How can I test my hypothesis with these variables?

#1
Hello all,

I'm writing my thesis and I can't figure out the following:

Two of my hypothesis is the following:
1) The effect of political preferences on vaccine readiness increases when individuals encounter less barriers in their vaccination decision making.
2) The effect of political preference on vaccine readiness increases when a person is in better health.

My professor had the following feedback on both hypothesis: "nice" – which implies I can test the hypothesis I guess. But how can I do it? I have to following variables and results:

X (political preferences) – it is measured on a 10 point scale. 0 = political right – 10 is political left (I mirrored it because it was easier to interpreter my other hypothesis).
Y (Vaccine readiness) – it is measured on a 100 point scale. Respondents had to tell how big their chance is to get a covid-19 vaccine. 0 means no chance and 100 that they absolutely take a vaccine
W (Barriers) – it is measured on a continuous scale from 0 to 2 (it is a product of 5 variables, so there are also respondents who score between a 0 and a 1 and a 1 and the 2)
Z (Health risk) – (0 to 5 scale) – a higher scores indicates a higher health risk and a lower score on a better health.

My main problem is: I don't understand how I can say something about the effect of political preferences. I think I only can say something about if respondents get more left of right orientated, right?

The output is below (it contains other variables you can ignore). My X-variable is significant. The moderations too. So they have a significant effect on Y. But the interaction terms (mean centered) are not. So what does this tell me exactly?
1652943268002.png
 

Karabiner

TS Contributor
#2
My main problem is: I don't understand how I can say something about the effect of political preferences. I think I only can say something about if respondents get more left of right orientated, right?
Yes, you can tell, while adjustng for other variables, what happens if the political orientation
measure increases (or decreases). Could tell us why you consider this as a problem?

My X-variable is significant. The moderations too.
Both moderations (interactions) are NOT statististically significant (p=0.876 and p=0.603,
respectively). The assumed moderators have a main effect on the dependent variable,
but they do not moderate the effect of political preference.

With kind regards

Karabiner
 
#3
Thank you for your response Karabiner. I consider this as a problem because the variable only says something about a left or right oriëntation, right? But it does not say something about how important politics is to the people. Some background of my hypothesis (for 1 an example)

When people are very healthy, they value politics more in their decision making for a vaccination, but then people are very ill or have a bad health, they value it less. a vaccination is much more of a lifesaver than a way of expressing their political views (it doesn't matter that much if that is leftist or rightist).

And you are right about the significance part. But what doe sit mean that the main effects are significant but the interaction isn't? It doesn't matter that much since the effect is very small, but what if it was bigger and significant. What does it tell me than exactly?
 

Karabiner

TS Contributor
#4
I consider this as a problem because the variable only says something about a left or right oriëntation, right? But it does not say something about how important politics is to the people.
Yes, people with low importance, medium importance, high importance are collapsed here.
Nevertheless, across all (unobserved) levels of importance within your sample, you can make
a general statement about the association between political orientation and vaccine readiness.
And you are right about the significance part. But what doe sit mean that the main effects are significant but the interaction isn't? It doesn't matter that much since the effect is very small, but what if it was bigger and significant. What does it tell me than exactly?
A statistically significant interaction effect could say, for example, that the association between the
variable "health risk" and vaccine readiness is different for people with a rightist, or moderate, or leftist
political orientation: the association becomes stronger if participant orientation moves from left to right.

With kind regards

Karabiner