Page 518 - Encyclopedia of Nursing Research

STATISTICAl TeChnIQUeS n 485

each group will have the same variance The correlation coefficient is a mathemat-
covariance matrix means that the homoge- ical representation of the relationship that
neity of variance assumption is met for each exists between two variables. The correlation S
dependent variable and that the correlation coefficient may range from +1.00 through
between any two dependent variables must 0.00 to –1.00. A +1.00 indicates a perfect pos-
be the same in all groups. Box’s M is a mea- itive relationship, 0.00 indicates no relation-
sure of the multivariate test for homogeneity ship, and –1.00 indicates a perfect negative
of variance. relationship. In a positive relationship, as
Repeated measures AnOVA is an exten- one variable increases, the other increases.
sion of AnOVA that reduces the error term In a negative relationship, as one variable
by partitioning out individual differences increases, the other decreases. The strength
that can be estimated from the repeated of correlation coefficients has been described
measurement of the same subjects. There as follows: .00–.25—little if any; .26–.49—
are two main types of repeated measures low; .50–.69—moderate; .70–.89—high; and
designs (also called within-subjects designs). .90–1.00—very high (munro, 1997, p. 235).
One involves taking repeated measures of The coefficient of determination, r , often is
2
the same variable(s) over time on a group used as a measure of the “meaningfulness”
or groups of subjects. The other involves of r. This is a measure of the amount of vari-
exposing the same subjects to all levels of the ance the two variables share. It is obtained by
treatment. This is often referred to as using squaring the correlation coefficient.
subjects as their own controls. logistic regression is used to determine
Correlation is a procedure for quanti- which variables affect the probability of the
fying the linear relationship between two occurrence of an event. In logistic regression,
or more variables. It measures the strength the independent variables may be at any level
and indicates the direction of the relation- of measurement from nominal to ratio. The
ship. The pearson product–moment corre- dependent variable is categorical, usually a
lation coefficient (r) is the usual method by dichotomous variable. Although it is possible
which the relation between two variables to code the dichotomous variable as 1/0 and
is quantified. There must be at least two run a multiple regression or use discriminant
variables measured on each subject; and function analysis for categorical outcome
although interval- or ratio-level data are most measures (two or more categories), this is gen-
commonly used, it is also possible in many erally not recommended. multiple regression
cases to obtain valid results with ordinal and discriminant function are based on the
data. Categorical variables may be coded for method of least squares, whereas the max-
use in calculating correlations and regres- imum-likelihood method is used in logis-
sion equations. Although correlations can be tic regression. Because the logistic model is
calculated with data at all levels of measure- nonlinear, the iterative approach provided
ment, certain assumptions must be made to by the maximum-likelihood method is more
generalize beyond the sample statistic. The appropriate. logistic regression has been
sample must be representative of the popu- reported in the medical literature for some
lation to which the inference will be made. time, particularly in epidemiological stud-
The variables that are being correlated must ies. Recently, it has become more common
each have a normal distribution. The rela- in nursing research. This is the result of a
tionship between the two variables must new appreciation of the technique and the
be linear. For every value of one variable, availability of software to manage the com-
the distribution of the other variable must plex analysis. This multivariate technique for
have approximately equal variability. This is assessing the probability of the occurrence
called the assumption of homoscedasticity. of an event requires fewer assumptions than