Tools and Issues in Data Collection and Analysis (Scuola Normale Superiore, PhD, 2014)

Tools and Issues in Data Collection and Analysis (Third part)

 April-June 2014, Palazzo Strozzi

 Dr. Federico Russo

 The third part covers the basic concepts of inferential statistical analysis and introduces the classical linear regression model. The recommended text for the third part (final set of nine encounters) is:

Alan Agresti and Barbara Finlay, Statistical Methods for the Social Sciences (3rd or 4th Edition), Pearson.

The more mathematically inclined students may find useful also the following book:

 Damodar N. Gujarati, Basic Econometrics with Applications, (4th o 5th Edition) McGraw−Hill.

 

23 April – 10-12:30 p.m. (SIENA)

Introduction to the third part

Statistical Methods are increasingly employed in Political Science to test hypotheses about social and political phenomena. The growing power offered by computers and simple statistical packages opened new analytical possibilities, but are no substitute for a firm understanding of the basic inferential techniques.

Slides

23 April – 2-4:30 p.m. (SIENA)

Probability distributions

Inferential statistical methods use sample statistics to make predictions about the values of some parameters of the population of interest. To understand how this is done it is essential to introduce the concept of probability and sampling distributions.

REQUIRED READING: Chapter 4. Probability Distribution (Agresti & Finaly, 4th edition)

Sildes

7 May – 10-12:30 p.m.

Estimation

Sample data can be used to form two types of estimator of parameters, a point estimate and an interval estimate. Both can be estimated for quantitative variable (means) and for qualitative variables (proportions).

REQUIRED READING: Chapter 5. Statistical Inference: Estimation (Agresti & Finaly, 4th edition)

Slides

14May – 3-5:30 p.m.

Significance test

Theories generate hypotheses. A common aim in many studies is to check whether the hypotheses generated by a theory are compatible with the empirically observed data. This can be done with two complementary approaches, the significance test and confidence interval approach.

REQUIRED READING: Chapter 6. Significance Tests (Agresti & Finaly, 4th edition)

Slides

21 May – 3-5:30 p.m.

Introduction to the two variable regression model

The two variable regression model studies whether an association exists between two quantitative variables, the strength and the form of that relationship.

REQUIRED READING: Chapter 9. Linear Regression and Correlation (Agresti & Finaly, 4th edition)

Slides

 26 May – 3-5:30 p.m.

When there are several Independent Variables: Multiple Regression

It is often necessary to go beyond bivariate analysis to study partial relationships between two variable controlling for other variables. The multiple regression model allows for that.

REQUIRED READING: Chapter 11. Multple Regression and Correlation (Agresti & Finaly, 4th edition)

Slides

 28 May – 3-5:30 p.m.

ANOVA models

Qualitative explanatory variables often play an important role in political theories. For quantitative response variables, ANOVA model is a way to compare the mean responses of several groups defined by the categories of the qualitative explanatory variable.

REQUIRED READING: Chapter 12. Comparing Groups (Agresti & Finaly, 4th edition)

 4 June – 3-5:30 p.m.

Slides

ANCOVA models

When there are both quantitative and qualitative explanatory variables regression and ANOVA must be combined.

REQUIRED READING: Chapter 13. Combining Regression an ANOVA (Agresti & Finaly, 4th edition)

Slides

11 June – 3-5:30 p.m.

Issues and tools in model building

Building a regression model involves various steps that are often neglected, such as checking regression assumptions and take remedial actions when some of them are not entirely satisfied.

REQUIRED READING: Chapter 14. Model Building with Multiple Regression (Agresti & Finaly, 4th edition)

 Slides

Scienza Politica A.A. 2013-2014 (Economia, Unisi)

60 ore, 8 crediti

Inizio delle lezioni: Martedì 4 Marzo 2014

Orario lezioni: Martedì 10-12 (Aula 5) Giovedì 10-12 (Aula 10) Giovedì 16-18 (Aula 10)

Obiettivi

Il corso si propone di presentare agli studenti i fondamenti metodologici della scienza politica e di esplorare le principali tematiche sostantive che ne delineano il campo di indagine. Nella prima parte il corso fornirà una visione d’insieme delle principali forme di regime politico, per poi approfondire gli attori e le istituzioni principali delle democrazie contemporanee. La seconda parte del corso, condotta anche in forma seminariale, illustra i principali strumenti di analisi comparata dei sistemi politici e guida gli studenti nella loro applicazione pratica.

