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Multilevel models for repeated measures research designs in psychophysiology: An introduction to growth curve modeling
Author(s) -
Kristjansson Sean D.,
Kircher John C.,
Webb Andrea K
Publication year - 2007
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/j.1469-8986.2007.00544.x
Subject(s) - psychology , psychophysiology , multilevel model , repeated measures design , latent growth modeling , cognitive psychology , developmental psychology , statistics , mathematics , neuroscience
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.