Increasing efficiency and precision of data analysis: multivariate vs. univariate statistical techniques.
In deciding upon an appropriate analysis strategy during the planning phase of a research study, it is important to specify all of the independent and dependent variables to be included. From there, a technique should be chosen that will yield all of the information desired in the most efficient, precise, and powerful way. Frequently, the method of choice in nursing research will be a multivariate technique, since so many studies involve numerous variables whose effects on, or relationships with, other variables are of interest, as well as involving additional variables that should be taken into account for control or generalizability purposes. Not discussed in this paper, but worth mention, is the fact that all of these techniques involve various underlying assumptions (e.g., normality, homogeneity of variance, and independence in ANOVA) that must be met in order to use the techniques appropriately. If the assumptions are not met, the researcher might want to consider the use of nonparametric techniques--and there are both univariate and multivariate techniques available to choose from (Hollander & Wolfe, 1973; Siegel, 1956). The general advantages of a multivariate technique rather than separate univariate techniques would apply in terms of nonparametric statistics as well as parametric statistics. Increasing the accuracy, power, and efficiency of data analysis strategies should be a major concern to researchers.(ABSTRACT TRUNCATED AT 250 WORDS)
D000704:Analysis of Variance; D009729:Nursing; D012107:Research Design; D013223:Statistics as Topic