A quick post on crime and test scores

To further extend my California test score series, I decided to compare crime rates at a county level to test scores (English Grade 11) for all test takers.

Violent Crime Rate

violent_by_ela_g11

[Un-weighted correlation: -0.38]

Homicide Rate

homicide_by_ela_g11

[Un-weighted correlation: -0.40]

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Sex differences within schools

I have long been of the impression that most of the gender gap in academic achievement is skewed towards lower SES groups and URMs, that much of it has much to do with a comparative lack of male engagement in primary and secondary school, especially with respect to home work compliance, so I decided to exploit my prior efforts to try to assess the accuracy of this view.  Of course this data isn’t broken out by race or SES per se, but the right side on the x-axis is generally higher SES and more white and asian (whereas the left side is generally more black and latino and generally lower SES).

English

English Grade 2

sex_school_ela_g2

English Grade 4

sex_school_ela_g4

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Some additional plots of within school achievement gaps

In a prior post I compared the performance of various ethnic groups directly against each other within the same schools.  That method has the advantage of reducing the effect of racial/ethnic composition from influencing the apparent relationship between each groups performance within school environments.  However, there aren’t always enough of both groups to clear the minimum reporting requirements for student privacy and the school average is a better indicator of “school quality” in some peoples’ minds.  Below I simply took the reported average for all students on the x-axis so that I could compare multiple groups at once.

I do not believe that this is a particularly good way to view the data, since it’s confounded by racial/ethnic composition and sorting by education levels (despite the obvious correlation), but if you do happen to think that the average score is a particularly good measure of “school quality” this approach might be eye opening.

I also plotted this same data for poor and non-poor blacks and whites and (all race/ethnic) scores by parental education level below.  You’ll need to scroll down a ways to see it though.

English

English Grade 2

school_ela_g2

English Grade 4

school_ela_g4

English Grade 6

school_ela_g6

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A brief analysis of suspension rates in California public schools

Since I have heard more than a few complaints about disparities in school suspension rates, I thought I’d extend my prior analysis of data from California public schools to better understand the patterns here.

UPDATE: I realized after the fact that I botched the asian rate data a bit because California reports Filipinos as a separate group for some strange reason, i.e., they’re not included in the reported suspensions/numerator, and my denominator uses the normal federal/OMB definition, which includes doesn’t put filipinos in a separate category.   Filipinos are about 1/4th of the (federal) Asian category in CA schools and probably have a higher suspension rate than east asians (which is extremely low).  I don’t think it’d alter the between group differences all that much, especially not in ordinal terms, but I don’t feel like re-doing these all plots right now, so keep that in mind.

Out-of-school suspension rate (“(OOOS”) by district (weighted)

oos_boxplot_linearlte2

OOSS by district (weighted), y-axis as log-10

oos_boxplot_log10

Observation: There are clear racial/ethnic patterns here that cut across many different school districts.

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Standardized tests correlations within and between California public schools

To exploit some of my prior work with California’s test score data, I decided to extend this analysis to SAT, ACT, and AP scores in the state of California, i.e., to compare the relationships of these different tests within and between schools.

Notes/Caveats:

  • To achieve more stable results with schools with small numbers of test takers I averaged 2-4 years worth of test score data together.
  • Unfortunately, AP results are not broken out by subject (some are much harder than others and there are apt to be different test taking patterns at different sorts of schools)
  • SAT/AP/ACT data is not available by race/ethnicity

ACT vs SAT (r=0.97)

sat_vs_act

[It’s almost like they’re testing the same construct…. :-)]

SAT by English grade 11 (r=0.84)

sat_composite_by_ela_g11

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Some quick plots pertaining to various “Asian” ethnic groups

Back in my earlier post on the effects of concentrated poverty, I mentioned that California provides test score data for various specific “Asian” ethnic groups (e.g., Chinese, Japanese, Korean, Hmong, Indians, etc) and that I might update my analysis with that information later.

