The aim of my thesis is to see whether anxiety score can predict sleep quality and dream content.

So for this, there is one continuous variable (=anxiety score) that I want to use as a predictor on multiple dependent variables to see whether it can predict the outcomes on those variables. In order to do this, I used the manova function in R:

model1 = manova(cbind(d1,d2)~X

(in this function: d1 = dependent variable 1, d2 = dependent variable 2, X = anxiety score)

Now, when I type summary(model1), I get this output:

> summary (model1)

Df Pillai approx F num Df den Df Pr(>F)

X 1 0.30237 7.585 2 35 0.001834 **

Residuals 36

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

And then, because it is significant, I look at the results per dependent variable by typing

summary.aov(model1)

> summary.aov (model1)

Response d1 :

Df Sum Sq Mean Sq F value Pr(>F)

X 1 1791.5 1791.5 6.505 0.01515 *

Residuals 36 9914.2 275.4

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Response d2:

Df Sum Sq Mean Sq F value Pr(>F)

X 1 2164 2164.05 7.3081 0.01041 *

Residuals 36 10660 296.12

Now, I interpreted this as: "so anxiety score is a significant predictor of both d1 and d2, interesting!" But now that I have to report my results, I'm a bit confused on how to do this. I read that there should be a beta-coefficient that indicates the relationship, but there isn't one. What did I actually test? Did I use the right test?

Everything I can find online uses multiple predictors with one outcome variable instead of the other way around, which is what I intended to do...

So for this, there is one continuous variable (=anxiety score) that I want to use as a predictor on multiple dependent variables to see whether it can predict the outcomes on those variables. In order to do this, I used the manova function in R:

model1 = manova(cbind(d1,d2)~X

(in this function: d1 = dependent variable 1, d2 = dependent variable 2, X = anxiety score)

Now, when I type summary(model1), I get this output:

> summary (model1)

Df Pillai approx F num Df den Df Pr(>F)

X 1 0.30237 7.585 2 35 0.001834 **

Residuals 36

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

And then, because it is significant, I look at the results per dependent variable by typing

summary.aov(model1)

> summary.aov (model1)

Response d1 :

Df Sum Sq Mean Sq F value Pr(>F)

X 1 1791.5 1791.5 6.505 0.01515 *

Residuals 36 9914.2 275.4

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Response d2:

Df Sum Sq Mean Sq F value Pr(>F)

X 1 2164 2164.05 7.3081 0.01041 *

Residuals 36 10660 296.12

Now, I interpreted this as: "so anxiety score is a significant predictor of both d1 and d2, interesting!" But now that I have to report my results, I'm a bit confused on how to do this. I read that there should be a beta-coefficient that indicates the relationship, but there isn't one. What did I actually test? Did I use the right test?

Everything I can find online uses multiple predictors with one outcome variable instead of the other way around, which is what I intended to do...

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