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[ODP]Putting Spearman’s Hypothesis to Work: Job IQ as a Predictor of Employee Racial

#21
(2016-Jun-05, 02:35:40)bpesta22 Wrote: Could we perhaps summarize (beyond the revisions I already agreed to make in my Word-file replies) what I must do next?


To be clear: I approve the paper, conditioned on the agreed up revisions.
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#22
Quote:REPLY: Would it be ok to include “(but, see, <your link>)” immediately after the word “version” in our quote above? If not, we can delete this entire section.
Quote:No. Please see the discussion about your previous submission. The BDS does not have twice the g-loading of FDS. Dalliard and myself compiled several studies to show this, yet you made the same exact claim in your next submission. I find that odd.

That section shall be removed from the revision.

Emil, previously Wrote:This (the slightly higher mean IQ among the occupations) corresponds to the low correlation found between being out of a job and IQ at the individual level. You could back-estimate this correlation using this mean. Just a minor check.


See my reply to Chuck re my thoughts on additional analyses like these. I will defer, if that's what consensus here demands.
Quote:You misunderstood. I proposed that you use the average of the BLS 2014 and 2012 values to remove some of the slight 'measurement error'.
I did this in my replication.
I admit to not being a statistician, but to me, I found it more compelling to show that the demographic percentages were stable across two time periods than to get slightly more precise estimates by averaging them.
Quote:
1)
Not controlling for known confounds (interests in this case) which you already have the data for is not really defensible. After all, you are interested in the effect of cognitive ability itself, not whatever it is that it happens to be correlated with. If you use correlations, you will get a confounded estimate of the influence of cognitive ability itself.

I misinterpreted what you meant last time, and see that you have done the analyses below. How shall I proceed?

Quote:
Your correlations are effect sizes, yes. However, I asked for "the effect sizes of the prior research so readers can see whether the effect sizes are similar".

You present some new results. What the readers need to know is whether they fit in size with the previous results. For instance, if you find r = .20 and previous studies have found r = .95, something is wrong somewhere.

The data variable (complexity) was correlated with mean IQ at .86 in your study. You cite:

Gottfredson, L. S. (1986). Occupational aptitude patterns map: Development and implications for a theory of job aptitude requirements (Monograph). Journal of Vocational Behavior, 29, 254-291.

Gottfredson, L. S. (2003). g, jobs, and life. In H. Nyborg (Ed.), The scientific study of general intelligence: Tribute to Arthur R. Jensen (pp. 293-342). New York: Pergamon.

However, I could not find any complexity x mean IQ correlation in these papers. She does give job mean IQs and presents factor analysis results of job attributes, but does not appear to actually correlate them. Maybe I missed the number somewhere?

I will look into this further. It’s possible Gottfredson didn’t report any.
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#23
Bryan,

Some comments.

Bryan Wrote:I admit to not being a statistician, but to me, I found it more compelling to show that the demographic percentages were stable across two time periods than to get slightly more precise estimates by averaging them.


There may be some misunderstanding here. Sorry if I have not been entirely clear.

My point is that:
1) There is year-to-year random variation in the BLS race% data.
2) By correlating data from different years, one can see how large this variation is. You did this and found that there is little variation (r's = .87 to .88).
3) By averaging data across years, the random variation cancels out in accordance with the Spearman-Brown formula. This should improve the true score variance (i.e. the signal in the noise).

So, I'd like you to use the average values of years 2012 and 2014 for the BLS data, and note their intercorrelation (as you already do).

Bryan Wrote:I misinterpreted what you meant last time, and see that you have done the analyses below. How shall I proceed?


I think that your correlations are good to include. They are the simplest test one can do of this hypothesis with these data.

Furthermore, I think that you should include the regression results. I.e. have each of the race% as outcome (dependent) variables, and use mean IQ, people, and things as predictors (independents). This gives a less confounded view on the effect of GCA alone (which was in fact pretty stable across racial groups).

I don't think the path model results are interesting enough to add.

Bryan Wrote:I will look into this further. It’s possible Gottfredson didn’t report any.


Please do. However, it might simply mean that your study is more novel than you thought.

Perhaps contact Linda Gottfredson to hear. Perhaps she is familiar with other literature on this topic.

