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wisconsin_teachers

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b20db9c3668878ff03142c01f4c9fa420a1f7f00

more minor stuff

MMichaelChirico committed 8 years ago
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d2513cfdcc7ce7bdfe7172747347467484b69bbc

simplify raw data cleaner, add self-created dictionary file to repository for reproduction, update README

MMichaelChirico committed 8 years ago
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7879f33866297771bc7ca9983fc2dc326a0af65d

more copy editing; add README

MMichaelChirico committed 8 years ago
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76dfb5f87572daea68481ebc5b458c223c8450a4

progress on final tuning of paper

MMichaelChirico committed 8 years ago
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0e9e41309d11981a6e6afc30fb0948dd9a67db4d

facilitate automation

MMichaelChirico committed 8 years ago
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subject area heterogeneity

MMichaelChirico committed 8 years ago

README

The README file for this repository.

This repository provides reproduction code for the paper Teacher Turnover in Wisconsin, which can be downloaded here. The paper was compiled with rmarkdown through knitr; the .Rmd document for the paper is this one.

Raw Data

There are three public sources of data for this paper:

  1. Wisconsin Department of Public Instruction (DPI) collects and releases annual WISEstaff PI-1202 reports which give teacher- (more specificaly, assignment-) level snapshots of the full panoply of school employees in the state. These are available here. The R script raw_data_cleaner.R will download these files and do some baseline touchup to the raw files (which are mostly in fixed-width format) before producing easy-to-use .csv versions of the raw data. The script can be run at the command line with Rscript raw_data_cleaner.R; be sure to customize the wd.data variable to the local paths to which to download the data and write the .csvs.

  2. Wisconsin's WKCE test score data is also released by DPI at the district and school level. As per here, the WKCE is part of the WSAS battery of tests; the full set of these results can be downloaded through the WINSS historical data file repository here.

  3. The NCES Common Core of Data District- and School-level data files.

Pre-Paper Data Cleaning

turnover_paper.Rmd runs the turnover_data_cleaner.R script internally (which does some final data wrangling and runs the COBS routine, but relies on two scripts to be run beforehand:

  1. background_data_cleaner.R assembles school- and district-level files from the DPI and NCES; be sure to customize wds here as well, which tell the script where to find these raw data files and where to write the output.

  2. teacher_match_and_clean.R runs the teacher matching algorithm described in the paper's Appendix in order to create a panel of data from the DPI's cross-sections.

Feel free to file an issue or e-mail me for any further clarification/concerns.