I'm trying to do a power calculation for a study I'm involved in, and I'm a bit stuck.

The study is about textual cues of trust. We want to analyze a number of predictors in the text using the LIWC tool (which generates features based on text) and correlate them with perceived trust as determined by the users of this internet platform.

We want to do a multiple linear regression with a fixed number of predictors (we still have to determine the exact set, but think around 10-15). We expect low to medium correlations (say, .2-.3)

I want to do a power analysis. It's easy to find material to calculate the power for a correlation (like this)

However, I'm not sure how to deal with the measurement error. The thing is, the dependent variable is the mean perceived trustworthiness. We estimate this with a sample of ratings for each profile. From a pilot study I know that the SD of these ratings is around 0.6 (out of a Likert-scale of 5). So the SE of the mean rating, and thus the error of my measurement, is estimated to be:

SE = 0.6/sqrt(a) , where a is the number of raters per profile

How can I combine the power analysis that uses perfect measurement with the error in measurement to get a definite power?

And how does the number of predictors come into play?

So, basically, how do I get from:

a = number of raters

n = profiles (sample size)

p = predictors

r = expected correlation (0.2)

to a power? Any help is greatly appreciated!

The study is about textual cues of trust. We want to analyze a number of predictors in the text using the LIWC tool (which generates features based on text) and correlate them with perceived trust as determined by the users of this internet platform.

We want to do a multiple linear regression with a fixed number of predictors (we still have to determine the exact set, but think around 10-15). We expect low to medium correlations (say, .2-.3)

I want to do a power analysis. It's easy to find material to calculate the power for a correlation (like this)

However, I'm not sure how to deal with the measurement error. The thing is, the dependent variable is the mean perceived trustworthiness. We estimate this with a sample of ratings for each profile. From a pilot study I know that the SD of these ratings is around 0.6 (out of a Likert-scale of 5). So the SE of the mean rating, and thus the error of my measurement, is estimated to be:

SE = 0.6/sqrt(a) , where a is the number of raters per profile

How can I combine the power analysis that uses perfect measurement with the error in measurement to get a definite power?

And how does the number of predictors come into play?

So, basically, how do I get from:

a = number of raters

n = profiles (sample size)

p = predictors

r = expected correlation (0.2)

to a power? Any help is greatly appreciated!

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