A Step by Step Guide for SPSS and Exercise Studies

128 Statistical tests
Figure 39
first criterion is based on whether the variables have reached a certain
probability level of the F value. The second criterion is based on whether the
variables have reached a certain F value. Most regression analyses should
contain a constant term. It is therefore advisable to tick the box include constant
in the equation. Under Missing values you can specify a number of different
ways of handling missing values. Exclude cases listwise deletes all cases
(participants) with missing values in any of the variables in the analysis. Exclude
cases pairwise deletes cases with missing values only on the pair of variables
used to compute the correlation coefficient on which the regression analysis is
based. Replace with mean does not delete any cases. On the contrary, it replaces
all missing values of a variable with the mean score of that variable. Use the last
two options to deal with missing cases when the sample size is not large enough
to provide a good ratio of cases to independent variables (see the assumptions of
regression analysis above). The analysis below was carried out using both males
and females.
The R value shows the linear association between the independent variables
and the dependent variable. The R Square value indicates that 22% of the
variance in the dependent variable is explained by the two independent
variables. Adjusted R square represents an adjustment of the R Square value, as
the latter is often overestimated in small sample sizes. Change Statistics are
useful when there is more than one block of independent variables in the