As the masking requirements for public schools changed so does my map. This time I figured out how to link sources in the tooltips. Tooltips are apparently what you call the hover-over information. I like having clear sources for my visualizations it is important that I can retrace my steps and I would like others to be able to retrace my steps. This is especially important because I work with so many sources of data and my products often have a quick turnaround time.
Tag: County Data
Remiation Rates of Recent Maryland Public High School Graduates Enrolled at Maryland Public Institutions
Today I take a look at the remediation rates of recent Maryland public high school graduates enrolled at Maryland public institutions as published by the Maryland Higher Education Commission in their 2021 Data Book. According to data published by the Maryland State Department of Education, 57,622 students graduated from Maryland public high schools during the 2017-2018 academic year. According to MHEC’s remediation data set, 25,575 students enrolled at a Maryland public institution in fall 2018. Thus, less than half, 44%, of the public school graduates are reflected in the map below. So, the data may tell us less about the quality of public schools than first thought. I will have to see if I can drill down further to this data. My first step will be to look at the number of high school graduates from each county; however, since the percentage of students who are low-income is different in each county, the college-going behavior is likely not consistent.
I was a little surprised to see that 25,575 students enrolled in a public institution immediately after high school because according to the college pipeline data published by the Maryland Longitudinal Data System Center, 17,410 students enrolled in a public institution immediately after high school. I wonder if MLDS is better able to separate students enrolled in multiple institutions. Further, the MLDS dataset only is capturing “degree-seeking” students.
Notes About the Data
- Students may be enrolled at more than one institution. They are included in enrollment figures for each insituiton at which they are enrolled.
- Data include all degree- and non-degree seeking students enrolled in credit courses.
- Maryland residents are identified using their place of residence at the time of application to the insituion.
- Maryland public instiutitions include community colleges and four-year public colleges and universities.
- Recent Maryland public high school graduates are defined as those graduating from a Maryland public high school, identitifed using the College Board School Code, who graduated in the 2017-2018 academic year and first enrolled in higher education in Maryland in fall 2018. Analysis relies on high school graduation date and reporting of remedial assesment data; missing data for these variable may result in underreporting.
- Maryland residents whose county of residence is unknown are included in insitutuional-level remedial data, but excluded from reporting by county of residence.
- Salisbury University and St. Mary's College of Maryland do not offer remedial coursework.
Ploting the Data
Yesterday I looked at SAT scores in Maryland by county and income and for the incoming freshman classes of the public four-year institutions. Today I take a brief look at SAT scores by county and race/ethnicity from the same dataset published by the Maryland State Department of Education. I made a dot plot using Datawrapper with all the race/ethnicity categories available in the data set as well as “all students”. To be honest, the chart looks very busy and is rather hard to read. I changed the color scheme to reds and oranges to aid with distinguishing the categories, but it only helped a little. I could choose custom colors for each group, and would if I was intending to show this data to a wider audience, but since this is primarily for my own exploration of the data I decided I did not have the energy to make those choices today. I did decide to highlight the “all student” category to help with readability a bit.
An alternative visualization, and the one I have seen used at State Board of Education meetings, is a grouped bar chart. While I think that would work for smaller numbers of counties or race/ethnicities, I think that it is worst than the dot plot for a large amount of data. However, I may explore this visualization in the future.
SAT SCores by county and Race/Ethnicity
It is hard to draw any conclusions from this data. For one thing, I am unsure if this data represents public school students or all students who took the test from that county. For another thing, not all students take the SAT and different local school systems have different policies about pushing students to take the SAT.
Howard County stands out as having very high scores, for students of all races. From the data, I do not know if Howard County encouraged only high-performing students to take the SAT. I would be surprised if that was the case, but it is a possibility. Since there is information about the number of students that took the test, I might be able to infer the policy from that data or I might look at their website to see if they have a SAT policy.
Context
I have been reading through the Maryland Higher Education Commission’s Annual Data Book 2021. I am interested to see if I can learn any new insights by exploring different visualizations of the data. I am also teaching myself how to use data visualization/graphing software. Today I am taking a look at SAT scores statistics published in the databook as well as additional SAT scores published by the Maryland State Department of Education. The populations for all of these datasets are slightly different.
SAT Scores of Entering for High School Seniors 2020
According to the databook, the mean combined evidence-based reading and writing score and math for Maryland high school seniors in 2020 was 1029, slightly less than the nationwide mean of 1051. I have read in the past this is because a higher percentage of students in Maryland take the SAT than nationally, but I will have to find a citation that is true.
