Preliminary abstracts for preparatory courses in Rostock

Six weeks from early September to mid October 2017

Note: The following Abstracts pertain to the EDSD preparatory courses in Rostock in 2016. The courses in 2017 will be similar.

EDSD 110 Measures and Models of Demography
Course description:
The course is designed for first-year doctoral students in demography who have relatively little previous experience with the subject. It aims to provide EDSD students with an introduction to demographic methods and terminology. It deals with rates and probabilities, the Lexis diagram, the life table, life-time indices of quantum and tempo, period and cohort indices, stable and stationary population, population trends, and basic population projections.
Organization:
Instruction is given in the form of six 90-minute lectures in a three-week period.
Examination:
There is no examination in this course.
Textbook:
Preston, Samuel H, Patrick Heuveline and Michel Guillot. (2001). Demography: Measuring and Modeling Population Processes. Oxford: Blackwell Publishers.
EDSD 120 Basic Mathematics for Demographers
Course description:
This course is designed for students who are enrolled in the doctoral program in demography. During six weeks basic mathematics used in demography are reviewed. For most of the students this course is the opportunity to review and practice the mathematics of college level. The principal aim of this course is to bring students entering the doctoral program up to a common standard in mathematics. The course is also intended to prepare students for other courses of the doctoral program, which require that students know the basic mathematics. The main sections covered are: differential and integral calculus, matrices and differential equations. Applications to the study of populations are presented in each class.
Organization:
The course involves a total of 24 lecture hours. Instruction is given in 90 minutes for the first eight lectures, one-hour sessions for the rest of the lectures. Perfect attendance is strongly encouraged for those students that have not had mathematics for more than two years. A buddy system with fellow students is recommended and collective assignment will be encouraged.
Prerequisites:
The course is designed for first year doctoral students of demography, no prerequisites are required.
Examination:
Students are expected to hand in assignments each week to reinforce theory and examples of class. Weekly exams and assignments count for the final grade, which is based on the total number of points earned in the course.
General readings:
The course will rely heavily on the book Calculus by Steward (2003). The book on Matrix Population Models by Caswell (2001) will be used for the matrices part. However, for the demographic applications many other different sources are used which are also recommended to the students (Keyfitz (1968 and 1985) among others).
Stewart, James. 2003. Calculus 5e. Thomson Books/Cole, Belmont CA.
Caswell, Hal. 2001. Matrix Population Models 2e. Sinauer Associates, Sunderland Massachusetts.
Keyfitz, Nathan. 1985. Applied Mathematical Demography. Springer-Verlag: New York.
Keyfitz, Nathan. 1968. Applied Mathematical Demography. Addison-Wesley: Reading Massachusetts.
EDSD 130 Basic Statistics for Demographers
Course Description:
This course will reiterate basic statistical models and techniques for demographers. The topics covered will be:
- Tools for Exploratory Data Analysis (Tabulation, graphical displays, summary statistics)
- Basic Probability Theory (including random variables, discrete and continuous distributions)
- Statistical models and their estimation (with an emphasis on maximum likelihood)
- Sampling variability, Confidence Intervals and Testing
- Regression (Linear models, including ANOVA, diagnostics)
- Generalized Linear Models: Logistic Regression, Poisson Regression
Organization:
The course involves twelve 90-minute lectures with occasional additional computer labs. The statistical software R is used (freely available, see www.r-project.org).
Prerequisites:
Even though this course covers many topics of an introductory statistics course, the speed of presentation of the first part is quite high as the emphasis in this course is on modelling data. Hence some background in descriptive statistics (including correlation and regression) is advantageous. Students who do not feel comfortable with mathematical expressions (differentiation, integral, matrix notation) are recommended to also attend the course “Basic Mathematics for Demographers”.
Examination:
Students have to hand in solutions to weekly assignments and will have to pass a written exam at the end of the course (120 minutes). The exam and the assignments contribute equally to the final grade.
General Readings:
The course is based on the book:
Agresti, A. and B. Finlay: Statistical Methods for the Social Sciences. Third edition. Prentice-Hall, 1997.
Complementing material is distributed during the course.
EDSD 140 Computer Programming for Demographers
Course Description:
This course introduces the programming language R. It is very well suited for typical demographic analyses which often require more capabilities than other packages can offer. Besides the availability of more built-in statistical procedures than any other widely used statistical package and its outstanding plotting possibilities, R offers an easy way to extend the language by implementing new methods or modifying existing ones. Hence this introductory course for R will be aimed into two directions: On the one hand, the course will show how to perform 'standard' data manipulation and statistical methods known from other programs. On the other hand, a thorough introduction will be given on programming with R. The topics covered in the course are:
a) Basic concepts of R (calling functions, object-orientation)
b) Representation of Data (vectors, matrices, dataframes, lists); manipulation of data; referencing elements in data;
c) Distributions in R
d) Data Input / Output; reading data (ASCII format as well as binary formats like .sav from SPSS), writing data
e) Programming with R; flow control (conditional, repetitive execution); writing functions; selected aspects of vectorized computations
f) Optimization
g) Graphing Data
h) Simple statistical modelling
Organization:
There are six lectures à 90 minutes in the computer lab. The instructor will present concepts and methods using R. The participants will employ those methods immediately on their computers in the lab.
Prerequisites:
Familiarity to work in a computer environment is essential, especially being able to work with a text editor. The presentations will be given using Tinn-R which is free software and can be downloaded from www.sciviews.org/Tinn-R. Nevertheless course participants can freely choose their preferred editor (Emacs, UltraEdit, WinEdt,…). This course requires for some parts material covered in the other preparatory courses (e.g. linear regression, probability theory, statistical tests).
Examination:
Students are expected to complete two take home assignments.
General readings:
Some parts of the course will be based on:
W.N. Venables, D.M. Smith and R Development Core Team (2005): An Introduction to R. (Available online)
In addition, a script is distributed in each lecture which covers all concepts and examples.