# How to interpret significance of interaction term?

I have a problem interpreting an interaction effect. The interaction term (continuous_variable*Female) is significant (p < .05) in logistic regression, but the simple slope analysis suggests that the slopes of the continuous_variable (as a predictor) is not significant for Female=1 or Female=0

> interactions::sim_slopes(my.logit, pred = continuous_variable, modx= FEMALE, johnson_neyman = FALSE)
SIMPLE SLOPES ANALYSIS

Slope of continuous_variable when FEMALE = 0.00 (0):

Est.   S.E.   z val.      p
------- ------ -------- ------
-0.10   0.07    -1.34   0.18

Slope of continuous_variable when FEMALE = 1.00 (1):

Est.   S.E.   z val.      p
------ ------ -------- ------
0.10   0.09     1.17   0.24


However, when when I take continuous_variable as a moderator, simple slope analysis suggest significant slopes as below:

> interactions::sim_slopes(my.logit, pred =FEMALE , modx= continuous_variable,johnson_neyman = FALSE, robust='HC1')
SIMPLE SLOPES ANALYSIS

Slope of FEMALE when continuous_variable = 0.06 (- 1 SD):

Est.   S.E.   z val.      p
------ ------ -------- ------
0.10   0.14     0.75   0.45

Slope of FEMALE when continuous_variable = 0.88 (Mean):

Est.   S.E.   z val.      p
------ ------ -------- ------
0.27   0.10     2.70   0.01

Slope of FEMALE when continuous_variable = 1.71 (+ 1 SD):

Est.   S.E.   z val.      p
------ ------ -------- ------
0.43   0.13     3.22   0.00


Not sure how to interpret the significance of interaction term along with these simple slope analyses results. Does it suggest that I failed to find support for the interaction?

• An effect, even when not significant, is still an effect. This looks like a nice example of the importance of distinguishing significance from effect size. – whuber May 11 at 19:11

The significant interaction indicates that there is evidence that the simple slope of continuous_variable when FEMALE = 0 is different from the simple slope of continuous_variable when FEMALE = 1. Considering your first table, this means that -.10 is statistically significantly different from .10. It says nothing about whether each simple slope differs from zero.