Erdfelder:
Applied Analysis of Variance
and Linear Modelling
Prof. Dr. Edgar Erdfelder, Professor at the Department of Psychology, Chair III,
University of Mannheim, Germany.
- Education:
University of Göttingen, Germany (1974 - 1980)
- Affiliations:University of Trier, Germany (Research Assistant and Lecturer,
1981-1984); University of Bonn, Germany (Senior Lecturer, 1984-2001); University of
Gießen, Germany (Professor of Psychological Methodology, 2001-2002); University of
Mannheim, Germany (Professor of Experimental Psychology, 2002-)
- Teaching experience:
Experimental Psychology, Experimental Design, Statistical Methods
and Data Analysis, Methodology of Psychology, Cognitive Psychology, Motivation and
Emotion.
- Research interests:
Experimental Cognitive Psychology (especially memory);
Quantitative research methods in the social sciences; Statistical power analysis;
Psychological measurement
- Editor: "Experimental Psychology" (Editor in Chief since 2007),
"Zeitschrift für Psychologie -- Journal of Psychology" (Coeditor since 2007)
-
Editorial board memberships:
"Experimental Psychology" (2002-2006), "Journal of
Experimental Psychology: Learning, Memory, and Cognition" (2000-2002), "Methods of
Psychological Research - online" (1996-2004), "Methodology. European Journal of Research
Methods for the Behavioral and Social Sciences" (2004-), "Psychology Science" (2003-).
For more details and publications
click here.
The workshop will cover the analysis of experimental and quasi-experimental designs with continuous dependent variables from an applied perspective. Among the topics are:
- One- and multi-factorial analysis of variance with fixed effects (ANOVA)
- Post-hoc comparisons: to use or not to use?
- Planned comparisons and "tailor-made hypothesis tests"
- Analysis of covariance (ANCOVA) and alternatives
- Random and mixed effects ANOVAs: to use or not to use?
- Repeated-measures ANOVAs and MANOVAs
- Multivariate analysis of variance (MANOVA)
- Statistical power analyses for (M)ANOVAs, ANCOVAs, and planned comparisons
- What to do when the distributional assumptions are not met?
- Hierarchical Linear Models (Multilevel Modelling)
We will study and discuss these topics using practical research problems and both real and simulated data. We will apply most of these methods using the SPSS and the GPOWER computer programs.
Bibliography
Relevant background knowledge
- Hays, W.L. (1994). Statistics (5th ed.). Fort Worth: Harcourt Brace College
Publishers
Material that will be covered during the course:
- Cohen, J., Cohen, P., & West, S. G. (2003) Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.).
Mahwah, NJ: Lawrence Erlbaum Associates.
- Edwards, L. K. (Ed.). (1993). Applied analysis of variance in behavioral science. New York, NY, US: Marcel Dekker, Inc.
-
Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. (in press). G*Power 3: A flexible
statistical power analysis program for the social, behavioral, and biomedical sciences.
Behavior Research Methods.
Remark: A preprint of this article and the G*Power 3 program (both Windows XP/Vista and
Mac OS 10.4) can be obtained free of charge at
http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3/
- Myers, J. L. & Well, A. D. (2003). Research design and statistical analysis (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
- Keppel, G. & Wickens, T. D. (2004). Design and analysis. A researcher's handbook (4th ed.). Upper Saddle River, NJ: Pearson Education International.
- Snijders, T. A. & Bosker, R. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modelling. Thousand Oaks, CA: Sage.
- Stevens, J. (2002). Applied multivariate statistics for the social sciences (4th ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Prerequisites
Statistics/mathematics
You should have some background knowledge in experimental design and applied statistics as covered, for example, in the first one or two years of psychology studies (see, e.g., Hays, 1994; Myers & Well, 2003)
Computers/Software
You should be familiar with the SPSS data handling (i.e., data input, variable and value
labels, data transformations, merging and splitting data files, and the SPSS statistics
menu).
In addition, you should familiarize yourself with the G*Power 3 power analysis program
(Faul, Erdfelder, Lang & Buchner, 2007). G*Power 3 is free. The program may be obtained
from this web site.
Bring your own data
I urge you to bring your own data to the workshop so that we can solve the data analysis
problems you are faced with. If you are planning to collect data that will not be
available by August 2007, think about preparing a simulated data set. In any case, if you
will bring your own (real or simulated) data to the workshop, please contact me in
advance (preferably by email):
and describe both
your data set and the underlying research questions. Please do so as early as possible
but not later than August 11, 2007.