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STATISTICAL CORNER |
From the Behavioral Medicine Research Center, Department of Psychology, University of Miami, Coral Gables, Florida.
Address reprint requests to: Maria M. Llabre, PhD, Department of Psychology, University of Miami, P.O. Box 248185, Coral Gables, FL 33124. Email: mllabre{at}miami.edu
ABSTRACT
OBJECTIVE: This paper provides an introduction to latent growth curve (LGC) modeling, a modern method for analyzing data resulting from change processes such as cardiovascular recovery from stress. LGC models are superior to traditional approaches such as repeated measures analysis of variance and simple change scores.
METHODS: The basic principles of LGC modeling are introduced and applied to data from 167 men and women whose systolic blood pressure was assessed before, during, and after the cold pressor and evaluated speech stressors and who had completed the Cook-Medley Hostility Inventory.
RESULTS: The LGC models revealed that systolic blood pressure recovery follows a different nonlinear trajectory after speech relative to the cold pressor. The difference resulted not from the initial decline at the completion of the stressor, but from higher levels at the end of the stressor and slower rate of change in decline for the speech. Hostility predicted the trajectory for speech but not for cold pressor. This relationship did not differ as a function of gender, although men had larger systolic blood pressure responses than women to both stressors.
CONCLUSIONS: LGC modeling yields an understanding of the processes and predictors of change that is not attainable through traditional statistical methods. Although our application concerns cardiovascular recovery from stress, LGC modeling has many other potential applications in psychosomatic research.
Key Words: latent growth, cardiovascular, blood pressure, recovery.
Abbreviations: LGC = latent growth curve;; BP = blood pressure;; SBP = systolic blood pressure;; DBP = diastolic blood pressure;; CFI = comparative fit index;; RMSEA = root mean squared error of approximation;; CHD = coronary heart disease;; FIML = full information maximum likelihood;; SEM = structural equation modeling;; RM-ANOVA = repeated measures analysis of variance.
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