Course teached as: B019225 - METODI STATISTICI PER LA RICERCA SOCIALE Second Cycle Degree in SOCIOLOGY AND SOCIAL RESEARCH
Teaching Language
Italian
Course Content
Review of statistical inference methods; Comparison of two groups; Analyzing
association between categorical variables; Correlation; Linear regression; Introduction to multivariate relationships; Multiple regression;
Analysis of variance (ANOVA) methods; Analysis of co-variance (ANCOVA) methods;
Model Building with multiple regression and Regression diagnostics; Logistic regression models for binary response variables; Introduction to causal inference and evaluation methods.
Agresti Alan, Finlay Barbara. (2015) Statistical Methods for the Social
Sciences (4th edition) Pearson Prentice Hall.
Learning Objectives
Students will develop expertise to use and interpret statistical models for continuous and binary response variables and the ability to employ and interpret appropriate statistical methods for causal analyses.
Prerequisites
Descriptive statistics and inferential statistics
Teaching Methods
Lectures
Further information
Not available
Type of Assessment
Written exame
Course program
Review of statistical inference methods: Variables and their measurement; Statistics and sampling distributions; Point estimation; Interval estimation; Statistical hypothesis tests.
Comparison of two Groups: Introduction; Comparing two proportions; Comparing two means; Comparing means with dependent samples.
Analyzing Association Between Categorical Variables: Joint, marginal and conditional frequency distributions; Statistical dependence and independence; Chi-squared test of independence; Residuals; Association measures (difference between two proportions; relative risk, odds ratio).
Correlation: Linear dependence; Covariance; Linear correlation coefficient; Inference for the linear correlation coefficient.
Linear Regression: Model assumptions; Interpretation of the model parameters; Least square estimation of the regression parameters; Fitted values and residuals; Interpolation and extrapolation; Decomposition of the sum of squares; R-squared; Inference in the regression model
(tests and confidence intervals for the slope; confidence intervals for expecteed values and predicted values).
Introduction to multivariate relationships.
Multiple Regression: Model assumptions; Interpretation of the model parameters; Fitted values and residuals; R-squared and
Multiple correlation; Inference in multiple regression models (tests and confidence intervals for multiple regression coefficients); Interaction between predictors in their effects; Comparing regression models.
Analysis of Variance (ANOVA): Comparing several means: One-way analysis of variance and F-test; Multiple comparisons of means; Performing ANOVA by regression modeling; Two-way analysis of variance;
Comparing regression models.
Analysis of Co-Variance (ANCOVA): Regression models with quantitative and categorical predictors; Interaction between quantitative and categorical predictors; Inference for regression with quantitative and categorical predictors; Comparing regression models.
Introduction to model selection procedures; Regression diagnostics; Multicollinearity.
Logistic Regression for binary response variables: Introduction to generalized linear models; Logistic regression; Multiple logistic regression; Inference for logistic regression models; Comparing logistic regression models.
Causal inference and evaluation methods: Introduction to the potential outcome appraoch (definition of the primitive concepts and of the assignment mechanism); Randomized experiments; Different modes of inference (assignment-based modes of inference and model-based inference); Design and analysis of observational causal studies.