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[OQSPS] Inequality across prefectures in Japan is different

#11
I have added the distribution of ISR correlations to the paper (version 8). The next version also has table borders for the table that is missing them.
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#12
Zigerell sent me some comments on the writing via email, attached.

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Quote:Quotes here but not elsewhere for S factor.

I used the single quotes to talk about the first factor that would otherwise be S if it actually looked like S. They are not only used in the abstract, but also in the text (Sections 8 and 9) and on Figures (e.g. 4).

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Municipal vs. municipality. Wikipedia uses the first. https://en.wikipedia.org/wiki/Administra...s_of_Japan

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Ratio of dwellings with flush toilet

Quote:Percentage instead of ratio?

The descriptions are copied verbatim from the Japanese website. Sometimes their translations are a bit odd. In this case, they talk about ratios, i.e. fractions (values 0-1), but a look at the data reveals them to be percentages (values 0-100). However, since these are equivalent for use in correlational analysis, there is little point to dwell on this minor problem.

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Quote:this decimal place is dislocated from the 82

It is because LibreOffice does not understand that dots can be part of words and so they can be split across linebreaks. There is no automatic solution for this, but I have fixed the identified instances.

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Quote:should there be a comma between "data" and "analysis"?

I meant to write "data or analysis error". Fixed.

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Quote:can be avoided?

Meant to write "could be avoided".

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Other minor errors fixed.

Table borders added.


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Files updated. This is version 9 of the paper.

https://osf.io/4bw8u/files/

I have also written an explanation of the files for the project on OSF: https://osf.io/4bw8u/wiki/home/

This is to make it easier for re-use by other people including myself later!.


Attached Files
.pdf   zigerell.pdf (Size: 1.32 MB / Downloads: 415)
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#13
Newbie here. I assume this paper still needs at least one more reviewer. If so, I will download it and have some comments hopefully within a week. If there's another manuscript with more pressing need for review, please let me know.

B
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#14
(2016-Jan-25, 18:27:51)bpesta22 Wrote: Newbie here. I assume this paper still needs at least one more reviewer. If so, I will download it and have some comments hopefully within a week. If there's another manuscript with more pressing need for review, please let me know.

B


The paper needs 2 more reviewers because Kenya Kura was too closely related to the project to be an independent reviewer. I will try to get an external reviewer, perhaps another Japanese sociologist (Arikawa?)
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#15
This is well-written and rather-straightforward. I’m ok with accepting the version I read. Emil might consider any of the changes I suggest below, but these are not required. My comments on sub-domains, I think, would add to the paper, but the time involved to do this would likely not be worth the incremental benefit.

1. I’m uncomfortable adjusting for population size when looking specifically at aggregate level data—i.e., geo-political subdivisions such as states, prefects, nations, etc. The unit of analysis here is uniquely a prefecture, and there are important mean differences across prefectures, including population size. To treat the latter as unique and something that needs controlled for (versus something that might be a variable in the S factor) seems arbitrary. I get that the correction allows the data to make sense relative to other data sets, but the correction makes me uneasy in ways that I wish I could express better (and so I could be wrong on this point). Also, as far as I can tell, no explanation is offered for why density mediates the S/IQ relationship.

2. (Minor). Perhaps expand more on Jensen’s method, unless you think all readers here would be familiar with it.

3. Quantitatively, you do a fine job identifying and excluding redundant variables. Qualitatively, you do not. For example, I don’t see “In work male” and “unemployment male” to be unique enough to include in the same analyses. The sum of both define “male labor force participation,” and so I think these should be summed and collapsed into one variable. Labor participation, unemployment, and unemployment benefits seem like examples of this as well.

4. I suggest calculating S factors hierarchically—as we do with g. There are sub-domains of well-being (income, health, crime...) that could be derived. Thereafter, S could be extracted from the sub-domains. In the USA, for example, though a very strong S factor exists, the sub-domains show divergent validity with other variables. For example, health and religiosity correlate more strongly with IQ than does income and education (albeit the education factor for the USA contains relatively few variables—the downside to my suggestion is to make sure each sub-domain is equally construct valid).

