Year 2010-11
Statistics (21975)
Qualification: Degree in Political and Administration Sciences.
Year: 2nd
Term: 2nd
Number of ECTS credits: 6 credits
Hours of student dedication: 140
Teaching languages: Catalan
1. Introduction to the course
This course is designed as a basic training course for students of Political and Administration Sciences. It builds on the previous related course, Data Analysis (code?) and hence assumes students have a basic knowledge of descriptive statistics and an ability to work with data using SPSS and Excel.
The main objective is to introduce the essential concepts associated with statistical variability and the role that variability plays in empirical analysis. Based on the concept of population statistics the concepts of randomness, random sample, as well as different methods used to obtain accurate statistics of the population from sample data will be introduced. The course aims to provide students with the ability to use, avoiding frequent conceptual mistakes, techniques that are today recognized as being essential in empirical social science. The course includes topics such as the basic aspects of probability, sample designs, estimating by interval and point estimation, chi square tests, the logic of hypothesis testing and significance tests, analysis of variance, analysis of correlations and lastly, introduction to regression analysis. The course will take an applied approach whereby the theorems and formulas will be substituted with real life statistical examples using relevant social science databases. While formulas may not be essential, great emphasis will be placed on concepts and the logic behind statistics. The main statistical software programme used will be SPSS. Students will be able to substitute or complement the use of SPSS with the open-access R programme (http://www.r-project.org/).
2. Competences to be achieved
Generic competences:
Ability to analyse and synthesise quantitative information
Ability to design the collection of statistical information (sample design)
Ability to critically assess statistical reports. Ability to produce basic statistical reports.
Ability to differentiate between association and causation, between experimental and observational data and different levels of measurement of statistical variables.
Ability to apply statistical logic and thinking in diverse practical scenarios.
Ability to manage databases and widely used statistical software (such as SPSS).
Specific competences.
Ability to critically evaluate claims involving statistics
Ability to use statistical software (SPSS) to make statistical inferences from data
Ability to critically engage with the differences between statistics and parameters, sampling errors, representative samples, biased samples, error type I and II, and other conceptual elements.
Ability to determine the sample size necessary for a desired level of accuracy
Ability to read / understand technical survey information published in newspapers
Ability to do basic statistical analysis and make a summary in plain rather than technical language.
3. Contents
Topic 1. Population and sample. The nature of statistical information. The concept of random. Population and probability simple. Sample designs. Precaution when using sample surveys. Experimental data.
Topic 2. Basic elements of probability. The concept of probability, interpretation, the properties of probabilities, the sum and product of probabilities, conditional probability, Bayes Law, probability distributions, random discrete and continuous variables, random variable parameters, the normal distribution law.
Topic 3. Statistical inference: estimates by interval. Sample distribution, median samples and proportionality, estimation bias, estimator efficiency, sample proportionality, median sample, sample distribution in large samples. Estimating by interval: the case of proportionality and median sample. Estimate precisión and sample size. Calculating precise sample sizes. The problem of small samples.
Topic 4. Statistical inference: statistical significance tests and hypothesis testing. The logic behind significance tests, chi-square tests, p -values, proportionality test and population mean, hypothesis testing, type I and type II errors, the value of tests, the power of tests and the application of the test for equal means.
Topic 5. Statistical inference: comparing means. The problem of comparing groups or populations. The case of two proportions, the case of two means, more than two means (ANOVA). The consequences of deviating from basic assumptions
Topic 6. Association between variables. Statistical correlation, correlation does not imply causality, association between categorical variables: chi-square test of independence. Association between continuous variables: covariance and correlation. Correlation inferences.
Topic 7. Simple regression model. Mitjana condicional, linera regression line, regression model parameters, estimating regression models: MCO method, determination coefficient, introduction to multiple regression.
4. Assessment
Continuous assessment based on seminar activities carried our individually and/or in groups (homework/tasks/tests). A piece of coursework in groups (5 students max.) that must be handed in the same day as the final exam at the latest. It will count towards 20% of the final mark.
Final exam: 50% of the final mark.
5. Bibliography and other resources
MANTZOPOULOS, V. L. Statistics for the Social Sciences. Englewood Cliffs: Prentice-Hall, 1995.
MOORE, D. S. Statistics. Concepts and Controversies. Nova York: W. H. Freeman, 2000.
MOORE, D. S. Estadística aplicada básica. Barcelona: Antoni Bosch Editor, 1998.
PEÑA, D.; ROMO, J. Introducción a la estadística para las ciencias sociales. Madrid: McGraw-Hill, 1997.
SIRKIN, R. M. Statistics for the Social Sciences. Newbury Park: Sage, 1995.
TANUR, J. M. La estadística. Una guía de lo desconocido. Madrid: Alianza, 1992.
5.2. Other resources
Resources from Moore's book http://bcs.whfreeman.com/bps3e/default.asp?s=&n=&i=&v=&o=&ns=0&t=&uid=0&rau=0 and other resources which will be made available in the course web page provided by the teacher.
6. Methodology
The course will combine lectures and seminars. There will be 10 lecture sessions each two hour long where the concepts and general procedures of the subject will be explained with examples using data from the social sciences.
Seminars will be taught in computer labs and students will work with SPSS. Students will also work on and solve problems set during these sessions.
Students will also be required to carry out individual work which will consist in solving exercises, reading on topics recommended during lectures, the acquisition of the contents covered by the lectures, writing a statistical report related to a newspaper article.
7. Programme of activities
There will be a weekly two hour-long lecture for 10 weeks. Seminars will be held approximately every 3 weeks.
Week 1: Lecture on topic 1 and its applications + seminar 1.
Week 2: Lecture on the first half of topic 2 and its applications
Week 3: Lecture on the second half of topic 3 and its applications
Week 4: Lecture on the first half of topic 4 and its applications + seminar 2
Week 4: Lecture on the second half of topic 4 and its applications
Week 5: Lecture on the first half of topic 5 and its applications
Week 6: Lecture on the second half of topic 5 and its applications + seminar 3
Week 7: Lecture on part of topic 6 and its applications
Week 8: Lecture on the rest of topic 6 and introduction to topic 7
Week 9: Lecture on topic 7, simple regression and its applications. Seminar 4
Week 10: Summary lecture. In this lecture the different topics covered will be related to one another, their different advantages and limitations will be explored and further fields in the study of statistics will be introduced. Part of this session will be devoted to preparation for the final exam.
Seminars:
Seminar 1: Students will work on randomness, the concepts of probability and probability distributions and random samples. This will be done using basic functions on SPSS. Chapter 3 in Moore's book (Applied Basic Statistics) will also be discussed.
Seminar 2: Students will be required to apply their knowledge on sample design, statistical inferences and simple linear regression model. During the seminar SPSS basic functions relating to probability, sample distributions and confidence estimates will be introduced through the use of a Political Science database. Chapter 5 of Moore's book (Applied Basic Statistics) will also be discussed.
Seminar 3: In this seminar students will work on topics related to significance tests and hypothesis testing. During the seminar basic SPSS functions relating to statistical tests will be introduced. Chapter 6 and 7 of Moore's book (Applied Basic Statistics) will be discussed.
Seminar 4: Students will work on topics relating to ANOVA and regressions. During the session SPSS functions relating to ANOVA and regression will be introduced. Chapters 9 and 10 in Moor's book (Applied Basic Statistics) will be discussed.