1C Applying Regression
Jeremy Miles, RAND Corporation, Santa Monica
8 - 19 July (two week course / 35 hrs)
Detailed Course Outline [PDF]
THIS COURSE IS NOW FULLY BOOKED AND A WAITING LIST IS IN OPERATION
Course Content
The course will cover the theory and practice of regression analysis in its various forms. Regression models (broadly defined) are models which attempt to use predictors to explain a single outcome variable. This outcome variable may be continuous, ordinal, categorical or discrete counts and the predictors may be interval or categorical. The predictors may be linear, non-linear, or interactive.
Although the focus of the course is applying regression, we will start by looking at the meaning of models in statistics. We will consider the mean, correlation and regression as models, and regression to the mean. We look at describing models, and at statistical significance and confidence intervals (even though we expect you to know about this stuff, we will refresh it). Then we move on to develop more complex models (e.g. hierarchical regression, categorical independent variables), and consider the implications of the assumptions made in regression analysis (including the effect of their violation). We then look at extending regression in different ways: logistic regression, path analysis, interactions, Poisson regression and we will finish with a look at some more advanced techniques, such as multilevel modelling. Throughout the course we will cover examples in Stata, and occasionally use other programs, e.g. GPower for power analysis.
Course Objectives
The course will enable participants to carry out a range of regression analyses. It is appropriate for participants who have covered some statistics, and wish to extend their knowledge to modelling more complex social science phenomena. The course provides appropriate background for people who want to go on to courses such as multilevel modelling, probit and logit analysis, or structural equation modelling. The course starts off on a similar ground to the Introduction to Regression course, but proceeds more quickly and reaches beyond the material discussed in that course.
Course Prerequisites
The course starts from the beginning - we cover the mean, standard deviation, statistical significance, etc, but participants should probably consider this a refresher, and should have knowledge of descriptive and inferential statistics. Similarly, while we begin with simple correlation and regression, we will be thinking about these in some (possibly) new ways. You will need to know Stata , both the analysis techniques, and also transformation and recoding. We shall use Excel a little at the start as well.
Remedial Reading
We will expect that you have some knowledge of descriptive statistics, statistical significance, correlation, sampling and estimation, and will only cover these things briefly. Any introductory statistics book from your field will cover these issues. A couple of examples would be:
Miles, J and Banyard, P (2007). Understanding and using statistics in psychology. London: Sage.
But there are many others that you may be familiar with, which are just as good, or even better.
If you’re not familiar with Stata, a little practice would be a good thing, and the same goes for Excel. (Please feel free to contact me if you would like guidance on what you need to know – Jeremy.miles@gmail.com).
Representative Background Reading
Cohen, J., P. Cohen, et al. 2003. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. (3rd ed.). Erlbaum. (Very long, very thorough, best if your background is psychology).
Miles, J.N.V., and, Shevlin, M. 2001. Applying Regression and Correlation. Sage. (The course closely follows this book – making it worth buying to cover the course, but making it not worth buying, because the material is similar to the handouts).
Pedhazur, E. J. 1997. Multiple Regression in Behavioral Research. Harcourt Brace. (Not everyone likes the style of this book, so have a look before you buy it)
Studenmund, A. H. 2005. Using Econometrics: A Practical Guide. Addison Wesley. (This book focuses on econometrics, which has a slightly different emphasis from that we will take; it’s also very expensive).
A more gentle starter is: Allison, P. 1999. Multiple Regression: A Primer. Pine Forge Press.
Different books have different emphases, and we shall be talking about some of these issues in the classes.