6.4 Visualizing One or More Continuous Variables

Continuous variables contain meaningful numerical values. The best plot depends on whether you want to examine the distribution of one variable, compare the distributions of several variables, or examine the relationship between two variables.

Histograms: The Distribution of One Continuous Variable

A histogram divides a continuous variable into intervals, called bins, and shows how many observations fall within each interval. Histograms are especially useful for examining:

The overall shape of the distribution
Symmetry or skew
The location of most observations
Gaps, clusters, or multiple peaks
Possible extreme values

Unlike a bar plot, the bars in a histogram touch because each bar represents part of a continuous scale.

The apparent shape of a histogram can change depending on the number and width of the bins. Avoid drawing conclusions from one arbitrary bin setting alone, especially with a small sample.

Creating a Histogram in jamovi

Select Plots → Histogram.
Move the continuous variable into Variable.
Add an optional Grouping Variable only when comparing the distributions of clearly defined groups will help answer the research question.
Leave Bin Width set to Auto unless you have a reason to adjust it.
Select Show density when a smooth density curve would help reveal the overall shape.
Revise the title and axis titles as needed.

You may also use Exploration → Descriptives when quickly inspecting a variable or checking assumptions.

Box Plots: Compact Summaries of Continuous Variables

A box plot summarizes a continuous variable using the median, quartiles, and spread of the data. It can also flag possible outliers.

A box plot is less detailed than a histogram, but it is compact and makes it easier to compare several continuous variables or groups on a common scale.

When interpreting a box plot, look for:

The median within the box
The middle 50% of the data, represented by the box
The spread represented by the whiskers
Possible outliers beyond the whiskers
Differences in spread or skew suggested by uneven boxes or whiskers

Remember that a point flagged by a box plot is a possible outlier, not automatically an error or a value that should be removed.

Creating a Box Plot in jamovi

Select Plots → Box Plot.
Move the continuous variable into Variable.
Add Grouping Variable 1 or Grouping Variable 2 only when you want to compare the variable across categorical groups.
Leave Show outliers selected so possible unusual observations remain visible.
Select Flip axes when horizontal boxes make long category labels easier to read.
Revise the title, axis titles, and legend as needed.

The Notch option adds an approximate interval around the median. It is not necessary for the basic box plots used in this course unless an assignment specifically requests it.

Scatterplots: Relationships Between Two Continuous Variables

A scatterplot shows the relationship between two continuous variables. Each point represents one observation with a value on the x-axis and a value on the y-axis.

When interpreting a scatterplot, examine:

Direction: Is the relationship positive, negative, or absent?
Form: Is the relationship approximately linear, curved, or more complex?
Strength: How closely do the points follow the overall pattern?
Unusual observations: Are there points far from the rest of the data or from the overall relationship?

A scatterplot is important even when you plan to calculate a correlation. Two datasets can have similar correlations while having very different patterns, such as a curved relationship or an influential outlier.

Creating a Scatterplot in jamovi

Select Plots → Scatter Plot.
Move one continuous variable into X-Axis and the other into Y-Axis.
Add a categorical Grouping Variable only when distinguishing groups helps answer the research question.
Select Show line when a regression line would help summarize a linear pattern.
Keep the confidence interval around the fitted line when communicating uncertainty would be useful.
Revise the title and axis titles as needed.

When possible, place a predictor or explanatory variable on the x-axis and an outcome variable on the y-axis. This convention does not establish causality; it simply makes the intended question easier to follow.

Line Plots: Ordered Continuous Observations

A line plot connects observations across a meaningful order, such as time. It is useful when the sequence itself matters and the connecting line represents continuity or change.

Do not use a line plot merely because values can be placed in some order. Connecting points implies that movement from one x-axis value to the next is meaningful.

Creating a Line Plot in jamovi

Select Plots → Line Plot.
Move the ordered or time variable into X-Axis.
Move the continuous outcome into Y-Axis.
Add a categorical Grouping Variable when separate lines would help compare groups.
Choose Individual points when each row represents a meaningful paired x- and y-observation.
Choose Aggregated data when the graph should summarize observations at each x-axis value, such as displaying a mean at each time point.
Keep Show lines and Show points selected when both the pattern and the observed locations matter.
Use different colors, line types, or point types when groups must remain distinguishable.
Revise the title, axis titles, and legend as needed.

Check Your Understanding

A researcher measures stress and sleep quality for 150 participants. The researcher wants to know whether participants with greater stress tend to report poorer sleep quality.

Which plot should be used? Why would a histogram not answer the same question?

A scatterplot should be used because both variables are continuous and the question concerns their relationship. A histogram would show the distribution of only one variable at a time and would not show how stress and sleep quality vary together.