentation of the results of the tasks assigned by the lecturer contribute to the development of communication skills.
The presentation and discussion of the final report contribute to the acquisition of the specific scientific technical language of the discipline.
Learning skills.
The practical experience of research of the Laboratory enable students to strengthen their theoretical knowledge and to improve the capacity to learn more advanced methods and techniques of survey management.

Educational objectives

Learning goals.
The main goal of this lab course is the acquisition of the logic and the fundamentals of the statistical package SAS for the analysis of real data.
Moreover, at the end of the course students should be able to formalize simple questions on real practical problems using standard statistical tools.
The focus is also on the theoretical framework together with the computational details related to real applications, giving special attention to the interpretation of the results from the statistical software output. Knowledge and understanding.
When completing the course, students will have learnt the logic and basics of SAS programming to import and manipulate data, perform standard statistical analyses on real data.
Moreover, they will have learnt the basics of empirical checking of statistical laws and inferential theoretical properties by simulation.
Applying knowledge and understanding.
When completing the course, students will be able to formalize a selected statistical problem, make standard statistical analyses in autonomy, interpret and explain the results.
Moreover, they will be able to carry on simple simulations.
Making judgements.
Students develop the critical thinking by applying standard methodologies learnt in their curricula which they are able to use in autonomy by means of a statistical software.
The skill to process data and produce the output by themselves helps to learn how to interpret the results taking into considerations theoretical criteria.
Communication skills.
By processing data and interpreting the results, students will learn the correct use of the technical language which is required in both coursework and final exam.
Special attention is given to the skill of communicating results to non-specialists by using a rigorous but understandable language.
Learning skills Students passing the exam have learnt how to perform standard statistical analyses in autonomy with SAS and the logic to study and apply different methodologies in other applicative cases which are the premises to continue their studies in a Second Cycle Degree in either Statistics or Applied Statistics.