5. I’ve considered IQ to be a central node in the S nexus for the USA. You instead use S to predict IQ. I wonder if IQ is just a sub-domain of S, or a cause/effect of S.

6. Right before Section 3, “the data are from Kura and the second that they are from myself.”
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#16
Bryan,

Thanks for reviewing.

Adjusting for population size
As you say, this adjustment is exploratory and there was no strong theoretical justification for doing this. For this reason, it is clearly labelled as so in the paper, e.g. I write "However, in an exploratory (unplanned) analysis ...".

The question of what the unit of analysis is (in this case prefectures) and what one is really interested in is a common conundrum for multi-level data analysis. Should we give more importance to results from schools if they have more students? My preference is to weigh units by their importance if possible, while yours seem to be not to do so. Presumably, in a country-level analysis, you would give equal weight to Monaco as you would to the USA despite the latter having about 8500 times as many citizens. Many would probably find that strange. I note that not using weights has been questioned in the cognitive sociology literature (Hunt and Sternberg), so it is (sort of) a case of: damned if I do, damned if I don't.

In this case, the control was not made for population size itself, but for (log) population density (in the final analysis). Population density has (I think) been used as an indicator in other S factor analyses and usually has a positive loading (closely related to urbanity which loads positively). Thus, using it as a control is somewhat strange at least.

However, one could make similar criticism of immigrant % as done in the recently published paper on French departments. http://mankindquarterly.org/archive/paper.php?p=803 (ungated https://www.researchgate.net/publication...ev=prf_pub) Perhaps immigrant % should be seen as another indicator of S, not something to control for. After all, the higher S areas tend to attract more immigrants if one looks at a fairly zoomed-out level (immigrants live in cities, tho in the poor areas). Here I am talking about European style, recent immigration (since about 1970).

I don't offer any particular reason for why one would need to control for population density in Japan, but not other places because I can't think of any such reason. It is quite the mystery. In future S factor studies I will try including controls for population density to see what it does. Perhaps one should do a big meta-analysis. Since I have published all my data from all studies, anyone with the time and competence can do this.

Jensen's method
You are right that the paper is light on this method. I have used it so much that it doesn't seem special to me any more (the curse of knowledge).

I have added a footnote with a brief explanation:

Quote:Jensen's method (method of correlated vectors, named after the great psychologist Arthur Jensen) consists of correlating the factor loadings of indicators with their relationships to some criterion variable. The reasoning is that if the relationship between the factor scores and the criterion variable is real, then (everything else equal) the indicators that have stronger loadings on the factor (i.e. are better measures of it) should be more strongly related to the criterion variable than those with weaker loadings. For more details, see [1], [19].

Data-driven vs. theory-driven research
The difference in methods that you identify concerns one aspect of data-driven research vs. theory-driven research. You suggest using researcher judgment to classify variables into groups and aggregate the results. This however requires that choices be made, choices that could be questioned. I prefer the more agnostic approach. Often, these employment variables were not that strongly correlated, owing to complex definitions of who is and isn't included in the categories. For instance, unemployment might only include those that can work, thus excluding the pensioned and the disabled. Or it might include only those receiving benefits (and thus not housewives/husbands). Combining variables risks missing importance differences between the variables.

Hierarchical extraction
One could use hierarchical extraction of S. In fact I have been experimenting with this but not published much on it. The topic opens up a large number of methodological questions that require quite a bit of work to answer. I have not had the time yet to seriously examine all of them and thus I prefer not to use this approach. It may change in the future.

See, however: http://openpsych.net/forum/showthread.php?tid=264 This paper is mostly a reply to your comments on this topic in another paper (commentary to our target article in Mankind Quarterly; the paper is currently not publicly available).

Causality
One could make a broader factor "well-being" as you did in the 2010 paper (Pesta et al). However, I want to align the integrate the research with that from behavioral genetics and differential psychology looking at the causality from individual differences to educational, economic, criminologic and medical outcomes. The reasoning is that the same general causality that holds in that area holds at the aggregate level: countries, states, communes, departments, prefectures, cities, etc.. This is my working hypothesis, not something that is definitely established.

For this reason I classify S as an outcome variable and cognitive ability as a causal predictor. It is likely that some backwards causation exists for the poorer regions of the world, but most will be forwards (my guess).

