Community Features and School Achievement Gaps

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# Community Features and School Achievement Gaps
## And some other stuff
### Daniel Anderson
### June 22, 2018

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# Introduction
* Much recent focus on open data in research generally
* Open data tend to be rare in educational research
  + Privacy concerns

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.Large[.bolder[.center[NCLB Required Publicly Available Data]]]

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* School-level data 
* Percent proficent in each of at least four proficiency categories
* Disaggregated by student subgroups

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# Illustrating one approach

* Simulate some data from two distributions
  + Group 1: `$n = 200$`, `$\mu = 200$`, `$\sigma = 10$`
  + Group 2: `$n = 500$`, `$\mu = 210$`, `$\sigma = 8$`

* Cut Scores: 190, 205, 215 .gray[.tiny[(totally made up)]]

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# Reardon & Ho Method<sup>.gray[.tiny[(2)]]</sup>
* Calculate the empirical CDF of each distribution
* Pair the ECDFs
* Calculate the area under the paired curve
* Transform it to an effect-size measure (standard deviation units)

.footnote[(2) Reardon, S. F., & Ho, A. D. [(2015)](http://journals.sagepub.com/doi/abs/10.3102/1076998615570944). Practical issues in estimating achievement gaps from coarsened data. *Journal of Educational and Behavioral Statistics*, *40*, 158–189.]

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![](sicss-pres_files/figure-html/sim_ecdf-1.png)
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![](sicss-pres_files/figure-html/sim_pp-1.png)
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# Transformation to effect size

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$$
V = \sqrt{2}\Phi^{-1}(AUC)
$$
]

* `$V$` = Cohen's `$d$` under the assumption of respective normality
* In this case, Cohen's `$d$` = -1.07 and `$V$` = -1.06.

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# Why does this all matter?

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# Purpose

* Evaluate `$V$` with empirical data

+ Compare estimates from full data and manually coarsened data. Compare results to Cohen'd `$d$` estimated with full data.

* Apply these methods to publicly available data to investigate between-school differences in achievement gaps

+ Specifically using geo-spatial mapping, overlaying data about the surrounding area
  
  
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# Results

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# Comparing `$V_c$` and `$d$`

Both continuous: `$r = 0.87/0.86$`.
![](anderson_pres_ncme18_files/img/vd_bivariate_continuous.png)

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.pull-left[
### Comparing `$V_d$` and `$d$`
.center[
`$r = 0.73/0.72$`
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<img src = "./anderson_pres_ncme18_files/img/vd_bivariate_discrete.png" height = 450/>
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### Comparing `$V_c$` and `$V_d$`
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`$r = 0.83/0.89$`
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<img src = "./anderson_pres_ncme18_files/img/vv_bivariate.png" height = 450/>
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## Distribution of differences: `$V_c$` and `$d$`

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  .center[
    `$\mu = -0.12, \sigma = 0.15$` 
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  .left[
    `$\mu = -0.16, \sigma = 0.16$`
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.pull-left[
### Comparing `$V_d$` and `$d$`
  .center[
    `$\mu = -0.10/-0.13, \sigma = 0.21/0.23$`
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]

.pull-right[
### Comparing `$V_d$` and `$V_c$`
  .center[
    `$\mu = 0.02/0.03, \sigma = 0.17/0.15$`
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<img src = "./anderson_pres_ncme18_files/img/vv_distributions.png" height = 425/>
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# Substantive Investigations

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# Achievement Gap Distributions

.grey[Reminder: School-level Distributions]

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# Alameda County
### Median Housing Cost

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# Alameda County
### `$n$` Identifying as Black

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# Alameda County
### `$n$` Income/Poverty Ratio > 2.0

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# Conclusions
* Effect size appeared well estimated from coarsened data
  + `$V$` was similar to Cohen's `$d$` with these empirical data
* The vast majority, but not all, schools had sizeable estimated achievement gaps
* Clear geographic clustering of achievement gaps was evident
  + Did Not appear to depend on the Census data investigated here

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# Really quick: These slides!

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# Reproducibility & Transparency
* I care a lot about it
* Slides are pretty (I think)
* Super customizable (css)
* Open source, transparent
* [slidex](https://github.com/datalorax/slidex) helps you transition from Microsoft Powerpoint!

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# Thanks so much!

[<i class="fab  fa-github "></i> datalorax](https://github.com/datalorax)

[<i class="fab  fa-twitter "></i> datalorax_](https://twitter.com/datalorax_)

[<i class="fas  fa-globe "></i> datalorax.com](http://www.datalorax.com/)

[<i class="fas  fa-envelope "></i> daniela@uoregon.edu](mailto:daniela@uoregon.edu)

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Slides available at [datalorax.com/talks/sicss-seattle](http://www.datalorax.com/talks/sicss-seattle)
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