February 15-18, 2009 Washington State Convention Center Seattle ...

ANALYZING RESULTS THROUGH TIME USING A MULTIVARIATE APPROACH
Heidi A. Lewis* and Christopher C. Kohler
Fisheries and Illinois Aquaculture Center
Southern Illinois University
Carbondale, IL 62901-6511 USA
hal7e7@siu.edu
Aquaculture experimental design commonly involves measurement of multiple dependent variables in response to one or more
experimental treatment(s). Frequently, repeated measurements are taken over the course of the experiment to monitor progress.
Often these intermediary measurements are removed from the data set and the final measurement is used to evaluate the
predetermined hypothesis. Utilizing a hypothesis testing procedure that includes all data collected over the course of the trial
can provide researchers with the ability to interpret the rate of change as well as the final effect of the experimental treatment.
However, analyzing each dependent variable separately at each time point using univariate analysis of variance (ANOVA) will
inflate the risk of committing a Type I error. Whereas, multivariate analysis of variance (MANOVA) approach to repeated
measures allows researchers to statistically analyze multiple dependent variables simultaneously, therefore lowering the incidence
of Type I error and generating a more valid p-value. This multivariate approach to repeated measures uses MANOVA to
test within subjects hypotheses of flatness and parallelism within the data set. Further univariate hypothesis testing evaluates
between subject factors using one-way ANOVA. Advantages and disadvantages of utilizing profile analyses in aquaculture
nutrition research will be discussed with emphasis on juvenile and broodstock research.
1 1