Brian S. Everitt A Handbook of Statistical Analyses using SPSS

a) “Parametric” correlation coefficients
husbands' ages at
marriage
wives' ages at marriage
Correlations
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
**.
Correlation is significant at the 0.01 level (2-tailed).
b) “Non-parametric” correlation coefficients
Kendall's tau_b
Spearman's rho
husbands' ages at
marriage
wives' ages at marriage
husbands' ages at
marriage
wives' ages at marriage
Correlations
**.
Correlation is significant at the .01 level (2-tailed).
husbands'
ages at
marriage
Correlation Coefficient
Sig. (2-tailed)
N
Correlation Coefficient
Sig. (2-tailed)
N
Correlation Coefficient
Sig. (2-tailed)
N
Correlation Coefficient
Sig. (2-tailed)
N
wives' ages
at marriage
1 .912**
. .000
100 100
.912** 1
.000 .
100 100
husbands'
ages at
marriage
wives' ages
at marriage
1.000 .761**
. .000
100 100
.761** 1.000
.000 .
100 100
1.000 .899**
. .000
100 100
.899** 1.000
.000 .
100 100
Display 2.25 Correlations between husbands’ and wives’ ages at marriage.
The table also gives another t-test for testing the null hypothesis that the
regression coefficient is zero. In this example, as is the case in most
applications, we are not interested in the intercept. In contrast, the slope
parameter allows us to assess whether husbands’ age at marriage is
predictable from wives’ age at marriage. The very small p-value associated
with the test gives clear evidence that the regression coefficient differs
from zero. The size of the estimated regression coefficient suggests that
for every additional year of age of the wife at marriage, the husband’s age
also increases by one year (for more comments on interpreting regression
coefficients see Chapter 4).
© 2004 by Chapman & Hall/CRC Press LLC