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U.S. Ethnic/Race Differences in Aptitude by Generation

Quote:Maybe you could specify how you would like the tables to be presented so that I don't run into this problem again. For example, can I screen shot and paste tables? Can I use colors?

Meng Hu apparently doesn't like screenshots. I don't mind them. I also don't mind colors. Perhaps we need some table conventions for the journals? Just conventions not strict rules.
(2014-Jul-18, 01:02:45)Chuck Wrote: Imagine we had 5 NAEP Reading tests. And 2 of them showed no DIF and no measure non-invariance. Imagine that the H/W difference on these 2 tests was 0.65. This would provide evidence that there was a "true" population level latent H/W ability difference of 0.65 SD, no? Now imagine we had 3 other NAEP tests for which DIF and measure non-invariance were found, but adjusted scores were not presented. Imagine that these 3 tests also showed an average score difference of 0.65 SD. Knowing nothing else, we can infer that the psychometric bias on the latter 3 tests is not accounting for much of the average 0.65 H/W score difference because the evidence show that there is, in fact, a 0.65 SD latent ability difference.

I understand what you mean, And i think I said something like this earlier. My point is that your argument is correct only if the first 2 NAEP (invariant; d=0.65) and last 3 NAEP (non-invariant; d=0.65) test have the same or similar properties (note; if that happens, there is some indirect evidence that the bias is not cumulative, while I'm talking about cumulative bias, since non-cumulative bias is generally irrelevant when it concerns IQ). Why I said earlier that most people (including practioners) do not understand what measurement equivalence/invariance is, has to do with test composition. When MI is violated, this means that the group difference differs depending on the kind of subtest/items the test is composed of. This is probably why Wicherts said something like "scores are not comparable" when MI is not fulfilled. It's not 100% wrong, but it's highly misleading.

However, when you are talking about tests tapping into different cognitive dimensions (e.g., reading vs. math, achievement vs. IQ) the assumption that the tests have similar properties is very likely to be violated. And in this case, generalizability becomes impossible.


Oh, concerning the median/mean issue...

You can use the following


When you do this, the d gap for blacks are 1.05, 0.85, 1.00, while using means you have 0.98, 0.80, 0.98.
So they get a little bit bigger. Means the results are skewed towards 0. Are those school samples? Because if there is ability correlated drop-out (a known phenomenon), then samples of older school children should have smaller gaps because the less bright Africans (and some Europeans too) tend to drop out.
Hmm, let me see. For hispanic, means are 1.02, 0.68, 0.56, and for medians, 0.90, 0.68, 0.56. For asian, means are 0.10, -0.21, -0.19, and for medians, 0.05, -0.20, -0.16. In other words, it seems to make an effect only for 1rst generations. The numbers for means differ from the last version of the paper, because (I assume) the version of the data set I have is outdated and modifications should have been made so far.

Below is, again, based on the spreadsheet data I have.

For blacks, the values are wide, between 0.50 and 1.60. I'm not sure there is a particular outlier here.
For hispanics, it's also difficult to tell because the HSLS2009 has 0.37 while other values range between 0.60 and 1.80-1.90.
For asians, outliers should be the Add Health (0.82) and GSS (0.85). Interestingly, those surveys have small samples for asians.

I made the corrections noted by Emil; I checked over the tables and made corrections where needed. I added medians and noted: "On a reviewer’s request, median d-values were also reported in Table 2."

Regarding MH's point, there is nothing I can say more. Richwine's analysis of the CNLSY PIAT provides evidence that correcting for bias does not substantially reduce the (math) d-values. MH thinks that this is irrelevant because the tests used in my meta-analysis are "too different". But PIAT MATH is a good measure of math ability and most of my tests (14/18) involved Math tests.

BPSS-- SAT/ACT (Math + Reading)
NPSAS2012* -- SAT/ACT (Math + Reading)
NPSAS2008* -- SAT/ACT (Math + Reading)
TIMSS Grade 4 2007 -- (Math + Science)
TIMSS Grade 4 2011-- (Math + Science)
TIMSS Grade 8 2007 -- (Math + Science)
TIMSS Grade 8 2011 -- (Math + Science)
PISA 2009 -- (Reading + Math + Science)
PISA 2012 -- (Reading + Math + Science)
NAAL 2003 -- (Numeracy + Literacy)
PIAAC 2012 -- (Numeracy + Literacy)
NLSF -- SAT/ACT (Math + Reading)
HSLS 2009 -- (Math test)
NELS88 -- (Math test)

That said, I am willing to allow MH to rewrite the passage in a way that works for him.


Now, this apart, there is only one other issue. Most of my tests are "crystal" or "informational". If this is a problem, I could note PISA creative problem solving results and quickly compare these to the PISA R/M/S results. I would have to have MH compute the d-values, though. I don't recall there being a large difference, though.

Attached Files
.   U.S. Ethnic-Race Differences in Aptitude by Generation - An Exploratory Meta-analysis (John Fuerst 2014) (07182014) (2). (Size: 1 MB / Downloads: 98)
.   U.S. Ethnic-Race Differences in Aptitude by Generation - An Exploratory Meta-analysis (John Fuerst 2014) (07182014) (2). (Size: 91.07 KB / Downloads: 87)
.xl   U.S. Ethnic-Race Differences in Aptitude by Generation - An Exploratory Meta-analysis (John Fuerst 20147172014) (ODP).xl (Size: 346.06 KB / Downloads: 97)
I have no more comments. I approve of publication. Remember that one needs 4 approvals now due to increased size of the review team.
I approve the publication, too, but I have a few more quibbles that you may want to address:

1) "Submitted to Open Differential Psychology June 29nd, 2014"


2) A better title would be "Ethnic/Race Differences in Aptitude by Generation in the United States: An Exploratory Meta-analysis".

3) "subpopulations within these broad categories need not perform as do the racial/ethnic groups do on average"

one "do" too many

3) For better readability, you should justify paragraphs so that both sides are aligned, and use a bigger font size for main headings as compared to sub-headings.

4) "They found that, relatives to stayers"

relative to

5) "Negatively selection, then, is not likely"

Negative selection

6) Regarding the IQs of different Asian American groups, you might want to cite Hsin & Xie (2014) (attached) who found that Filipinos and South Asians had lower math and reading test scores than whites nationally (see Fig. 4).

Attached Files
.pdf   Explaining Asian Americans’ academic advantage over whites.pdf (Size: 759.35 KB / Downloads: 791)
(2014-Jul-18, 22:57:09)Dalliard Wrote: 6) Regarding the IQs of different Asian American groups, you might want to cite Hsin & Xie (2014) (attached) who found that Filipinos and South Asians had lower math and reading test scores than whites nationally (see Fig. 4).

Thanks for the feedback, D. I spent the day adding 4 more TIMSS results to increase reliability (combining new and old). The results were effectively the same. Just a lot of extra work. As for Hsin and Xie, they present within school differences i.e., controlling for school fixed effects. OK. But I imagine that this also controls for cognitive ability. You end up effectively matching on a bunch of unobserved factors. Also, the ECLS results seem to be all over the place.
I think this paper greatly improved from the prolonged reviewing instead of the rapid approval from another reviewer in post #7.
I'm quite fond of my terse reviews. Lynn reviews in the same way. If you'd like me to review more thoroughly, however, I will do so.
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