SAT Scores By COunty
On the Maryland Report Card, the Maryland State Department of Education publishes data about students. As part of the "college readiness data", average SAT scores are published. In addition to an average for all students from the local school system, the scores are disaggregated by a number of subpopulations including by low-income students, that is those eligible for free or reduced price meals. Low-income students scored lower on the SAT than all students in every county. However, the average scores were nearly identical for students from Dorchester County. At the SEED school, where almost all students are low-income, low-income students scored higher than all students.
There is a large range of scores between counties and between low-income students and all students in most counties. Howard County has the highest average score for all students and the second-highest average score., after Carroll County, for low-income students. In fact, low-income students from Carroll and Howard counties had a higher average score than the average score for all students from 14 counties, Baltimore City, and the SEED school.
I plan to examine the data for more subpopulations in the future. Including the best way to visualize the data.
SAT SCores of Entering Freshmen
The MHEC databook publishes the average, 25th percentile, and 75th percentile SAT scores of entering freshmen at the public four-year institutions. Institutions submit aggregated data on average SAT scores for all incoming freshmen. Some institutions do not require SAT scores for admission. Institutional score ranges are based upon those scores that were used as a basis for admitting students to the institution.
This chart shows the SAT scores of entering freshmen for the University of Maryland, College Park from the 25th to the 75th percentile
Data Range Chart
I have been testing the features of the Datawrapper data visualization tool. I started with maps, and still have mapping features to explore, but today I decided to try making a chart. In particular a data range chart. Like my other experiences with Datawrapper the range chart was easy to make. I just put the data into four columns, changed the percent data to just the number without the percent sign, saved the Excel workbook, and uploaded it to Datawrapper. With a few clicks and a bit of typing, I made the chart below which shows college enrollment for high school graduates.
Public Policy Questions about the CHart
Statewide 78% of public high school students enrolled in college either full-time or part-time as a degree-seeking or non-degree seeking at any point after high school graduation. The statewide average hides the variability in college enrollment in the State both by county and by family income. Only 44% of low-income high school graduates from Kent County enrolled in college, while 93% of non-low-income high school graduates from Howard County enrolled in a college. That is a difference of 49 percentage points!
All counties also have gaps in college enrollment between low-income and non-low-income high school graduates. In particular, Carroll and Queen Anne's counties have the largest gaps, 31 and 29 percentage points respectively. Talbot Couty stands out as having the smallest gap in college enrollment between low- and non-low-income high school graduates, 4 percentage points.
What I don't know from this data is what is a good level of college enrollment. Many studies have found that earning a bachelor's degree pays off financially for most people, but there are other pathways to financial and life fulfillment. This data captures some "non-traditional" education pathways such as certificate programs, so some "trades" are captured, but not all. Although I do not know what the college enrollment rate should be, I think that high school graduates should have equal opportunities to enroll in college. The huge variability in college enrollment rates might point to underlying factors that prevent some students from enrolling in college.
This data also does not tell me why high school graduates choose to enroll or not enroll in college. However, given that low-income students enrolled at a lower rate for all counties, money is likely a major factor. Other factors may be the distance to an affordable college, high school preparation, or community expectations.
Adding a Legend Caption to Reflect the Statewide Average
In my past posts exploring using Datawrapper maps I looked at the percentage of public high school graduates that enrolled in college and the percentage of those students that enrolled in college that earned a college degree by age 25. In this post I examine the percentage of high school graduates that earn a college degree by age 25.
Again I am using publically posted data from the Maryland Longitudinal Data Center. In their data set they did not post the percentage of public high school students that earned a college degree by age 25, I calculated it by dividing the number of high school graduates in a county by the number of students that earned a college degree from that county by age 25. From my understanding of the data this should work, but I haven’t done a deep analysis into the potential flaws of that process.
For this Datawrapper map, I added a legend caption to reflect the Statewide average. The caption appears directly above the legend. For now I this placement makes sense for a Statewide average. Otherwise, I used the same settings I have used with the other maps I have made thus far.
Public Policy Thoughts About the Data
According to this data, 40% of Maryland public high school students who graduated in 2011-2012 earned a college degree by age 25. There is not directly comparable data nationwide because Maryland Longitudinal Data Center only collects and publishes data about Maryland. The American Community Survey Data collects data about the educational attainment of individuals based on where they live, not where they graduated high school or where they were educated. According to that data 44% of Marylanders 25-44 years old, have earned a bachelor's degree or higher. Maryland is known as a State with a highly educated workforce. Is that because a large number of the State's high school graduates graduate college by age 25 or is it because educated workers move to the State? I do not know the answer, but I interested in exploring the data more.
