Avoiding self-plagiarism?

[futureapppsy2]

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I'm writing up a secondary analysis of some data for publication with a professor. Because this is a "large for the target population" longitudinal data set, several articles have already been published from the data set, but the analysis is novel and supplies a good deal of information that the published analyses don't contain. My question is how to write the demographic and measures while avoiding "self-plagiarism." Honestly, it seems like there are a finite number of ways to describe the same set of measures and same sample while having all the necessary information.

I've run into the same issues while writing intros on similar topics--there's only so many ways you can write "X is a serious public health issue that has been linked to X and Z."

Tips?

 

[MBellows]

I'm writing up a secondary analysis of some data for publication with a professor. Because this is a "large for the target population" longitudinal data set, several articles have already been published from the data set, but the analysis is novel and supplies a good deal of information that the published analyses don't contain. My question is how to write the demographic and measures while avoiding "self-plagiarism." Honestly, it seems like there are a finite number of ways to describe the same set of measures and same sample while having all the necessary information.

I've run into the same issues while writing intros on similar topics--there's only so many ways you can write "X is a serious public health issue that has been linked to X and Z."

Tips?

As described in study x,y, and z, this analysis elaborates on the relationship between blahblah mentioned in those studies by evidencing blah blah blah.

Include citations.


At least that's how I would do it, but I'm sure there are people who are actually published who could help a lot more.

 

[Ollie123]

Agree with the above - paraphrase and cite. If need be, get permission from the publisher and just include the descriptives table from your prior paper.

This is one of those areas where, much like the IRB, I feel that "ethics" (in quotations because I don't actually see an ethical issue here) has gotten out of control and simply become foolishness for the sake of foolishness.

I don't worry about this too much, nor does a single successful scientist I've met. Obviously don't copy and paste from your previously published work. Make sure its not identical. Jumble the order, put a slightly different spin on things, etc. (this is generally good writing anyways since I'm hard-pressed to think of situations where the exact nature of things is the same). Realistically, no one is going to come after you because you said word-for-word "The outcome variables were normally distributed and no outliers were detected." in two separate manuscripts or you have a few go-to citations you like to open with when writing on a certain topic, or your list of measures looks strikingly similar across projects. I firmly believe the fact that we are taught to worry as much as we do about such trivial, meaningless issues distracts us from the substantive issues that are of actual importance for moving the field forward and doing legitimate science.