---

As Fuerst notes, it is a good idea to note that the White, Black, Asian groups in this study include Hispanics. Hispanics mostly self-identify as White or Other, so these are the groups that are 'contaminated' and harder to interpret results for.

https://en.wikipedia.org/wiki/Hispanic_a...icans#Race

The BLS report states (p. 59):

Quote:White, Black or African American, Asian, American Indian and Alaska Native, and Native Hawaiian and Other Pacific Islander. In accordance with the Office of Management and Budget guidelines, these terms are used to describe the race of people. Beginning in 2003, people in these categories are those who selected that race group only. Those who identify multiple race groups are categorized as people of Two or More Races. (Previously, people identified a group as their main race.) People who identified themselves as Asian are further classified as Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, or Other Asian. The Other Asian category includes individuals of group not listed—such as Pakistani, Hmong, and Cambodian— and those who reported two or more Asian groups. Estimates for American Indians and Alaska Natives, Native Hawaiians and Other Pacific Islanders, and people of Two or More Races are not shown separately in all tables because the number of survey respondents is too small to develop estimates of sufficient quality. In the enumeration process, race is determined by the household respondent. More information on the 2003 changes to questions on race and Hispanic ethnicity is available on the BLS website at http://www.bls.gov/cps/rvcps03.pdf.

Hispanic or Latino ethnicity. This refers to people who identified themselves in the enumeration process as being of Hispanic, Latino or Spanish origin. These individuals are further classified by detailed Hispanic ethnicity. Previous versions of this report presented data for the following detailed Hispanic ethnicity categories: Mexican, Puerto Rican, Cuban, Central and South American, or Other Hispanic or Latino. The latter two categories were expanded in 2014 into additional categories: Central American, which includes the two subcategories of Salvadoran and Other Central American (excluding Salvadorans); South American; and Other Hispanic or Latino, which includes the two subcategories of Dominican and Other Hispanic or Latino (excluding Dominicans). People whose ethnicity is identified as Hispanic or Latino may be of any race. More information on the 2003 changes in questions on race and Hispanic ethnicity is available online at http://www.bls.gov/cps/rvcps03.pdf

---

One should also do the analyses for Hispanic%. It is not important, statistically, that this group overlaps with the others because you are not doing a simultaneous analyses of the race%'s.
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#24
(2016-Jun-08, 15:27:47)Emil Wrote: Bryan,

So, I'd like you to use the average values of years 2012 and 2014 for the BLS data, and note their intercorrelation (as you already do).
...
Furthermore, I think that you should include the regression results. I.e. have each of the race% as outcome (dependent) variables, and use mean IQ, people, and things as predictors (independents).
....
Perhaps contact Linda Gottfredson to hear. Perhaps she is familiar with other literature on this topic.
....
As Fuerst notes, it is a good idea to note that the White, Black, Asian groups in this study include Hispanics.
....
One should also do the analyses for Hispanic%. It is not important, statistically, that this group overlaps with the others because you are not doing a simultaneous analyses of the race%'s.


Emil,
For sake of transparency -- I've asked this of other reviewers, too -- could you clarify which changes you absolutely require for approval versus which you recommend but do not insist upon? Thanks.
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#25
(2016-Jun-09, 20:55:49)Chuck Wrote:
(2016-Jun-08, 15:27:47)Emil Wrote: Bryan,

So, I'd like you to use the average values of years 2012 and 2014 for the BLS data, and note their intercorrelation (as you already do).
...
Furthermore, I think that you should include the regression results. I.e. have each of the race% as outcome (dependent) variables, and use mean IQ, people, and things as predictors (independents).
....
Perhaps contact Linda Gottfredson to hear. Perhaps she is familiar with other literature on this topic.
....
As Fuerst notes, it is a good idea to note that the White, Black, Asian groups in this study include Hispanics.
....
One should also do the analyses for Hispanic%. It is not important, statistically, that this group overlaps with the others because you are not doing a simultaneous analyses of the race%'s.


Emil,
For sake of transparency -- I've asked this of other reviewers, too -- could you clarify which changes you absolutely require for approval versus which you recommend but do not insist upon? Thanks.


Thanks for the query, Chuck. In the spirit of this, I'm ok with doing all of Emil's analysis, except including Hispanics.
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#26
Of my comments, I require:
1. The regression analyses to control for the interest differences.

I recommend but do not require:
1. Using the average of BLS 2012 and BLS 2014 values.
2. Doing the Hispanic analyses.
3. Contact Linda Gottfredson to hear if someone else correlated job complexity with mean IQs by job/occupation.

After (1), I have no further objections and will approve of publication.

---

I'm curious as to why you don't want to do the Hispanic one? In fact, the Hispanic one is the only one that uses a clear definition! It's the White, Black and Asian which are confounded with Hispanics (mostly White).
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#27
(2016-Jun-10, 18:29:06)Emil Wrote: Of my comments, I require:
1. The regression analyses to control for the interest differences.

I recommend but do not require:
1. Using the average of BLS 2012 and BLS 2014 values.
2. Doing the Hispanic analyses.
3. Contact Linda Gottfredson to hear if someone else correlated job complexity with mean IQs by job/occupation.

