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

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.

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.

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.