Master in Political and Social Sciences, UPF
Techniques of Statistical Analysis I, fall 2007
Anna Cuxart (office 201E52)
[email protected]
Subject teaching plan
1. Introduction
This subject entitled Techniques of Statistical Analysis (I) is aimed to provide the basic
statistical knowledge for the use and the analysis of quantitative data in empirical social science
research. Techniques of data analysis, basic inference and simple linear regression1 will be the
main topics. The course is problem-based, that is the theory is always presented in the context of
a practical problem needing solution.
The course therefore connects with the next subject Qualitative Research and also aims to
support the “Final Research Paper” as two of the core courses of the Master’s degree. Moreover,
through the more practical issues it provides a useful toolbox for doing quantitative research on
different compulsory courses. In order to have an applied background required for each
professional on social sciences, statistical software will support all the introduced concepts.
2. Prerequisites
There is not any specific prerequisite other than those asked for the Master.
3. Competences to be achieved in the subject
•
General competences (Instrumental, Interpersonal and Systemic)
Instrumental Competences
Ability to analyse and synthesise
Planning and management of time
Basic computer competence and ability of using statistical software
Information management abilities (ability to search and analyse information coming from a
variety of sources)
Interpersonal Competences
Critical and self-critical ability
Team work
Ability to work in a interdisciplinary team
Ability to communicate with people that are not experts in the subject
Systemic Competences
Research abilities
Ability to work autonomously
Ability to generate new ideas (creativity)
Design and management of projects
•
Specific competences
1
Students who need a deeper knowledge in regression techniques (multiple regression, logistic
regression,…) should take the further optional course named Techniques of Statistical Analysis (iI).
1
Basic statistical competence involves the following components:
1.
2.
3.
4.
data awareness,
knowledge of the basics of collecting data and generating descriptive statistics,
an understanding of certain basic concepts related to inference,
interpretation skills (ability to describe what the results mean in the context of the
problem), and
5. communication skills (being able explain the results to someone else).
6. understanding the hypothesis involved in modeling relationship between two or more
variables
4. Assessment
In the following the most relevant assessment criteria is specified:
•
The type of assessment will be continued with a compulsory final project on data
analysis. Some indications on the general structure of this project will be provided.
•
Five weeks during the course some homework will have to be delivered. It will be a
mixture of conceptual and applied activities. This will be individual job.
•
The final project could be individual or by couples. The second week of the course
working groups will be stablished.
•
At the end of the course, students will show their achievements in a final exam of two
hours length. The exam will include some questions and exercises related to the topics of
the course as well as some questions concerning to the developed project.
•
The weighting/assessment of each action within the overall value of the subject will be:
30% from the delivered homework, 40% from the final project and 30% from the final
exam.
5. Contents
•
Five content blocks or modules
1. Building the foundations: variability of the response.
The mean and the standard deviation: interpretation. Standard scores.
Distributions and plots. Some topics in a normal distribution.
2. Understanding the key points in a survey technical report. Some topics in Inference.
3. Exploring the association between two variables
Quantitative variables: scatter plots, covariance and correlation coefficient.
Qualitative variables: tables, profiles and Chi- square coefficient
4. Modelling continuous response with linear regression
The simple linear regression model in social science research: equation and
assumptions. Interpretation of the coefficients. Goodness of fit. Revision of the
hypothesis. Regression coefficients as random variables, properties and
hypothesis testing.
2
5. Introduction to multiple regression
Regression with continuous and categorical explanatory variables. Building the
model. Transformation of variables. Dummy variables. Model specification,
goodness of fit, interpretation of the coefficients.
6. Methodology
•
Methodological focus of the subject
Even though this is an applied subject, some techniques may be a little cumbersome for the
students. Thus it is required the attendance to the theoretical sessions named large group
sessions (see bellow) as well as the seminar sessions or small group sessions. There will also
be tutorial sessions for a more personalized questions on the final project or related to
conceptual questions. Individual weekly job outside the classroom will be needed to obtain a good
knowledge of the introduced techniques and methods.
•
Organisation of time: sessions, learning activities and estimated time of
dedication
In this subject will be three types of sessions: Large group sessions, seminar sessions and tutorial
sessions. The activities related with each of these sessions are as follows:
1. Large group sessions: These will be sessions for the whole group where the teacher
will introduce and explain all the subject’s contents defined in the previous section. These
sessions will include definitions, introduction to the most relevant results and
methodologies and examples of each issue. These large group sessions will take a total
of 12 hours.
2. Seminar sessions: These will be sessions for half (or third) group. These sessions will
be used to deepen and to practice with the concepts explained in the theory sessions.
Some seminar sessions will take place in the computer’s room. Each student will have a
total of 10 hours.
3. Tutorial sessions: Students will conduct a final research project. The last four weeks of
the course there will be some extra tutorial sessions in order to attend students’ consults.
Some recommendations from the teacher will also be provided in order to improve the
quality of the projects if it is needed.
7. Sources of information and didactic resources
•
Basic bibliography
Moore, D.S. 2006. The Basic practice of statistics. 4th ed., New York: W. H. Freeman and
Company.
Moore, D.S and G. McCabe. 2006. Introduction to the practice of Statistics, 5th edition. New
York: W.H.Freeman
Chatterjee, S., A.S. Hadi & B. Price 2000. Regression Analysis by Example, 3rd ed., John
Wiley.
Lewis-Beck, M. 1980. Applied Regression: an introduction. Sage Series on Quantitative
Applications in the Social Sciences n. 22
3
Lewis-Beck, M.S. Data Analysis: an introduction. SAGE Series on Quantitative Applications in
the Social Sciences n.103. University Press
•
Complementary bibliography
Moore, D. S. (1997), Statistics: Concepts and Controversies. 4th ed., New York: W. H.
Freeman and Company.
Any SPSS basic manual (and specific manuals for regression analysis) for versions 10 and
higher.
8. The programming of activities
See below an overall view of planned activities. A complete view of the activity to be carried
out each week of the term will be presented the first day of the class.
4
Week
1
2
3
4
5
6
7
8
9
10
Classroom activity
room
Large group session
Module 1
20031
Seminar session
Introduction to SPSS.
40257
Large group session
Module 2
20027
Seminar session
Assignment 1, discussion
20027
Tutorial session
Teams and Projects
201E52
Large group session
Module 3
20031
Seminar session
Computer’s room session
40257
Tutorial session
Teams and Projects
201E52
Large group session
Module 4
20027
Seminar session
Assignment 2, discussion
20027
Large group session
Module 4
20031
Seminar session
Computer’s room session
40257
Large group session
Module 5
20027
Seminar session
Assignment 3, discussion
20027
Large group session
Module 5
20031
Seminar session
Computer’s room session
40257
Tutorial session
Research project
20027
Seminar session
Assignment 4, discussion
20027
Tutorial session
Research project
20031
Seminar session
Computer’s room session
40257
Tutorial session
Research project
20027
Seminar session
Assignment 5, discussion
20027
5
Activity outside of the
classroom
Students prepare and deliver
Assignment 1
• To consolidate concepts
explained in the classroom.
• To decide the aim of the
project and the database
Students prepare and deliver
Assignment 2
Students prepare and deliver
Assignment 3
Students prepare and deliver
Assignment 4
Students prepare and deliver
Assignment 5