Quantitative Data Analysis: Logistic Regression Analysis Course, 2 op
The course is made up of several components, as follows:
1) Lectures (two each day);
2) Practical sessions on the computer using supplied exercises and/or student’s own data (one each day);
3) Discussion of papers in the literature that have used logistic regression (this will be a group discussion during the lecture sessions).
Exercises will be provided for the practical sessions. However, students may also wish to bring their own data that could also be used during the course. Past experience has shown that participants can derive considerably greater benefit from the course if they are able to work on their own data sets. Your data might come from an ongoing research study or thesis topic, or downloaded from publicly accessible databases. Time will be available for participants to work with their own data during the practical sessions, with the assistance of the course faculty members. If your data are not yet available from a particular project, you can alternatively discuss potential future uses of the methodology with course faculty members, again during the practical sessions.
Text book: The course will be partly based on material in Applied Logistic Regression, Hosmer DW, Lemeshow S, Sturdivant RX, Wiley. This is currently in a 3rd edition (ISBN: 978-0-470-58247-3, April 2013). It is certainly not necessary to buy this text for the course itself, but if you do, it will provide you with more details of things to be discussed in the course, and it also covers additional topics that we will not be able to discuss, because of time limitations. It therefore is a useful resource for your personal reading during and after the course. Note that the book is available as an electronic version from Tampere University Library.
The timetable is an approximate guide. Some flexibility may be required to deal with topics of interest to the group in greater depth, and we will not have time to cover not all the listed topics in detail (or at all).
Course notes will be distributed to students. These include copies of the lecture notes, exercises, and selections from the research literature illustrating the use of LR.
Students will be asked to read 3 research articles during the course, and they should be prepared to contribute to the group discussion of their strengths, weaknesses and interpretation. The articles will illustrate the use of LR in various studies. Several articles will be provided, and the group will decide which three to discuss in class.
Computer assignments will be based on the SPSS software package. Previous familiarity of students with SPSS is not required, but it would be useful. A brief orientation to data management and use of LR in SPSS will be given in the first practical session.