To aid interpretability across multiple tests, I first converted all average scores into standard deviations above the non-hispanic white mean (both are weighted for the number of test takers in each school district to better approximate the actual individual distributions) and then plotted these as box plots for detailed ethnic groups, poverty status by race/ethnicity, and education levels.  All of these box plots are also weighted by the number of test takers in each group.

X = 0 should correspond to the weighted white mean across school districts in California (which ought to be very close to the individual white average state wide), ergo groups or parts of groups (e.g., IQR) that are on the right hand side are generally outperforming the white mean and vice versa for groups on the left hand side.

English Grade 2

boxplot_ela_g2

Math Grade 2

boxplot_math_g2

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What “blank slate”?

James Thompson of Psychological Comments blogged on a huge meta-analysis of 50 years worth of twin studies published in the journal Nature. (Full text copy here).  Although the study covered a wide range of phenotypes, several of them have particular relevance to intelligence, academics, and occupational outcomes.  The authors of the study also published an interactive website, MaTCH, where more detailed statistics can be browsed.

I screen captured traits some traits relevant to academic and occupational outcomes below:

Structure of Brain

Google Chrome (6)

Higher level cognitive function (general intelligence)

Google Chrome (3)

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Small update to my prior post on concentrated poverty

This is just quick update to my prior post on concentrated poverty.  I re-ran the California test score data at a school-level to compare within school black-white differences in test scores and converted all of the scores data to standard deviation units relative to (above) the non-hispanic mean by school, weighted by the number of test takers.  The pattern can be observed as early as 2nd grade and it is quite consistent for all major/mandatory tests.

 

Black vs White within school comparison

Grade 2 English

bw_ela_g2

 

Grade 2 Math

bw_math_g2

 

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On concentrated poverty and its effects on academic outcomes

According to a large and growing number of progressives, the achievement gap between “minorities” (especially blacks) and whites can be traced directly to the effects of “concentrated poverty”.  This implies that we cannot compare the outcomes of individual “middle class” blacks to whites of similar income because they don’t have the same amount of wealth, which would allow them to escape their poor neighbors, bad schools, or something along those lines.

Presumably the relationship between the actual neighborhood-level SES, as measured by poverty rates, income levels, education levels, etc, and academic outcomes should look something like this:

prog_model_1

prog_model_2

In other words, this achievement gap is presumably only found in areas of concentrated poverty, but those few families that manage to “escape” these particular bad environments converge on white outcomes or even close the gap entirely.

Having actually studied this data, I can tell you that reality looks more like this:


reality_model_1

reality_2

Put simply, there is no evidence to support convergence.   Broader outcome measures generally show a solidly linear relationship with these measures.  There is also much more overlap in material condition than the picture that most progressives present (curiously they sing a very different tune when they want to talk about these differences in other contexts).    Below I will present some evidence to this effect.

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A bit of data on the income stagnation and related arguments

The main difficulties I have with the “falling incomes” argument is that the country has changed dramatically over the past few generations and people are often unclear about what they mean by this.

Here are just some of the key changes/issues:

  • Women constitute a larger proportion of the workforce than they once did
  • Minorities, especially latinos, comprise a larger share of the population (households, families, tax units, etc)
  • Families (and thus households) are substantially smaller than before because younger generations are less likely to get and stay married and because they have fewer children when they do.
  • There has been a marked increase in education credential attainment.  Comparing a HS (only) grad from 1960 to 2015 doesn’t make much sense.
  • Some subgroups have changed their workforce participation behavior dramatically over the past few decades

Thus when we talk about directional changes in income it’s important to understand what we are actually concerned with.  Is it more along the lines of “the same groups in the same job working the same number of hours are earning less in real dollars” (i.e., people are getting paid less for the same sorts of efforts) or is it a broader statement like “households have less income than they did generations ago” (regardless of work, household size, race/ethnicity, gender, etc)?   The latter category is much easier to argue than the former.

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