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; Memory and judgment processes, Quantitative research methods in the social sciences; Statistical power analysis; Psychological measurement
• Editor positions: "Experimental Psychology" (Editor-in-Chief, 2007-2010), "Zeitschrift für Psychologie/Journal of Psychology" (Associate Editor, 2007-)
• Editorial board memberships: "Experimental Psychology" (2002-2006, 2011-), "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 Quarterly" (2003-2010), "Psychological Test and Assessment Modeling" (2011-).
• For more details and publications click here.

Dr. Martin Brandt, Senior Lecturer at the Department of Psychology, Chair III, University of Mannheim, Germany.

• Education: University of Bonn, Germany (1987 - 1993)
• Affiliations: University of Kiel, Germany (Research Assistant, 1993-1994); University of Bonn, Germany (Research Assistant and Lecturer, 1994-2000); University of Düsseldorf, Germany (Senior Lecturer, 2000-2003); University of Mannheim, Germany (Senior Lecturer, 2003-).
• Teaching experience: Experimental Psychology, Statistical Methods and Data Analysis, Cognitive Psychology, Motivation and Emotion, Biological Psychology, Applied Cognitive Psychology and Ergonomics
• Research interests: Experimental Cognitive Psychology; Memory and judgment processes, Formal models in Cognitive Psychology; Perception, Applied Cognitive Psychology
• For more details and publications click here.

Workshop contents and objectives

The workshop will cover the analysis of experimental and quasi-experimental designs with continuous dependent variables from an applied perspective. Among the topics are:

• Important concepts in experimental research
• Analysis of Variance (ANOVA) as a special case of Multiple Regression/Correlation (MRC)
• One- and multi-factorial ANOVA with fixed effects
• Post-hoc comparisons
• Planned comparisons and tailor-made hypothesis tests
• Analysis of covariance (ANCOVA) and alternatives
• Random and mixed effects ANOVAs
• 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?
• Brief introduction to Hierarchical Linear Models (Multilevel Modelling)

We will study and discuss these topics using behavioral research problems and both real and simulated data. We will apply most of these methods using SPSS and G*Power 3.1.

Bibliography

Relevant background knowledge

• Hays, W.L. (1994). Statistics (5th ed.). Fort Worth: Harcourt Brace CollegePublishers

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. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175-191.
• Faul, F., Erdfelder, E., Buchner, A. & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160.
• Hox, J. J. (2010). Multilevel analysis. Techniques and applications (2nd ed., chap. 1-4). New York: Routledge.
• Myers, J. L., Well, A. D., & Lorch Jr., R. F. (2010). Research design and statistical analysis (3rd ed.). New York: Routledge.
• Keppel, G. & Wickens, T. D. (2004). Design and analysis. A researcher's handbook (4th ed.). Upper Saddle River, NJ: Pearson Education International.
• Stevens, J. (2002). Applied multivariate statistics for the social sciences (4th ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

Prerequisites

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, & Lorch, 2010).

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 3.1 power analysis program (Faul et al., 2007, 2009). G*Power 3.1 is free. The program may be obtained from this web site.

Bring your own data

We encourage you to bring your own data to the workshop so that we can solve the data analysis problems you are faced with. If you will bring your own data to the workshop (which is optional, not compulsory), please contact us in advance (preferably by email: and briefly describe your data set and the underlying research questions. Please do so as early as possible but not later than August 5, 2011.