See you in the new year!

The DASIL Blog is signing off for now, as Grinnell College faculty, staff, and students are on their winter holiday break.  We will return to blogging in January.

In the meantime, check out our data visualizations in the menu on the left, or revisit some of our old posts.

We hope you’ve enjoyed our blog and we’ll return with even more great posts on data.

-DASIL Staff

Teaching Basic Quantitative Concepts with Visualizations

Data do not speak. As has famously been noted, data and especially data displays –whether maps, statistics, or word clouds– can lie or at least be deceptive. Access to easy methods for generating visualizations and analyses may be as dangerous as liberating, unless we are careful as both producers and consumers.

The following three maps all show exactly the same data, but look very different—due to the choices made in display.


The first map uses natural breaks in the data to separate categories. The second uses quartiles, a measure based on medians. For this the states are separated into 4 equal piles and the most densely-populated states are given the darkest color. Note how much variation this group exhibits. While the least dense two groups have only a small range, the range for the most densely populated is huge. Continue reading →

Using Cartograms as a Visualization Tool: An Interactive Tool for Exploring Fertility Rates

Cartogram of the world based on population size.

Cartogram of the world based on population size.

Cartograms are spatial depictions that rely on quantitative attributes other than area to size their units. The most common cartograms show the world, and distort it by population or by wealth, but any geographic entity can be transformed into a cartogram. Because they force us to view the map in unfamiliar ways, cartograms provide dramatic visual portrayals of geographic, political, and socio-economic relationships. A look at a cartogram of the world based on population (above) quickly shows the potentially dominant places of India and China in Asia with respect to Russia and the significance of Korea and Japan as well.

The world sized by Gross National Product (GDP).

The world sized by Gross National Product (GDP).

Quite a different picture is presented by the world sized by Gross Domestic Product (GDP).  Here the U.S. and Europe dominate.  China is still large,Russia is still small, and the importance of Korea and Japan is still evident.

Continue reading →

Improving Nutrition in Poweshiek County One Food Box at a Time

Today, we are sharing an example of community collaboration, emphasizing a practical application of data to produce real-world solutions to policy issues. Mid-Iowa Community Action (MICA), located in Grinnell, IA, partnered with DASIL to evaluate the quality of its food pantry services and determine ways to promote healthier eating among the families it serves.  This partnership allows for the investigation of data, providing the necessary concrete evidence to drive future changes in MICA’s food box policy. Seth hopes that this will inaugurate a shift to more data-driven decision-making at MICA.

Obesity and Type II Diabetes differentially affect the lower-income Americans who are the clients of MICA. This has been largely attributed to financial constraints leaving families with no choice but purchasing the most inexpensive food they can, which is frequently less nutritional. Thus the food pantry is potentially an important potential part of the solution. To learn more about the influence of income on diabetes rates, take a look at this study by the Center for Disease Control and Prevention or explore DASIL’s interactive visualization on factors correlating with diabetes.

Food boxes are distributed monthly to the families MICA serves, providing varying amounts of food based on family size. After a few weeks at MICA, Grinnell Corps Fellow Seth Howard approached his director about conducting a survey to evaluate the need for changes in the food boxes. The goal of the survey was twofold: to assess satisfaction with MICA services, as it had been years since the food services had been adequately evaluated, and to ascertain the demand for healthier foods, different foods, nutritional information, and cooking tips.

Seth surveyed every individual who utilized the food pantry in the month of July using a questionnaire that could be returned anonymously to a submission box.  A total of 195 household took the survey, giving a response rate of 78.9% of the 247 households served in that month. Using a 5-point Likert scale (1-Strongly Negative, 2- Somewhat Negative, 3-Neutral, 4- Somewhat Positive, 5- Strongly Positive), survey takers responded to the frequency with which they use common food box items, as well as answering some questions about what they’d like to see in future food boxes.

As the graphic below shows, overall, MICA households using the food pantry wanted to see healthier items despite being generally satisfied with the food boxes (only 6.15% reported strong or slight dissatisfaction). Providing even better, healthier options will increase satisfaction and drastically boost use of food box contents.

Would you like to receive healthier food items in the monthly box?  72% Yes, 28% No

Continue reading →

How Traditional Introductory Statistics Textbooks Fail to Serve Social Science Undergraduates

When no weighting variable is used, the estimate is that about 50% of the population know the Jewish Sabbath starts on Friday.

No weighting variable: the estimate is that about 50% of the population knows that the Jewish Sabbath starts on Friday.

When the data is appropriately weighted, the estimate changes by about 5 percentage points.

Appropriately weighted data: The estimate changes by about 5 percentage points, suggesting that only 45% of the population knows the correct start time.

Full disclosure: I approach this topic simultaneously from the perspective of a social scientist and as the instructor of a traditional introductory statistics class for over twenty years. I am, thus, myself part of the problem. While I am mainly following the dictates of some of the most popular text books, it is fully within my power to diverge from the book. When I do not do so, it is really my own fault—a sheep following the sheep dogs.

Our worst failure as statistics teachers is to teach as if all or most of the data that our students will engage with in their future careers are from simple random samples. Continue reading →