|
|
Applied multivariate statistical analysis (second term 2016/17)
Structure of the course
Course aims and objectives
The objective of the course is to familiarize students with the underlying assumptions of the main statistical techniques for data analysis used in political science so that they will be able to evaluate and undertake quantitative research by their own. A great emphasis will be placed on the formulation of hypotheses and on the use of data to test hypotheses. The first part of the course is devoted to test non-linear models with Ordinary Least Squares (OLS). In the second part we will introduce some more advanced techniques for quantitative analysis (fixed/random models and probit/logit). Lectures are coordinated with computer lab instruction in data analysis. Students will also learn how to use the statistical software STATA to organize and analyze data.
Course prerequisites
The students must be familiar with the basic concepts of descriptive and inferential statistics (levels of measurement, probability, hypothesis testing, confidence interval).
Required reading
Stock J.H and M. W. Watson (2003), Introduction to Econometrics, Boston: Addison Wesley
Lectures
Ten lectures will take place
Examination
Course grades will be based on three assignments.
The objective of the course is to familiarize students with the underlying assumptions of the main statistical techniques for data analysis used in political science so that they will be able to evaluate and undertake quantitative research by their own. A great emphasis will be placed on the formulation of hypotheses and on the use of data to test hypotheses. The first part of the course is devoted to test non-linear models with Ordinary Least Squares (OLS). In the second part we will introduce some more advanced techniques for quantitative analysis (fixed/random models and probit/logit). Lectures are coordinated with computer lab instruction in data analysis. Students will also learn how to use the statistical software STATA to organize and analyze data.
Course prerequisites
The students must be familiar with the basic concepts of descriptive and inferential statistics (levels of measurement, probability, hypothesis testing, confidence interval).
Required reading
Stock J.H and M. W. Watson (2003), Introduction to Econometrics, Boston: Addison Wesley
Lectures
Ten lectures will take place
Examination
Course grades will be based on three assignments.
First theme: Interaction Models
Notes on Interaction Models with OLS
Dataset 1 (NES 2004)
Replication do-file
Second theme: Margins Command
Notes on Margins
Dataset 1 (Lijphart)
Dataset 2 (NES 2004)
Dataset 3 (Caschool)
Dataset 4 (SWD macro)
Replication do-file
First assignment (due the 20th of March)
Third theme: Fixed and Random Linear Models
Notes on issue of Independence
Dataset 1 (Consumption)
Dataset 2 (SWD micro)
Dataset 3 (Smoking)
Dataset 4 (Happiness)
Dataset 5 (Happiness for Assignment; do file for the variables)
Replication do-file
Second assignment (due the 27th of March)
Fourth theme: Regression with a Binary Dependent Variable
Notes on Probit and Logit
Dataset 1 (School)
Dataset 2 (NES 2004)
Dataset 3 (Itanes 2006)
Replication do-file
Fifth theme: Regression with a Binary Dependent Variable. Diagnostic
Notes on Probit and Logit: diagnostic
Dataset 1 (School)
Dataset 2 (NES 2004)
Dataset 3 (pre-electoral coalitions)
Dataset 4 (Itanes 2006)
Replication do-file
Third assignment (due the 6th of April)
Appendix: distributions
Critical values for the chi squared distribution
Cumulative Standard Normal Distribution Function
Notes on Interaction Models with OLS
Dataset 1 (NES 2004)
Replication do-file
Second theme: Margins Command
Notes on Margins
Dataset 1 (Lijphart)
Dataset 2 (NES 2004)
Dataset 3 (Caschool)
Dataset 4 (SWD macro)
Replication do-file
First assignment (due the 20th of March)
Third theme: Fixed and Random Linear Models
Notes on issue of Independence
Dataset 1 (Consumption)
Dataset 2 (SWD micro)
Dataset 3 (Smoking)
Dataset 4 (Happiness)
Dataset 5 (Happiness for Assignment; do file for the variables)
Replication do-file
Second assignment (due the 27th of March)
Fourth theme: Regression with a Binary Dependent Variable
Notes on Probit and Logit
Dataset 1 (School)
Dataset 2 (NES 2004)
Dataset 3 (Itanes 2006)
Replication do-file
Fifth theme: Regression with a Binary Dependent Variable. Diagnostic
Notes on Probit and Logit: diagnostic
Dataset 1 (School)
Dataset 2 (NES 2004)
Dataset 3 (pre-electoral coalitions)
Dataset 4 (Itanes 2006)
Replication do-file
Third assignment (due the 6th of April)
Appendix: distributions
Critical values for the chi squared distribution
Cumulative Standard Normal Distribution Function