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We at the MN State Demographic Center are pleased to partner with journalist Michael Olson to offer this unique opportunity to put some of the data we work with every day in the hands (and the code and the artistry) of people who can help us see it through new eyes. For the inaugural data opener, we have decided to focus upon educational achievement , which is the central theme of our forthcoming report, “A Time For Talent.”
This workbook contains some of the data found in that report—showing educational attainment among Minnesotans by age, race/ethnicity, gender, nativity (U.S.-born or foreign-born), etc. Plus a wealth of other data (25 tables to be exact) about education and how it can change people’s lives.
Some themes you may wish to explore:
* How does educational attainment differ by characteristics?
* Will the younger generation of Minnesotans, which is also the most diverse, be less likely to graduate from high school than the generations before them?
* How is educational attainment associated with poverty, median income, or health insurance?
* How well educated are Minnesota’s immigrant (foreign-born) population, compared to our native-born population?
* What degrees are college graduates earning? Do they match the needs of our economy?
These are some ideas to get you started, but feel free to go in any direction your data muse leads.
A word of caution about margins of error: The data in this workbook originated from surveys, which means we have a pretty good idea, but can’t be completely precise. So for example, you may see that , in 2010, 31.5% of Minnesotans age 25+ had a bachelor’s degree or higher. However, this percentage also has a margin of error (+ or - 0.4%). That means that we believe the actual percent is between 31.1% and 31.9%, the range created by adding and subtracting 0.4% from 31.5%. You do not need to worry much about margins of error, except when making comparisons between groups. If the percentage for two groups appears different in the estimate column, but the range created by the margins of error is overlapping, we can’t be certain that any true difference exists. So be careful not to make strong conclusions when the data are too close to call.
Also pay attention to the titles in cell A1. That will give you a sense of the “universe” (everyone the data in the tables are about), which is not always the total population. It may be only a certain age or racial group, for example.
Here are some recent reports and additional data that have shaped our thinking about how educational attainment is critical to the emerging economy. You may wish to read them for background. Feel free to reference them in whatever products you create.
* Alliance for Excellent Education, High School State Cards (Updated March 2012):
If you have questions about how the “MN Data Opener” works, please contact Michael Olson at firstname.lastname@example.org. If you have questions about how to interpret the data tables in this workbook, please contact Andi Egbert at email@example.com.
Thanks, and good luck! We can’t wait to see what you create.
MN State Demographer
Research Analyst Specialist