After (1), I have no further objections and will approve of publication.

---

I'm curious as to why you don't want to do the Hispanic one? In fact, the Hispanic one is the only one that uses a clear definition! It's the White, Black and Asian which are confounded with Hispanics (mostly White).


Hello,

I will do the regressions and all other changes suggested earlier. I just can't get past checking a box that says white, e.g., and then also a box that says Hispanic. That plus things summing to greater than 100% concerns me.
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#28
(2016-Jun-12, 03:18:52)bpesta22 Wrote:
(2016-Jun-10, 18:29:06)Emil Wrote: Of my comments, I require:
1. The regression analyses to control for the interest differences.

I recommend but do not require:
1. Using the average of BLS 2012 and BLS 2014 values.
2. Doing the Hispanic analyses.
3. Contact Linda Gottfredson to hear if someone else correlated job complexity with mean IQs by job/occupation.

After (1), I have no further objections and will approve of publication.

---

I'm curious as to why you don't want to do the Hispanic one? In fact, the Hispanic one is the only one that uses a clear definition! It's the White, Black and Asian which are confounded with Hispanics (mostly White).


Hello,

I will do the regressions and all other changes suggested earlier. I just can't get past checking a box that says white, e.g., and then also a box that says Hispanic. That plus things summing to greater than 100% concerns me.


Also, may I ask why you didn't include Data in with the regressions you want me to report?
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#29
Hi Bryan,

Quote:I will do the regressions and all other changes suggested earlier. I just can't get past checking a box that says white, e.g., and then also a box that says Hispanic. That plus things summing to greater than 100% concerns me.

It is of no statistical importance that the groups do not sum to 100% because you do not use multiple groups at the same time. In fact, one could create pseudo-groups if one wanted to. As long as they have a known mean IQ, one can do this type of analysis. For instance, one could create a group of persons who are all descended from at least one parent with a college degree. Then another group with two parents with college degrees, and another group with 0 parents with college degrees. These three groups sum to more than 100%, but they would work fine for doing this analysis.

Of course, these would not be racial groups. But then again, there are other things to research than race. :)

Quote:Also, may I ask why you didn't include Data in with the regressions you want me to report?

Data is more or less the same as mean IQ requirement (rated job complexity). However, since we have the actual mean IQ by occupation, using both would create strong multicollinearity which causes problems using OLS regression. Here's an example:

With IQ:

Code:
> lm_white = lm("white ~ iq + people + things", data = d_jobdata) %>% MOD_summary(runs = 200)
> lm_white
$coefs
        Beta   SE CI.lower CI.upper
iq      0.39 0.09     0.20     0.57
people -0.19 0.10    -0.37     0.00
things -0.09 0.08    -0.26     0.07

$meta
            N            R2       R2 adj. R2 10-fold cv
       124.00          0.26          0.24          0.19


With data:

Code:
> lm_white = lm("white ~ data + people + things", data = d_jobdata) %>% MOD_summary(runs = 200)
> lm_white
$coefs
        Beta   SE CI.lower CI.upper
data   -0.44 0.09    -0.62    -0.27
people -0.16 0.09    -0.34     0.02
things -0.08 0.08    -0.24     0.08

$meta
            N            R2       R2 adj. R2 10-fold cv
       124.00          0.29          0.28          0.21


With both:

Code:
> lm_white = lm("white ~ iq + data + people + things", data = d_jobdata) %>% MOD_summary(runs = 200)
> lm_white
$coefs
        Beta   SE CI.lower CI.upper
iq      0.07 0.15    -0.23     0.37
data   -0.39 0.15    -0.69    -0.09
people -0.15 0.09    -0.34     0.03
things -0.08 0.08    -0.24     0.08

$meta
            N            R2       R2 adj. R2 10-fold cv
       124.00          0.30          0.27          0.19


So, using both does not improve the predictive validity. Cross-validation R2 is .19 for the model with both, and .19 and .21 for the other two (trivial difference). In this case we can see that what happens is that data (job complexity) takes the validity from IQ. This happens because it just so happens that the relationship with data is slightly stronger (.44 vs. .39). It might as well have been the other away around (the sample size is small). When two predictors are highly correlated, results from OLS regression are unstable and have large standard errors.

You can see the standard error inflation in this case. When the model includes only one of the predictors, the standard error is .09. When they are both included, the standard error increases for both by about 67% to .15.
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#30
Hello,

I think we addressed all mandated concerns, and I did try to proofread carefully. If anyone finds a typo, please let me know.

Thanks for considering our work,

Bryan


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