Journalists and Maps: The Need for Teaching Quantitative Literacy to Everyone

 

In recent years programs like ArcGIS and Tableau have made it very easy to produce maps. Journalists have responded by richly illustrating their articles with quantitative data displayed as maps.  Maps are both attractive and easier to explore visually than the same data provided in tabular form, so in many ways they are ideal illustrations. For the average reader information transmitted as quantitative data appears authoritative and these maps are no exception.  On the surface they seem real and informative. Unfortunately, just as with any data-driven information maps can inadvertently be misleading.

In a recent example, NBC news illustrates an article about the Supreme Court consideration of a Texas law that would force the closure of a high percentage of existing abortion clinics across the country were similar laws to be enforced or enacted more broadly with a map of the U.S. showing the number of abortions per state in 2012, using data from the CDC (Center for Disease Control).

A quick perusal of this map seems to show why Texas is so concerned about abortions.  After all it is one of the states with the most abortions.  After a moment’s examination, the viewer might (or might not) note that the states with the highest populations also seem to have the largest numbers of abortions.

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Thus, this map really tells us little about which states have the biggest problem with abortions.  Some kind of standardization by population is needed.  One option would be to just use population size. Since the number of women of women who might potentially become pregnant and secure an abortion (usually defined as the number of women between 15 and 44) does not necessarily vary by state in direct proportion to the population size, this statistic may be a better measure for standardization than simple population size.  In terms of the number of abortions per 1,000 women ages 15-44 Florida and New York have high rates of abortions, but Texas no longer looks unusual.

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But this still might not be the most revealing measure to use, since there is state to state variation in the birth rate.  In 2012 the average birth rate for the U.S. was 1.88, but Texas had a birth rate of 2.08.  The highest birth rate in the 50 states was 2.37 in Utah and the lowest 1.59 in Rhode Island. An option that takes into account the differential birth rates is to examine the ratio between the number of births and the number of abortions.  Using this measure, New York remains very high, but due to its relatively high birth rate Texas is even lower.

Abortion Ratio

The CDC provides all of these statistics, but the journalist chose the least revealing of the possible measures to display.  Journalists are generally both well-educated and, we assume, well-meaning.  Why not pick a better measure to map when it would have been equally easy to do so?  I suspect that the answer lies firmly in the laps of educators like myself.  While we prioritize skills like writing and speaking well, we do not mandate that all students graduate statistically or even quantitatively literate, but we should.

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Mental Health Mortality, by Gender and Race

US President Barack Obama announced on January 5th that he would be taking executive action on gun control in light of a tragic trend of mass shootings in the last several years. Among the details of his gun control plan, he mentioned an increase in mental health services. While the expansion of mental health support may help in ameliorating the mass shootings epidemic, it may also has positive implications for reducing the number of Americans who die due to mental health causes. Using DASIL’s United States Mortality by Cause of Death, Race, and Gender visualization, one can see how deaths due to mental illness have been on the rise since the 1990s, and how the trend has had varying effects on every demographic:

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When looking at strictly male versus female deaths due to mental health causes, males in recent years are slightly more affected than females, at an average 3.84 deaths compared to 3.50 as of 2009. However, the 90s saw the reverse, with female fatalities at 1.92 compared to 1.37 in 1994.

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When breaking down within each gender by race, a much different story emerges. For females, the sharp rise in deaths due to mental health is observed after the year 2000, which differs from the trend for all races and all genders. In addition, while each race follows the same sharp increase after the year 2000, white women are more adversely affected, at an average 5.92 deaths compared to 4.09 for blacks and 3.68 for other races in 2009. For males, on the other hand, the same sharp increase also appears after the year 2000, however the averages for each race are much less in comparison to their female counterparts. White males are also more adversely affected in comparison to other races, at 3.11 deaths, while black males are averaging 2.41 deaths and other races 2.33 deaths.

Why has mental health been more fatal for women across all demographics? One reason may be eating disorders. Women are more likely to contract an eating disorder than men (although that does not mean men do not develop eating disorders), and eating disorders have the highest mortality rate of any mental illness. For example, according to the South Carolina Department of Mental Health, the mortality rate associated with anorexia nervosa, one type of eating disorder, is 12 times higher than the death rate associated with all causes of death for females between the ages of 15-24 years old.

President Obama’s plan for enforced support and better resources for those suffering with mental illness will not only help in tackling the gun violence epidemic, but also larger instances of mental illness fatalities.

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Testing Weighted Data

In previous posts we discussed the challenges of accounting for weights in stratified random samples. While the calculation of population estimates is relatively standard, there is no universally accepted norm for statistical inference for weighted data. However, some methods are more appropriate than others. We will focus on examining three different methods for analyzing weighted data and discuss which is most appropriate to use, given the information available.

Three common methods for testing stratified random samples (weighted data) are:

  • The Simple Random Sample (SRS) Method assumes that the sample is an unweighted sample that is representative of the population, and does not include adjustments based on the weights that are assigned to each entry in the data set. This is the basic chi-square test taught in most introductory statistics classes.
  • The Raw Weight (RW) Method multiplies each entry by their respective weight and runs the analysis on this adjusted weighted sample.
  • The Rao-Scott Method takes into account both sampling variability and varibility among the assigned weights to adjust the chi-square from the RW method.

One example of a data set which incorporates a weight variable is the Complementary and Alternative Medicine (CAM) Survey, which was conducted by the National Center for Health Statistics (NCHS) in 2012. For the CAM survey, NCHS researchers gathered information on numerous variables such as race, sex, region, employment, marital status, and whether each individual surveyed used various types of CAM. In this dataset, weights were assigned based on race, sex, and age.

Among African Americans who used CAM for wellness, we conducted a chi-square test to determine whether there was a significant difference in the proportion of physical therapy users in each region. Below is a table comparing the test statistics and p-values for each of the three statistical tests:

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The SRS method assumes that we are analyzing data collected from a simple random sample instead of a stratified random sample. Since the proportions in our sample do not represent the population, this method is inappropriate. The RW method multiplies each entry by their weight giving a slightly more representative sample. While this method is useful for estimating populations, the multiplication of the weights tends to give p-values that are much too small. Thus, both the SRS and RW methods are inaccurate methods for testing this data set. The Rao-Scott method involves adjustments for non-SRS sample designs as well as accounting for the weights, resulting in a better representation of the population.
Try it on your own!
Through a summer MAP with Pam Fellers and Shonda Kuiper, we created a CAM Data shiny app. Go to this app and compare how population estimates and test statistics can changes based upon the statistical method that is used. For example, select the X Axis Variable to be Sex and the Color By variable to be Surgery. Examine the chi-square values from each of the three types of tests. Which test gives the most extreme p-value? The least extreme? You can also find multiple datasets and student lab activities giving details on how to properly analyze weighted data here.

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Visualizing Disease Outbreaks: A Question of Scale?

Vaccinations are a hot-button issue right now as measles outbreaks crop up throughout the United States. Measles, mumps, rubella, whooping cough, and polio are all deadly diseases that can be easily prevented with vaccines. Outbreaks of these diseases have been occurring worldwide for a long time, but outbreaks have been increasing in the U.S. while going down in other countries, according to the video below:

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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

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