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[OQSPS] Inequality across US counties: an S factor analysis

#11
Publication approved.
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#12
I approve the submission, but I think the manuscript could use one more review for spelling and grammar. I was working with the May 2 version, and some of the imperfections have been addressed in the May 4 update, but things that can still be addressed include:

* Because correlations are measures [of] linear associations [association?]

* most previous research on the topic have [has] used correlations

* I don't have [a] hypothesis for why this is the case.

* To investigate, all the environmental predictors were discretized into 10 bins the same was [way] temperature was before and entered into a regression model
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#13
Zigerell,

Thank you. I will have someone with a better eye for this kind of thing go over it. It is hard to find language mistakes in your own work because you already know what all the sentences say, so you don't read them as detailed as you would with other sentences. I have fixed the 4 errors pointed out above (revision #10).

With regards to reviewing. Pesta is also reviewing this, and with his approval, there would be 3 approvals.
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#14
I read the paper, but did not scrutinize many of the complex statistics presented here, as frankly I am not expert on these. In that sense, my review may not be very helpful (I don’t know if any statisticians have provided feedback above).

The paper is perhaps overly data-driven and theory light, but I see no real issues. It makes several important contributions at a less-well studied level of analysis (i.e., counties in the USA). The results converge nicely with what’s found using higher-level data like states and nations. I’m still not sure statistical analyses of data like these can get at cause and effect (and note that the author doesn’t claim causality anyway).

Section 9 was very clear, and the strongest part of the paper, in my opinion.

Also, whenever I tried publishing aggregate-level data, I was told to mention the ecological fallacy. I think the author does this once, but it might be good to mention it again in the second last paragraph of the “discussion and conclusion.” County-level mediation doesn’t imply the same when looking at individual differences for race, CA, and S.

Finally, there’s still some minor grammar / typo issues in places (e.g., Section 11, “very was”), but I otherwise approve publication.

Bryan
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#15
Bryan,

Thank you for taking the time to review this. It is a long paper.

Bryan Pesta Wrote:I read the paper, but did not scrutinize many of the complex statistics presented here, as frankly I am not expert on these. In that sense, my review may not be very helpful (I don’t know if any statisticians have provided feedback above).


Unfortunately, it is very difficult to find persons with expertise in sociology, differential psychology, factor analysis (including the new methods I devised) as well as model selection with LASSO regression.

If you have any suggestions for someone who has the expertise and has the time to review this paper, it would be fine with me if we could recruit a fourth reviewer for this paper (only 3 are mandatory).

Bryan Pesta Wrote:The paper is perhaps overly data-driven and theory light, but I see no real issues. It makes several important contributions at a less-well studied level of analysis (i.e., counties in the USA). The results converge nicely with what’s found using higher-level data like states and nations. I’m still not sure statistical analyses of data like these can get at cause and effect (and note that the author doesn’t claim causality anyway).


In general, I prefer to take a show, not tell approach to science and publishing. Lots of tables and figures so that the reader gets a good understanding of the data. The interpretation of the results is mostly up to the reader.

As you note, these kind of cross-sectional data are not very good at deciding between causal models. For this reason, I did not try to draw strong conclusions about causality. This is about the same approach as was done with the Admixture in the Americas paper. This fits with the show not tell approach because it lets the reader draw his own causal conclusions.

Bryan Pesta Wrote:Also, whenever I tried publishing aggregate-level data, I was told to mention the ecological fallacy. I think the author does this once, but it might be good to mention it again in the second last paragraph of the “discussion and conclusion.” County-level mediation doesn’t imply the same when looking at individual differences for race, CA, and S.


I mentioned the problem in passing, but not by that name. I wrote:

The conflicting results may be due to aggregation effects (lack of ergodicity), that is, analyzing the data at too high a level. If there is an aggregation effect at the state-level that causes the conflicting results for White and S, then it should not be present when the data is analyzed at the county-level since this is one level below the state-level.

Lack of ergodicity is exactly what gives rise to inferential problems between levels of analyses and thus the ecological fallacy. I will add a paragraph about it in the discussion.

Bryan Pesta Wrote:Finally, there’s still some minor grammar / typo issues in places (e.g., Section 11, “very was”), but I otherwise approve publication.


You are right. I will go over the paper again.
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#16
Hello,


I don't think a stats person will say anything that makes the paper unacceptable. I say approve it.


Bryan
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#17
Moving thread to the post-review forum...
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#18
When I did the study I didn't know how to map the data. However, I have since learned that. Out of curiosity, I mapped some of the data. This revealed a problem with the method used to find the mean temperature for each county. The map shows it all.

There are large areas even in highly populated areas with no temperature data. This is clearly a mistake. The way I gathered the temperature data by county was by finding the nearest county for each weather station. If there were multiple, I averaged the data. Since there were many more weather stations than counties, I figured this would cover almost all of them, or at least the important ones. This is wrong. Instead I should proceed the other way, namely finding the nearest weather station for each county and using that. This will always result in the counties having a datapoint, but it may not be a good one if the nearest station is far away. Still, should be better than the present.

An alternative is to impute the climatic data.


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