In this section, you will learn how to choose the most appropriate type of graph and then use a checklist to ensure that the graph conforms to APA style. We also discuss innovative graphs that highlight the exciting future of graphing. Innovations in graphing can help us to deliver a persuasive message, much like that conveyed by Florence Nightingale’s coxcomb graph.

When deciding what type of graph to use, first examine the variables. Decide which is the independent variable and which is the dependent variable. Also, identify which type of variable—nominal, ordinal, or scale (interval/ratio)—each is. Most of the time, the independent variable belongs on the horizontal x-axis and the dependent variable goes on the vertical y-axis.

After assessing the types of variables that are in the study, use the following guidelines to select the appropriate graph:

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Let’s use the graph in Figure 3-16 to confirm your understanding of independent and dependent variables. This study of jealousy (Maner, Miller, Rouby, & Gailliot, 2009) includes two independent variables: level of jealousy (whether or not a participant is low or high in chronic jealousy) and how people were primed (whether they were primed to think about infidelity or about a neutral topic). People primed to think about infidelity visualized and wrote about a time when they had infidelity-related concerns. People primed to think about a neutral topic wrote a detailed account of four or five things they had done the previous day. The dependent variable is how long participants looked at photographs of an attractive same-sex person; for a jealous heterosexual person, an attractive person of the same sex would be a potential threat to his or her relationship.

Here are the critical questions you need to ask in order to understand the graph of the findings of the jealousy study. A well-designed graph makes it easy to see the answers; a graph intended to mislead or lie will obscure the answers.

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Now check your answers:

Because there are two independent variables—both of which are nominal—and one scale dependent variable, we used a bar graph to depict these data.

Here is a helpful checklist of questions to ask when you encounter a graph or when you’re creating a graph. Some we’ve mentioned previously, and all are wise to follow.

The last of these guidelines involves a new term, the graph-corrupting fluff called chartjunk, a term coined by Tufte (2001). According to Tufte, chartjunk is any unnecessary information or feature in a graph that detracts from a viewer’s ability to understand the data. Chartjunk can take the form of any of three unnecessary features, all demonstrated in the rather frightening graph in Figure 3-17.

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There are several computer-generated graphing programs that have defaults that correspond to many—but not all—of these guidelines. Computer defaults are the options that the software designer has preselected; these are the built-in decisions that the software will implement if you do not instruct it otherwise. You cannot assume that these defaults represent the APA guidelines for your particular situation. You can usually point the cursor at a part of the graph and click to view the available options.

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Thanks to computer technology, we have entered a second golden age of scientific graphing. We mention only three categories here: interactive graphs, geographic information systems, and multivariable graphs.

Interactive Graphs One informative and haunting graph was published online in the New York Times on September 9, 2004, to commemorate the day on which the 1000th U.S. soldier died in Iraq (tinyurl.com/55nlu). Titled “A Look at 1000 Who Died,” this beautifully designed tribute is formed by photos of each of the dead servicemen and women. One can view these photographs organized by variables such as last name, where they were from, gender, cause of death, and how old they were when they died. Because the photos are the same size, the stacking of the photos also functions as a bar graph. By clicking on two or more months in a row, or on two or more ages in a row, one can visually compare numbers of deaths among levels of a category.

Yet this interactive graph is even more nuanced because it provides a glimpse into the life stories of these soldiers. By holding the cursor over a photo that catches your eye, you can learn, for example, that Spencer T. Karol, regular duty in the U.S. Army, from Woodruff, Arizona, died on October 6, 2003, at the age of 20, from hostility-inflicted wounds. A thoughtfully designed interactive graph such as this one holds even more power than a traditional flat graph in how it educates, evokes emotion, and provides details that humanize the stories behind the numbers.

Geographic Information Systems (GIS) Many companies have published software that enables computer programmers to link Internet-based data to Internet-based maps (Markoff, 2005). These visual tools are all variations on geographic information systems (GIS). The APA has sponsored advanced workshops on how to apply GIS to the social sciences.

Sociologists, geographers, political scientists, consumer psychologists, and epidemiologists (who use statistics to track patterns of disease) have already become familiar with GIS in their respective fields. Organizational psychologists, social psychologists, and environmental psychologists can use GIS to organize workflow, assess group dynamics, and study the design of classrooms. Ironically, this advance in computerized mapping is pretty much what John Snow did without a computer in 1854 when he studied the Broad Street cholera outbreak.

Multivariable Graphs As graphing technologies become more advanced, there are increasingly elegant ways to depict multiple variables on a single graph. Using the bubble graph option under “Other Charts” on Microsoft Excel (and even better, downloading Excel templates from sites such as http://juiceanalytics.com/chartchooser), we can create a bubble graph that depicts multiple variables.

In an article titled “Is the Emotion-Health Connection a ‘First-World Problem’”?, Sarah Pressman and her colleagues used a more sophisticated version of a bubble graph to display four variables (Figure 3-18; Pressman, Gallagher, & Lopez, 2013):

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The researchers could have chosen to add a fifth variable by using either size or color to represent GDP, rather than both. They might have used size, for example, to represent GDP, and color to represent the continent for each country.

From this graph, we can see a strong relation between physical health and positive emotions. GDP appears to be related to both measures; for countries with higher GDP (larger, yellower dots) and lower GDP (smaller, darker red dots), there is a link between emotions and health.

Some interactive versions of a bubble graph have, amazingly, added a sixth variable to the five that are possible on a printed page. For instance, www.gapminder.org/world has fascinating bubble graphs with data on countries around the world. Gapminder allows you to add the variable year by clicking “Play” in the lower left corner; the graph is then animated and can show the movement of countries with respect to a range of variables since 1800!