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What are the advantages and disadvantages of using scatterplots to visualize association?

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Scatterplots are one of the most common and useful tools to visualize the association between two numerical variables. They show how the values of one variable change in relation to another, and can reveal patterns, trends, outliers, and clusters. However, scatterplots also have some limitations and challenges that you should be aware of before using them to analyze your data. In this article, you will learn about the advantages and disadvantages of using scatterplots to visualize association, and how to overcome some of the common pitfalls.

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1 Advantages of scatterplots

One of the main advantages of scatterplots is that they are easy to create and understand. You can use any software or tool that can plot points on a Cartesian plane, such as Excel, R, or Python. You can also customize the appearance and style of your scatterplot, such as adding labels, colors, shapes, or lines. Scatterplots are also intuitive and straightforward to interpret. You can see at a glance if there is a positive, negative, or no association between your variables, and how strong or weak it is. You can also identify any outliers or anomalies that do not fit the general pattern.

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Jennifer Kirk, Ph.D.

Gerontologist | Investigating the Intersection of Health Disparities and Aging with a Socioepidemiological Approach
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In my experience, scatterplots are beneficial for initial data exploration and visualization, because they allow us to quickly identify patterns and potential outliers in data.

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2 Disadvantages of scatterplots

One of the main disadvantages of scatterplots is that they can be misleading or inaccurate if you do not pay attention to the scale, range, and distribution of your variables. For example, if you have a very large or small range of values, or if your values are clustered in one area, your scatterplot may not show the true relationship between your variables. You may also miss some important details or nuances if you do not use appropriate markers or labels to distinguish different groups or categories within your data. Another disadvantage of scatterplots is that they can only show the association between two variables at a time. If you have more than two variables, you may need to use other methods or techniques, such as multiple regression, to explore the complex interactions and effects among them.

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pieter degraeve

PhD - Lead Data Analyst at Napoleon Sports & Casino Online
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Depending on the amount and spread of the data-points, sometimes the size of the dots is used to visualize a third variable. Nowadays, some people even add a video-effect, showing the movement of the different points over time.

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Jennifer Kirk, Ph.D.

Gerontologist | Investigating the Intersection of Health Disparities and Aging with a Socioepidemiological Approach
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Two additional disadvantages of scatterplots are that they can become cluttered and difficult to interpret if there are too many data points. Additionally, since they only show the relationship between two variables, they may not fully capture the complexity of the data.

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3 How to measure the strength of association

One of the questions that you may have when looking at a scatterplot is how to measure the strength of association between your variables. How can you quantify how closely or loosely your points are clustered around a line or a curve? One of the most common ways to do this is to use a correlation coefficient, such as Pearson's r or Spearman's rho. A correlation coefficient is a numerical value that ranges from -1 to 1, and indicates how linear or monotonic the relationship between your variables is. A positive value means that as one variable increases, the other also increases; a negative value means that as one variable increases, the other decreases; and a zero value means that there is no association at all. The closer the value is to -1 or 1, the stronger the association is; the closer it is to zero, the weaker it is.

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Nathaniel Zbaida

Ph.D. Candidate in Economics
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In my experience, it can be particularly challenging to interpret the direction of correlation in scatter plots with correlation values close to zero or with few data points. This can be addressed by labelling the scatter plot with the correlation value.

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4 How to interpret a correlation coefficient

However, a correlation coefficient is not enough to tell you everything about the association between your variables. You also need to consider the context, meaning, and significance of your data. For example, a correlation coefficient of 0.8 may seem very high, but it may not be meaningful or relevant if your sample size is very small, or if your variables have no causal or logical connection. Likewise, a correlation coefficient of 0.2 may seem very low, but it may be significant or important if your sample size is very large, or if your variables have a practical or theoretical implication. Therefore, you should always interpret a correlation coefficient with caution and critical thinking, and use other methods or tests to confirm or reject your hypotheses.

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Andrew L. Burruss
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" .. no causal or logical connection." (paragraph above) It is important to remember that correlation does not imply causation. A significant correlation coefficient does not imply that one variable is a consequence of the other.

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5 How to improve your scatterplots

Finally, here are some tips and best practices to improve your scatterplots and make them more effective and informative. First, choose an appropriate scale and range for your axes, and make sure they are consistent and clear. Second, use different colors, shapes, or sizes to distinguish different groups or categories within your data, and add a legend or a key to explain them. Third, add a title and labels to your scatterplot, and use descriptive and accurate names for your variables. Fourth, add a line or a curve to your scatterplot, such as a regression line or a loess curve, to show the general trend or direction of your data. Fifth, add a correlation coefficient or another measure of association to your scatterplot, and indicate its value and significance. Sixth, check your scatterplot for any errors, outliers, or anomalies, and investigate their causes and consequences.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the advantages and disadvantages of using scatterplots to visualize association?

1

2

3

4

5

6

1 Advantages of scatterplots

2 Disadvantages of scatterplots

3 How to measure the strength of association

4 How to interpret a correlation coefficient

5 How to improve your scatterplots

6 Here’s what else to consider

Statistics

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What are the advantages and disadvantages of using scatterplots to visualize association?

1

2

3

4

5

6

1 Advantages of scatterplots

2 Disadvantages of scatterplots

3 How to measure the strength of association

4 How to interpret a correlation coefficient

5 How to improve your scatterplots

6 Here’s what else to consider

Statistics

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