This research was funded by the Department of Defense Polygraph institute, Fort Jackson, SC, Grant No.: DASW01-03-1-0001. All views expressed in this report are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.
Multilevel models for repeated measures research designs in psychophysiology: An introduction to growth curve modeling
Article first published online: 26 JUN 2007
Volume 44, Issue 5, pages 728–736, September 2007
How to Cite
Kristjansson, S. D., Kircher, J. C. and Webb, A. K. (2007), Multilevel models for repeated measures research designs in psychophysiology: An introduction to growth curve modeling. Psychophysiology, 44: 728–736. doi: 10.1111/j.1469-8986.2007.00544.x
- Issue published online: 26 JUN 2007
- Article first published online: 26 JUN 2007
- (Received July 15, 2006; Accepted April 13, 2007)
- Multilevel models;
- Growth curve models;
- Repeated measures analysis of variance;
- Multivariate analysis of variance;
- Random effects;
Psychophysiologists often use repeated measures analysis of variance (RMANOVA) and multivariate analysis of variance (MANOVA) to analyze data collected in repeated measures research designs. ANOVA and MANOVA are nomothetic approaches that focus on group means. Newer multilevel modeling techniques are more informative than ANOVA because they characterize both group-level (nomothetic) and individual-level (idiographic) effects, yielding a more complete understanding of the phenomena under study. This article was written as an introduction to growth curve modeling for applied researchers. A growth model is defined that can be used in place of RMANOVAs and MANOVAs for single-group and mixed repeated measures designs. The model is expanded to test and control for the effects of baseline levels of physiological activity on stimulus-specific responses. Practical, conceptual, and statistical advantages of growth curve modeling are discussed.