Prerequisiti

Il corso non presuppone conoscenze pregresse ma richiede una partecipazione attiva dello studente e interesse per i fenomeni politici contemporanei.

Contenuti

Il corso affronterà i seguenti argomenti:

– La politica, le democrazie, i regimi non democratici, democratizzazione e crisi democratica, i movimenti sociali, i gruppi di interesse, i partiti, i sistemi elettorali, i parlamenti, i governi, le politiche pubbliche, il metodo della comparazione per lo studio dei fenomeni politici, istituzioni, partiti politici e rendimento economico delle democrazie.

Testi di riferimento

-Cotta, M., Della Porta, D., Morlino, L. 2004. Fondamenti di scienza politica. Il Mulino.

-Dispense fornite dal docente sul sito web del corso.

-In alternativa alle dispense gli studenti potranno studiare il seguente testo: Lijphart, A. 2001. Le democrazie contemporanee. Il Mulino.

Modalità di svolgimento delle prove di esame

Prova finale: La prova finale è composta da una parte scritta, con domande a risposta aperta e multipla, e da una prova orale.

Registrati al corso per ricevere materiali e aggiornamenti: clicca qui.

Materiali didattici e approfondimenti

Intervista a Francis Fukuyama (2011)
Lettura: “Political Science for What?”
Lettura: “Il crollo dei regimi non democratici”
Video: “Nascita di una dittatura”
Lettura: “Social Capital
Video:
Video:

 

Introduzione al corso
Scienza politica
Democrazie
Autoritarismi
Mutamenti di regime
Movimenti
Gruppi
Partiti

ESEMPIO ESAME SCRITTO

CALENDARIO LEZIONI E LETTURE DI APPROFONDIMENTO

Introduction to Data Analysis (Siena Ph.D. 2013-2014)

Introduction to Data Analysis
Fall semester 2013-2014

Teacher: Federico Russo (federico.russo@unisi.it)

Course Description:
This course provides an in-depth introduction to ordinary least squares (OLS) regression analysis and its use in political science. This course represents an important first step towards being able to interpret, criticize, and conduct regression analysis of political data. Much of the course will focus on the assumptions underlying OLS regression, the implications of violations of these assumptions, and solutions to these violations.
At the end of this course, students should:
1. Have a thorough understanding of the assumptions, limitations, extensions, and applications of regression analysis in political science.
2. Being able to read and critically assess political science articles that make use of regression analysis.
3. Use the tools of regression analysis to test hypotheses and appropriately model political phenomena.

Required text:
Damodar N. Gujarati, Basic Econometrics with Applications, 4th o 5th edition.

Tentative schedule

the content of the lectures may change depending on how the class react to the course. For instance, we can consider covering more advance topics such as models for qualitative or count dependent variable.

  • Week 1 16/10/2013 Regression analysis: a review of the two variables model

Required readings: Chapter 1 and 2

  • Week 2 23/10/2013 Estimation

Required readings: Chapter 3 and 4

  • Week 3 30/10/2013 Hypotheses Testing

Required readings: Chapter 5

  • Week 4 06/11/2013 Model fit, predicted values, and residuals

Required readings: Chapter 7, Sections 7.5-7.8

  • Week 5 13/11/2013 An introduction to Multiple regression

Required readings: Chapter 8

  • Week 6 20/11/2013 Dummy variables

Required readings: Chapter 9

  • Week 7 27/11/2013 Interaction models

Required readings: Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. “Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis 14(1):63-82.

  • Week 8 04/12/2013 Multicollinearity and Heteroscedasticity

Required readings: Chapter 10 and 11

  • Week 9 11/12/2013 Autocorrelation and Specification Bias

Required readings: Chapter 12 and 13

  • Week 10 18/12/2013 Discussion of students’ final research papers.

Evaluation

Your grade will be determined through the following means:
1. Small homework assignments that require you to apply the concepts and tools learned in class to real political data. These assignments will constitute 50% of your grade for the course.

2. A final research paper in which you identify an interesting research question, provide a theoretical answer to the question you pose, test your theoretical expectations by utilizing OLS regression to analyze relevant data (while demonstrating sensitivity to the assumptions of OLS), and interpret the results. Your paper will represent 50% of your course grade.