UPDATE: I don't think the single posted answer is correct. When I run summary(lm(DependentVar ~ IndVar1 * IndVar2, data = Data1)) the first listing is IndVar11 @ p = 0.113, and the four IndVar1:IndVar2 entries are all non significant (p = 0.16 to p = 0.41).
Regarding the suggested duplicate - the accepted answer there is about small p-value differences and small samples sizes, neither of which is the case here. In that answer, the author says he doesn't see other way this can happen. So what is happening here?
I'm looking for an explanation as to how an independent variable can go from being highly significant to being highly insignificant when an interaction term is added. IndVar1, below.
IndVar1 has 2 levels. IndVar2 has 5 levels.
Thanks
> Model_1 <- aov(DependentVar ~ IndVar1 + IndVar2, data = Data1)
> Anova(Model_1, type="III")
Anova Table (Type III tests)
Response: DependentVar
Sum Sq Df F value Pr(>F)
(Intercept) 18486.4 1 11622.9984 < 2.2e-16 ***
IndVar1 23.7 1 14.8532 0.0001353 ***
IndVar2 39.7 4 6.3382 5.711e-05 ***
Residuals 2175.1 1341
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> Model_1 <- aov(DependentVar ~ IndVar1 * IndVar2, data = Data1)
> Anova(Model_1, type="III")
Anova Table (Type III tests)
Response: DependentVar
Sum Sq Df F value Pr(>F)
(Intercept) 15473.4 1 9781.1865 < 2.2e-16 ***
IndVar1 4.3 1 2.7758 0.1131780
IndVar2 36.8 4 5.8247 0.0001231 ***
IndVar1:IndVar2 14.1 4 2.2124 0.0666647 .
Residuals 2149.1 1337
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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