Designing better graphs by including distributional information and integrating words, numbers, and images.

Lane, David M.; Sándor, Anikó

doi:10.1037/14805-022

Cited by 14 publications

(29 citation statements)

References 29 publications

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“…In any case, they should be provided in manuscripts so that reviewers may see them, even if the editor declares there is not enough space in a publication for them. Last—but not least—Lane and Sandor () stated, “There is an understandable desire on the part of researchers to show their data in a positive light. As a result, some may resist showing distributional data that reveal the variability and possible irregularities not apparent in a plot of means.…”

mentioning

confidence: 99%

Moving Beyond the Bar Plot and the Line Graph to Create Informative and Attractive Graphics1

Larson‐Hall

2017

The Modern Language Journal

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Graphics are often mistaken for a mere frill in the methodological arsenal of data analysis when in fact they can be one of the simplest and at the same time most powerful methods of communicating statistical information (Tufte, ). The first section of the article argues for the statistical necessity of graphs, echoing and amplifying similar calls from Hudson () and Larson–Hall & Plonsky (2015). The second section presents a historical survey of graphical use over the entire history of language acquisition journals, representing 192 years of journal publishing. This shows that a consensus for using certain types of graphics, which lack data credibility, has developed in the applied linguistics field, namely the bar plot and the line graph. The final section of the article is devoted to presenting various types of graphic alternatives to these two consensus graphics. Suggested graphics are data accountable and present all of the data, as well as a summary structure; such graphics include the scatterplot, beeswarm, or pirate plot. Such graphics attract readers, help researchers improve the way they understand and analyze their data, and build credibility in numerical statistical analyses and the conclusions that are drawn.

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confidence: 99%

Moving Beyond the Bar Plot and the Line Graph to Create Informative and Attractive Graphics1

Larson‐Hall

2017

The Modern Language Journal

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“…The SB is a useful graphical tool in that it keeps the graphical simplicity of the traditional boxplot and displays summary statistics around a broadly used estimator of location. Thus, the SB displays more information about data sets than graphical methods that are limited to conveying only means and error bars (e.g., bar plots or dynamite plots) (see Lane & Sándor, 2009). Additionally, the SB features accurate CIs that assist in making solid statistical inferences about results in the population.…”

Section: Discussionmentioning

confidence: 99%

The shifting boxplot. A boxplot based on essential summary statistics around the mean.

Marmolejo‐Ramos

Tian

2010

Int. j. psychol. res.

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Boxplots are a useful and widely used graphical technique to explore data in order to better understand the information we are working with. Boxplots display the first, second and third quartile as well as the interquartile range and outliers of a data set. The information displayed by the boxplot, and most of its variations, is based on the data’s median. However, much of scientific applications analyse and report data using the mean. In this paper, we propose a variation of the classical boxplot that displays information around the mean. Some information about the median is displayed as well.

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“…Amounts or comparisons are often displayed with a bar plot ( Figure 1A), although numerous other options exist, including Cleveland dot plots and even heatmaps ( Figure 1F). Bar plots are among the most common geometry, along with lines, 9 although bar plots are noted for their very low data density 16 (i.e., low data-ink ratio). Geometries for amounts should only be used when the data do not have distributional information or uncertainty associated with them.…”

Section: Principle #3 Use An Effective Geometry and Show Datamentioning

confidence: 99%

“…This is a good reminder to be very clear not only in stating what type of uncertainty you are sharing, but what the interpretation is. Others 16 even go so far as to recommend that standard error not be used because it does not provide clear information about standard errors of differences among means. One recommendation to go along with expressing uncertainty is, if possible, to show the data (see Use an Effective Geometry and Show Data).…”

Section: Principle #5 Include Uncertaintymentioning

confidence: 99%

Principles of Effective Data Visualization

Midway

2020

Patterns

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Summary We live in a contemporary society surrounded by visuals, which, along with software options and electronic distribution, has created an increased importance on effective scientific visuals. Unfortunately, across scientific disciplines, many figures incorrectly present information or, when not incorrect, still use suboptimal data visualization practices. Presented here are ten principles that serve as guidance for authors who seek to improve their visual message. Some principles are less technical, such as determining the message before starting the visual, while other principles are more technical, such as how different color combinations imply different information. Because figure making is often not formally taught and figure standards are not readily enforced in science, it is incumbent upon scientists to be aware of best practices in order to most effectively tell the story of their data.

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Designing better graphs by including distributional information and integrating words, numbers, and images.

Cited by 14 publications

References 29 publications

Moving Beyond the Bar Plot and the Line Graph to Create Informative and Attractive Graphics1

Moving Beyond the Bar Plot and the Line Graph to Create Informative and Attractive Graphics1

The shifting boxplot. A boxplot based on essential summary statistics around the mean.

Principles of Effective Data Visualization

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