Language
The entire paragraph is "A composite dataset was created by merging the two datasets, yielding 63 variables in total. For identification, “_A” and “_B” were added to the variables names, where the first indicates that the data is from Kura and the second that it is from myself.".

This seems grammatical and understandable to me.

References
Hunt, E., & Sternberg, R. J. (2006). Sorry, wrong numbers: An analysis of a study of a correlation between skin color and IQ. Intelligence, 34(2), 131–137. http://doi.org/10.1016/j.intell.2005.04.004

Pesta, B. J., McDaniel, M. A., & Bertsch, S. (2010). Toward an index of well-being for the fifty US states. Intelligence, 38(1), 160-168.

Update
Version 10 (2016-01-30 10:47 AM) has been uploaded to OSF. It features the footnote mentioned above and no other changes.

https://osf.io/zfw38/
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#17
Having already been commented upon by several reviewers, the paper's analysis seems pretty comprehensive at this point. In addition, the paper reads relatively well; there is no need for any major grammatical or stylistic changes. However, I would ask Emil to address the following points, following which I believe the paper will be ready for publication.

1. Table 1 would be easier for the reader to interpret if the variables were ordered by strength of correlation (from largest negative to largest positive, say).

2. Analogous to Emil's finding that many of the correlations between S variables and cognitive ability became more sensical after conditioning on population density, Lynn (1979) reported a positive correlation between IQ and crime rates across regions of the British Isles, which then fell to zero when conditioning on urbanisation. He noted:

"The positive correlation between crimes rates and mean population IQ (r = + 0.51) is surprising in view of the many findings of a negative relation among individuals... When urbanization is partialled out the correlation between crime rates and mean population IQ drops to zero. Perhaps this is the true relationship between crime and intelligence."

Perhaps Emil would like to mention this.

3. Underneath Figure 7, Emit notes, "Okinawa is an outlier, but it is reasonably close to the regression line”. However, eyeballing the graph suggests to me that Okinawa may not only be an outlier, but also an influential point––at least to some extent. It would be worth reporting the correlation with and without Okinawa included in the sample.
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#18
Noah,

Thank you for reviewing the paper.

1)
Quote:Table 1 would be easier for the reader to interpret if the variables were ordered by strength of correlation (from largest negative to largest positive, say).

I have updated the table so that they are sorted (highest first). The rationale for the other setup was to make it easier to see which variable was from which dataset. I guess that has little interest to the reader.

2)
Quote:Analogous to Emil's finding that many of the correlations between S variables and cognitive ability became more sensical after conditioning on population density, Lynn (1979) reported a positive correlation between IQ and crime rates across regions of the British Isles, which then fell to zero when conditioning on urbanisation. He noted:

"The positive correlation between crimes rates and mean population IQ (r = + 0.51) is surprising in view of the many findings of a negative relation among individuals... When urbanization is partialled out the correlation between crime rates and mean population IQ drops to zero. Perhaps this is the true relationship between crime and intelligence."

Perhaps Emil would like to mention this.

I have added a note about this in the Discussion:

Finally, during the review, Noah Carl pointed out that Lynn (1979) employed a similar control and observed that this can have large effects (see also Kirkegaard (2015g) for a reanalysis that study).

3)
Quote:Underneath Figure 7, Emit notes, "Okinawa is an outlier, but it is reasonably close to the regression line”. However, eyeballing the graph suggests to me that Okinawa may not only be an outlier, but also an influential point––at least to some extent. It would be worth reporting the correlation with and without Okinawa included in the sample.

Added:

Okinawa is an outlier, but it is reasonably close to the regression line. If Okinawa is excluded, the correlation decreases to .54 [95CI: .29 to .72].

So, yes, influential, but not solely responsible for the result.

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John Fuerst went over the paper and suggested formulation changes. I have followed his advise in most of the cases. The paper should now read somewhat better.

I have changed the reference system to APA.

New version uploaded: https://osf.io/zfw38/
Version #11.
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#19
I am happy with the revisions Emil has implemented, and approve the paper for publication.
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#20
Paper published.

http://openpsych.net/OQSPS/2016/04/inequ...-analysis/
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