Computer Lab #13 Output and Interpretations
SPSS Exercise for Chapter 14: What Are the
Correlates of Sexual Activity?
FREQUENCY
OF SEX
DURING LAST
YEAR
Correlations
FREQUENCY
OF SEX
DURING
AGE OF
GENERAL RESPONDENTS
LAST YEAR RESPONDENT HAPPINESS
SEX
**
1
-.386
-.028
-.017
Pearson
Correlation
Sig. (2tailed)
N
868
AGE OF
Pearson
-.386**
RESPONDENT Correlation
Sig. (2.000
tailed)
N
867
GENERAL
Pearson
-.028
HAPPINESS
Correlation
Sig. (2.418
tailed)
N
865
RESPONDENTS Pearson
-.017
SEX
Correlation
Sig. (2.617
tailed)
N
868
**. Correlation is significant at the 0.01 level (2-tailed).
.000
.418
.617
867
1
865
.016
868
.029
.537
.263
1445
1
1453
-.038
1453
.016
.537
.145
1445
.029
1449
-.038
.263
.145
1453
1449
1449
1
1457
Interpretation: Our dependent variable is the
frequency of sex during the last year. The
dependent variable is always the focus of your
study. When we look at the respondent’s age as
the independent variable, we see a Pearson
Correlation of -.386. That indicates a moderate
negative correlation between the two variables.
The negative sign indicates that as age increases,
frequency of sex during the last year decreases.
The association is statistically significant at p =
.000, which is less than .05.
When we look at general happiness as the
independent variable, we find the correlation is .028, a weak negative association. High values
on one variable are associated with low values
on the other variable. When we move toward
unhappiness, frequency of sex declines.
Therefore, when we move toward happiness,
frequency of sex increases. The two variables
are associated, but it’s not clear which is the
independent variable.
The last independent variable is the sex of the
respondent. The correlation is -.017, which is a
weak negative association. When sex moves to
female, frequency of sex declines. Therefore,
males report more sexual activity. If we assume
that men are having sex with women, then the
numbers should be identical. It appears that men
report more sex than women report, even though
the number of times they both had sex is the
same.
ANOVAb
Sum of
Model
Squares
df
Mean Square
F
Sig.
1
Regression
347.564
3
115.855
51.774
.000a
Residual
1924.432
860
2.238
Total
2271.995
863
a. Predictors: (Constant), RESPONDENTS SEX, AGE OF RESPONDENT, GENERAL
HAPPINESS
b. Dependent Variable: FREQUENCY OF SEX DURING LAST YEAR
Model
1
R
.391a
Model Summary
Adjusted R
R Square
Square
.153
.150
Std. Error of
the Estimate
1.496
a. Predictors: (Constant), RESPONDENTS SEX, AGE OF
RESPONDENT, GENERAL HAPPINESS
Model
Coefficientsa
Unstandardized
Coefficients
B
Std. Error
Standardized
Coefficients
Beta
t
Sig.
1 (Constant)
5.691
.272
20.914 .000
AGE OF
-.042
.003
-.391
- .000
RESPONDENT
12.418
GENERAL
-.123
.081
-.048 -1.524 .128
HAPPINESS
RESPONDENTS
-.110
.102
-.034 -1.074 .283
SEX
a. Dependent Variable: FREQUENCY OF SEX DURING LAST YEAR
Interpretation. You will first look at the number
under the “R Square” column. Multiply this
number by 100 to convert to a percentage. This
is the percentage of the variation in sexual
activity that is explained by all three
independent variables combined. Look at the
significance of the correlation in the ANOVA
table under “Sig.” That number needs to be less
than .05 for the correlation among the variables
to be statistically significant. The other numbers
you look at are in the table labeled,
“Coefficients.” Look in the column labeled
“Standardized Coefficients”, “Beta”. The
variable with the number that is farthest away
from zero is the most important independent
variable, the next closest to zero is the next most
important, and the one closest to zero is the least
important independent variable.
We look first at the R Square number in the
Model Summary table. The R Square value is
.153. We multiply that number by 100.
Therefore, our model that includes all three
variables together explains around 15% of the
variation in sexual activity. We also look at the
statistics under the column Standardized
Coefficients, Beta. Now the variables are
ranked according to their importance in the
relationship. Age is the most important variable
in our model of explaining the variation in
sexual activity among our respondents. We can
draw the conclusion about the whole population
of the United States, because the statistics under
the Sig. column are .000, less than .05, so the
association is statistically significant. The
direction is negative, so older people have less
sex than younger people. The next most
important variable is General Happiness. We
find that happier people have more sex. Lastly,
we find that males report having more sex than
females, so that explains a small amount of the
variation in reported sexual activity.