The course is designed for those who have limited knowledge and experience with multivariate statistical techniques and are seeking the knowledge and skills to use multiple regression for research at a post-graduate level and/or to publish in professional research journals. Particular attention is given to the application of multiple regression to substantive problems in the social and behavioral sciences.

By the end of the course, you will understand the principles of multiple regression, and be able to conduct regression analyses, interpret the results, obtain regression diagnostics to test the underlying model assumptions and write-up the results for publication. The course notes provide instructions for using the major statistical packages (SPSS, SAS, Stata or R) for regression.

Participants who are considering regression analysis of their own data are encouraged and there will be time for individual consultations.

This course provides the foundations necessary for progression to ‘Applied Multiple Regression Analysis’, and to subsequent advanced-level courses in structural equation modelling and multi-level analysis.

Participants should understand basic descriptive statistics and have some experience with a statistical software package. More specifically, they should have:

(a) completed an introductory statistics course, such as Fundamentals of Statistics, or have knowledge of basic descriptive statistics (central tendency, dispersion, Z-scores), bivariate analysis (crosstabs and/or bivariate correlations) and familiarity with the principles of null-hypothesis significance testing and “P-values”.

(b) experience with a statistical package (such as SPSS, Stata, or SAS) sufficient to read or import data, obtain descriptive statistics and frequencies, recode variables and compute (“generate”) new variables.

Participants are encouraged to use the statistical package of their choice. The courses notes provide the commands necessary to apply the principles of multiple regression to practical data analysis using statistical packages.

This course covers enough of the statistical material for the intelligent use of the technique. The approach is informal and applied rather than emphasising algebraic manipulation or proofs.

Participants can use their preferred statistical software (SPSS, SAS, Stata or R, etc) for the data analysis exercises. Data sets are provided; however participants may use their own data if they wish.

You will need a computer and your preferred software, SPSS, SAS, Stata, Jamovi or R.
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