Winter School in Empirical Research Methods
REGRESSION ANALYSIS II - LINEAR MODELS
The goal is to develop an applied and intuitive (not purely theoretical or mathematical) understanding of the topics and procedures, so that participants can use them in their own research and also understand the work of others. Whenever possible presentations will be in “Words,” “Picture,” and “Math” languages in order to appeal to a variety of learning styles.
Advanced regression topics will be covered only after the foundations have been established. The ordinary least squares multiple regression topics that will be covered include:
- Various F‑tests (e.g., group significance test; Chow test; relative importance of variables and groups of variables; comparison of overall model performance).
- Categorical independent variables (e.g., new tests for “Intervalness” and “Collapsing”).
- Dichotomous dependent variables: Logit and Probit analysis.
- Outliers, influence, and leverage.
- Advanced diagnostic plots and graphical techniques.
- Matrix algebra: A quick primer. (Optional)
- Regression models… now from a matrix perspective.
- Heteroskedasticity: Definition, consequences, detection, and correction.
- Autocorrelation: Definition, consequences, detection, and correction.
- Generalized Least Squares (GLS) and Weighted Least Squares (WLS).
This course will utilize approximately 325 pages of “Lecture Transcripts.” These Lecture Transcripts are organized in nine Packets and will serve as the sole required textbook for this course. (They also will serve as an information resource after the course ends.) In addition, the Lecture Transcripts will significantly reduce the amount of notes participants have to write during class, which means they can concentrate much more on learning and understanding the material itself. These nine Packets will be provided at the beginning of the first class.
It is important to note that this is a course on regression analysis, not on computer or software usage. While in‑class examples are presented using SPSS, participants are free and encouraged to use the statistical software package of their choice to replicate these examples and to analyze their own datasets. Note that many statistical software packages can be used with the material in this course. Participants can, at their option, complete several formative data analysis projects; a detailed and comprehensive “Tutorial and Answer Key” will be provided for each.
This course is a continuation of Tim McDaniel’s “Regression I – Introduction” course. While it is not necessary that participants have taken that specific course, they will need to be familiar with many of the topics that are covered in it.
Note: We will use matrix algebra in the second half of the course. We will not use calculus.
For more information click "LINK TO ORIGINAL" below.