Notice: Moving to new domain

The website is going to be changing domains this weekend. I’ll be moving to Dreamhost and away from WordPress.com. I will be making the move in order to accommodate some exciting new projects for the website. But, as these things go, there may be some service interruptions. Everything should be ready by Sunday, June 3rd.

Using Sublime Text 2 for R

My R interface has been pretty basic in the last few years. I have usually stuck to the R console. Yes, I’ve tried Emacs with ESS; a staple, but it is so unbearably antiquated that I always gave up on its significant learning curve. GUI packages–especially Rstudio–offer viable alternatives, but I feel the GUI lets me lose focus of the code. I have been envious of TextMate for Mac, but alas, I’m not a Mac user. Recently, though, I’ve moved to Sublime Text 2. With some nudging, I have been able to mimic the typical R console environment in the more-powerful Sublime Text program.

Agree with the point, but not the logic

Education scholar, Gary Rhoads, was the lead author on a report released by Center for the Future of Higher Education, an organization that Dr. Rhoads leads. The main thrust is the troubling decline in community college funding and it’s impact on student access. Specifically, that the reducing the budget has put caps on programs and created a conflict with the community college “open admissions” policies. However, in attempting to argue this, the report makes an odd claim: “Enrollment in community colleges across the country is plateauing and declining despite rising student demand.”

I agree with the thesis, but not the logic. Dr. Rhoads attempts to tie the recent enrollment declines to limited budgets. “Enrollment in community colleges across the country is plateauing and declining,” he argues, “despite rising student demand.” Dr. Rhoads seems insistent on this latter part, but here is where I get lost. Although demand is much higher than 5 years ago, many states have had declining enrollments since two years ago.

Data Nouveau: Ideas for Visualizations

Starting a project where you need to make a series of graphs or a visualization can be frustrating. One of the hardest tasks is to find a theme, a style which you want to use. Though minimalism is the dominate “style” in data visualization, there is a lot of approaches to any graphical approaches. So I’ve started a blog, Data Nouveau, to browse sources of influence for data projects.

It will mostly include graphs and data visualizations, but will also use other sources–such as art and movies–for ideas, such as color palette. Essentially, I hope it will serve as a sort of free style magazine for those starting new projects. I’m also accepting submissions of any interactive, static graphing style, data visualization project, or anything that might help someone choose a color palette, graphing style, or layout for their own project.

Measuring Transitions Into the Workforce as a Form of Accountability

My new publication in November’s IR Applications, which is published by the Association for Institutional Research:

This paper explores the relationship between student major and industry of employment and its application to higher education accountability. Data provided by statewide longitudinal data systems (SLDS) have enabled state educational agencies and colleges to follow students into the workforce. While most studies have focused on wage outcomes, this study shows how to use SLDS data to understand the correlation between major and industry. The transition into the workforce is an important outcome since it is an assessment of a college’s ability to develop specific, targeted sectors of the economy. We use SLDS data from Iowa to follow community college alumni from 2002 through 2008.

Find it here.

Poverty Estimates for Children by Iowa School Districts

U.S. Census Bureau released poverty and income estimates for small areas, including counties and school districts, in an interactive map. Here is a picture of poverty estimates for children (between 5 and 17 years-old) by Iowa school district.

Darker areas represent a higher proportion of kids in poverty. Pockets of high poverty, 30%+, are predominately in southern Iowa. Not surprisingly, educational attainment for adults in these same school districts also tend to be lower.

More on Midwest Urbanization

Kyle Munson from The Des Moines Register wrote a featured article on the changing population base in the Midwest based on new Census data. Below is the graph from the article:

The lower-left graphic–dependence on manufacturing jobs–is one of the first items I mention to folks when discussing Iowa’s economy. Rural areas rely more on manufacturing than agriculture as a percentage of employment and as a percentage of income after the 1982 recession–most assume it’s agriculture. The Federal Reserve Bank of Chicago noted the greater reliance of non-metro areas on manufacturing in the past decades. In the graph below, the orange areas represent counties which the share of manufacturing income is greater than the statewide average. The white areas are metropolitan areas. In 2009, two-thirds of non-metro counties are highlighted in orange.

Hollowing Out?

Well-written books like Hollowing Out the Middle and Caught in the Middle have noted the net outward migration of education populations to urban areas. The Federal Reserve Bank of Chicago shows that real per capita income shows there is not as much disparity in real income per capita between the upper Midwest metro areas and non-metro areas as one might guess.

Iowa, where research in Hollowing Out the Middle was based, has transformed from agriculture as a final good to agriculture as an input into manufacturing. The greater value-added from manufacturing–such a Marxian concept, I know–has helped keep real income higher in the non-metro areas.

This data does not negate the other concerns raised by the authors of the aforementioned books, but non-metro areas can provide a base for manufacturing that may not be easily outsourced.

Earnings and Unemployment by Major

Wall Street Journal posted data from the venerable Center on Education and the Workforce on earnings and unemployment by college major.

There is a relative floor at $40,000 with a wide variation of unemployment (poor clinical psychology). There is a negative correlation between earnings and unemployment rate, but it might be too presumptuous to presume that high-paying majors get their cake and eat it too. Depending on the methodology, the higher earnings might simply reflect the fact they have a job.

Battling Bad Science

A few important statistical concepts are mentioned here, including observational versus randomized trails (randomization apparently is mentioned in Daniel 1:12); causality; publication bias; and a “funnel” plot.