84613 - METODI DI CAMPIONAMENTO E INDAGINI CAMPIONARIE

Learning outcomes

At the end of the course the student has skills on complex sampling plans. In particular, the student is able to: - design complex sample surveys - analyze and synthesize the information obtained in an advanced statistical perspective - identify and use the appropriate estimators for the inferential problem to be faced - evaluate and communicate the degree of uncertainty of the estimates obtained .

Course contents

MODULE 1  Prof. Trivisano

This course provide a practical introduction to the sampling issues involved in designing and analysing sample surveys. The main learning goal is to familiarize students with several of the well known statistical sampling methods.

Course contents

• Inference in finite and infinite population sampling.
• Design based inference.
• Extraction probabilities, inclusion probabilities and the Horvitz-Thompson estimator.
• Simple random sampling with and without replacement.
• The design effect. Unequal probability sampling.
• Stratified random sampling.
• Cluster sampling. The estimation of sample size.
• Quotient estimators.
• Two stage sampling.

Each topic covered in the lectures will be followed by exercises in practical classes.
 


MODULE 2  Prof.ssa Stefania Mignani

The course introduces the fundamentals of techniques and models to analyze data from questionnaires. The main objective is to familiarize students with the most common latent variable models and hierarchical models.

-The survey tool: the questionnaire.

• Structure, typology and formulation of the questions.
• Methods of administering the questionnaire.
• Evaluation of the validity of the questionnaire: tools for the reliability analysis

- Latent variable models 

• For continuos data: the factorial analysis
• Item Response Thoery model
• Latent variable models for classification: the latent class analysis

 

 

The course includes laboratory exercises using the R software


Some case studies relating to socio-behavioral and economic investigations will also be discussed in the classroom

Readings/Bibliography

• Sharon Lohr, “Sampling: design and analysis”, Pacific Grove, Duxbury press, 1999.

• David J. Bartholomew ...[et al.], The analysis and interpretation of multivariate data for social scientists, 2002, Chapman & Hall
•  
• Basics of Item Response Theory (by Frank Baker) - EdRes.org (on-line)

 

Some additional readings will be indicated during the course.

Teaching methods

Course 1

Each topic covered in the lectures will be followed by exercises in practical classes.

 

Course 2

The course consists of lectures and computer laboratory activities in  R: lectures deal with methodological issues about the statistical tools listed in the course content, while computer laboratory sessions focus on the application of  on specific case studies.

Assessment methods

There is a partial test at the end of the first lesson period on module 1 ) and a partial test at the end of the second lesson period on module 2.

 The final grade will be the average of the two tests.

 

Course 1

Written test and a facultative oral examination

 

Course 2

The test (partial or total) is written and includes open and multiple choice questions both on methodological content and commentary on R.

Teaching tools

Slides, data sets and reports on statistical surveys

Office hours

See the website of Stefania Mignani

See the website of Carlo Trivisano