Work Description

Title: Estimating Student Capital of Community College Student Populations Open Access Deposited

h
Attribute Value
Methodology
  • The dataset here is simulated, designed to mimic information about a cohort of degree-seeking community college students who aimed to transfer to a 4-year college. It contains information about the number of credits each simulated student earned and whether they dropped out, graduated with an associate's degree, or transferred to a four-year college. The dataset can be used as a template for running the associated R files.
Description
  • Student capital is the set of skills, traits, and resources that an individual can draw upon to be successful in school. With dropout rates around 50%, community college students often don't have enough student capital to achieve their goals. The R code in this dataset estimates the average student capital of a group of community college students using data on their total credits and academic outcomes. It also contains R code to create figures, as found in the paper "The Shape of Educational Inequality" by Quarles, Budak & Resnick.
Creator
Depositor
  • cquarles@umich.edu
Contact information
Discipline
Keyword
Citations to related material
Resource type
Last modified
  • 11/17/2022
Published
  • 05/19/2020
Language
DOI
  • https://doi.org/10.7302/attc-n529
License
To Cite this Work:
Quarles, C. L. (2020). Estimating Student Capital of Community College Student Populations [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/attc-n529

Relationships

This work is not a member of any user collections.

Files (Count: 4; Size: 52.2 KB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
readme.txt 2020-05-15 2020-05-15 2.69 KB Open Access
mlecens.R 2020-05-15 2020-05-15 12.7 KB Open Access
code_from_QBR_paper.R 2020-05-15 2020-05-15 18.1 KB Open Access
sample_data.csv 2020-05-15 2020-05-15 18.6 KB Open Access

DATE: May 13, 2020
TITLE: Code and sample data to accompany "The Shape of Educational Inequality" by Quarles, Budak & Resnick, published in Science Advances
AUTHOR: Christopher L. Quarles, chrisquarles@gmail.com

This repository contains four files:
-- readme.txt: The file you're reading now.
-- mlecens.R: This file contains the R code for estimating student capital in a population of students. It contains one function, mlecens, which performs right-censored maximum likelihood estimation to fit a distribution to a data set.
-- code from QBR paper.R: This contains the R code used to make (most of) the images and tables in the paper. Because our data is unavailable, all of the code will run on the sample_data.csv. If you want to make an image from the paper with your own data, you can just format your data like in sample_data.csv and then run the code in this file.
-- sample_data.csv: For privacy reasons, the data used in the paper is not available to the public. This dataset mimics the type of data used for the analysis. The dataset has 4 variables:
- credits_earned = # of credits earned by a given student, rounded to the nearest positive integer
- droppedout = FALSE if the student graduated or transferred, TRUE otherwise
- transferred = TRUE iff the student transferred to a 4-year college
- transnograd = TRUE iff the student transferred but didn't graduate

IF YOU JUST WANT TO CALCULATE THE STUDENT CAPITAL OF A GROUP OF STUDENTS:

- Make sure that your cohort is large enough. In simulations based on real data, the standard error of the estimated average student capital was roughly: SE = 150/sqrt(sample size).
- Also, make sure that a middling number of your students dropped out. Otherwise, you won't observe enough students' capital to make an accurate inference. I don't have a good rule of thumb here, but 20% or fewer dropouts probably won't work. Nor will >90% dropouts.
- Save your data in the same format as sample_data.csv, or you can copy and paste over the sample data. You only need two variables: credits_earned and droppedout. droppedout can be either TRUE/FALSE or 1/0.
- Make sure all the files are in the same directory.
- Make sure that you have the VGAM package installed. You can run install.packages("VGAM") or install it through Tools menu in RStudio.
- Run the following lines in R. You'll have to change the file name to match your file. (The sample file should give q=.9917 and mu_s=120.2.)

source("mlecens.R")
coldat <- read.csv("sample_data.csv")
q = mlecens(x=coldat$credits_earned, yc=coldat$droppedout) # This returns the "per-credit retention rate"
mu_s = 1/(1-q) # This returns the "average student capital", measured in credits.

Download All Files (To download individual files, select them in the “Files” panel above)

Best for data sets < 3 GB. Downloads all files plus metadata into a zip file.



Best for data sets > 3 GB. Globus is the platform Deep Blue Data uses to make large data sets available.   More about Globus

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.