What strikes me about the map is the difference in the percentage of high school graduates who earn a college degree by age 25 in Baltimore City, 16%, and Howard County, 60%. That is a huge difference, 44 percentage points. As someone who is familiar with Maryland I am not surprised by the difference, but the difference is striking. I want to dig deeper into the data. I want to see if low-income high school graduates from Howard County earn college degrees at a similar rate to high-income graduates in the county, or is the rate more similar to that of jurisdiction that have higher levels of poverty such as Baltimore City. I also want to look at the opposite for Baltimore City.
When looking back on the map showing percent college enrollees with a college degree by age 25, there is no jurisdiction that stands out as being radically different in this map, but I will need to dig deeper into the data.
If I can understand the potential reasons behind the data better I hope I will be able to give better policy advice.
Yesterday I made my first map using Datawrapper. I noted in the write-up of my experience that I could not figure out how to display the data as a percentage; I ended up displaying it as a decimal. I figured that it was possible because I had seen Datawrapper maps with percentages, so I was pretty sure I just needed to dig through the menus and options. Unsurprisingly, I quickly found a tutorial written by Datawrapper that explained how to customize a choropleth map, which included the information I needed to figure out how to display percentages.
Since the Datawrapper tutorial does not directly address displaying map data as a percentage, I will give a quick explanation to remember how to do it next time. Basically, it is a three-step process.
- Upload the data striped of the percent sign as you want it displayed, not in decimile form. For example if you want a data point to be 8% upload the data as 8. This will allow your data to be displayed in the map. If you add the percent sign the data will not be displayed.
- On the “visulize” step choose a percent number format (there are a few choices) from the legend menu. This will display the data as a percentage in the legend.
- Use “Tooltips” to add the percent symbol after the variable code for the data. It is the second box.
As I suspected, it was not difficult to get the data to display as I wanted. It just took some digging into the menus and options.
Now for a look at the map.
As with the first map I made, I used data published by the Maryland Longitudinal Data Center. For their visualization of the data, you have to pull up each county individually because they have rich data on students from each county. I like seeing all of the counties at once on one map. I used the same color scheme as with my last map, with red being the lower percentages and green being the higher percentages. For this map, I allowed the lowest percentage to be the darkest red and the highest percentage to be the darkest green. I have not yet given much thought to if that is the best way to display the data.
Another piece of data that I want to explore adding to the map is the Statewide average. I know that the Statewide average is 51%, that is, Statewide, 51% of public school students who graduated in 2011-2012 earned a certificate, associate's, bachelor's, or master's degree or higher by age 25. At least among the students captured in the dataset.
It stands out that only 22% of students from Baltimore City who enroll in college earn a college degree by age 25. What the data does not tell me is why. Since I am a curious person, I plan to dig into the data more to look for why this might be. I might not find the whole story, but I hope to find some elements of the story. Dorchester and Somerset counties also have low levels of degree attainment for students who enroll in college. For future maps I want to look at total college degree attainment by high school graduates, college degree attainment by FARMS and non-FARMS students, and by FARMS percentage of the entire county. I also want to see how the college enrollment rate correlates with the college graduation rate. While examining this data, I want to see the capabilities Datawrapper has to display the data.
As a public policy professional, I consume and manipulate a ton of data. Unfortunately, as a government employee, I often do not have access to the latest data tools. There tend to be many layers of approval and cost restrictions. Therefore, I am really excited to give Datawrapper a try here on my own website. It offers an extensive free service that I want to explore with public data.
Today I was able to follow the easy instructions to install the Datawrapper Plugin on my WordPress-based website. Even without much website experience, the installation was easy. Then I decided to build a map in Datawrapper using data I have been looking at published by the Maryland Longitudinal Data Center for one of their data dashboards. I am really excited about their data, but I feel like I have to manipulate it myself to understand it. The map below is me just dipping my toe into the water to understand the data and how to use Datawrapper.
The map shows the percentage of public high school graduates from 2011-2012 that enrolled in college at any point after high school graduation. I was looking at 2011-2012 graduates because I am also interested in the number/percentage that graduated from college by age 25. I will likely examine the college graduation data in a future post.
It was really easy to upload the data into Datawrapper, I already had it in an Excel file. The map below was created in a few minutes. I did have an issue with getting the data to display using the percent symbol. I will have to see if there is a way to get the percent symbol to display using the program.
I am not sure about the choices I made regarding the scale and colors for the map. I want to make a bunch of maps and graphs to publish here on my website to explore how best to represent the data.
Overall I am really excited that I was able to quickly make a professional-looking map. I am looking forward to testing its capabilities and seeing if I can learn any insights about the data.
