When responses to two rating scale questions are plotted in four quadrants of a two dimensional table the result is called a histogram True False?

Quality Glossary Definition: Scatter diagram

Table of Contents Show

When to Use a Scatter Diagram
Scatter Diagram Procedure
Scatter Diagram Example
Additional Scatter Diagram Examples
Scatter Diagram Considerations
Scatter Diagram Resources
Survey Questions
Embedded Data
Survey Metadata
Types of Metrics

Also called: scatter plot, X-Y graph

The scatter diagram graphs pairs of numerical data, with one variable on each axis, to look for a relationship between them. If the variables are correlated, the points will fall along a line or curve. The better the correlation, the tighter the points will hug the line. This cause analysis tool is considered one of the seven basic quality tools.

When to Use a Scatter Diagram

When you have paired numerical data
When your dependent variable may have multiple values for each value of your independent variable
When trying to determine whether the two variables are related, such as:
- When trying to identify potential root causes of problems
- After brainstorming causes and effects using a fishbone diagram to determine objectively whether a particular cause and effect are related
- When determining whether two effects that appear to be related both occur with the same cause
- When testing for autocorrelation before constructing a control chart

Scatter Diagram Procedure

Collect pairs of data where a relationship is suspected.
Draw a graph with the independent variable on the horizontal axis and the dependent variable on the vertical axis. For each pair of data, put a dot or a symbol where the x-axis value intersects the y-axis value. (If two dots fall together, put them side by side, touching, so that you can see both.)
Look at the pattern of points to see if a relationship is obvious. If the data clearly form a line or a curve, you may stop because variables are correlated. You may wish to use regression or correlation analysis now. Otherwise, complete steps 4 through 7.
Divide points on the graph into four quadrants. If there are X points on the graph:
- Count X/2 points from top to bottom and draw a horizontal line.
- Count X/2 points from left to right and draw a vertical line.
- If number of points is odd, draw the line through the middle point.
Count the points in each quadrant. Do not count points on a line.
Add the diagonally opposite quadrants. Find the smaller sum and the total of points in all quadrants. A = points in upper left + points in lower right B = points in upper right + points in lower left Q = the smaller of A and B
N = A + B
Look up the limit for N on the trend test table.
- If Q is less than the limit, the two variables are related.
- If Q is greater than or equal to the limit, the pattern could have occurred from random chance.

Scatter Diagram Example

The ZZ-400 manufacturing team suspects a relationship between product purity (percent purity) and the amount of iron (measured in parts per million or ppm). Purity and iron are plotted against each other as a scatter diagram, as shown in the figure below.

There are 24 data points. Median lines are drawn so that 12 points fall on each side for both percent purity and ppm iron.

To test for a relationship, they calculate: A = points in upper left + points in lower right = 9 + 9 = 18 B = points in upper right + points in lower left = 3 + 3 = 6 Q = the smaller of A and B = the smaller of 18 and 6 = 6

N = A + B = 18 + 6 = 24

Then they look up the limit for N on the trend test table. For N = 24, the limit is 6.
Q is equal to the limit. Therefore, the pattern could have occurred from random chance, and no relationship is demonstrated.

Scatter Diagram Example

Additional Scatter Diagram Examples

Below are some examples of situations in which might you use a scatter diagram:

Variable A is the temperature of a reaction after 15 minutes. Variable B measures the color of the product. You suspect higher temperature makes the product darker. Plot temperature and color on a scatter diagram.
Variable A is the number of employees trained on new software, and variable B is the number of calls to the computer help line. You suspect that more training reduces the number of calls. Plot number of people trained versus number of calls.
To test for autocorrelation of a measurement being monitored on a control chart, plot this pair of variables: Variable A is the measurement at a given time. Variable B is the same measurement, but at the previous time. If the scatter diagram shows correlation, do another diagram where variable B is the measurement two times previously. Keep increasing the separation between the two times until the scatter diagram shows no correlation.

Scatter Diagram Considerations

Even if the scatter diagram shows a relationship, do not assume that one variable caused the other. Both may be influenced by a third variable.
When the data are plotted, the more the diagram resembles a straight line, the stronger the relationship.
If a line is not clear, statistics (N and Q) determine whether there is reasonable certainty that a relationship exists. If the statistics say that no relationship exists, the pattern could have occurred by random chance.
If the scatter diagram shows no relationship between the variables, consider whether the data might be stratified.
If the diagram shows no relationship, consider whether the independent (x-axis) variable has been varied widely. Sometimes a relationship is not apparent because the data do not cover a wide enough range.

Scatter Diagram Resources

You can also search articles, case studies, and publications for scatter diagram resources.

Books

The Quality Toolbox

Articles

Pitch Perfect (Lean & Six Sigma Review) Learning the ins and outs of capability analysis by examining Pittsburgh Pirates starting pitcher Jameson Taillon’s performance using scatter diagrams.

Adapted from The Quality Toolbox, ASQ Quality Press.

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Each visualization varies in its options and settings, which you can find detailed on the linked pages.

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A data source is the source of the data you want your visualization to display. These sources can include questions you asked in the survey, or additional fields, such as survey metadata and embedded data.

To set or change a visualization’s data source, click on the visualization, then click the dropdown under Data Source.

Some pages of your report are specific to a question. In this case, you cannot change the data source, and this section will be grayed out. However, some question types, such as NPS and matrix questions, will have multiple options. More is detailed in the Survey Questions section below.

In contrast, you can always change the data source on a visualization on a custom page.

Warning: You can only load one field into the data source at a time! You cannot put two different questions into the same visualization. If you want to split results by other fields, such as by showing how men vs. women responded to a question, see the Breakouts section below.

There are three main types of data sources you can represent in a visualization: survey metadata, question answers, and Embedded Data. Depending on the particular type of information you select, you’ll have different visualizations available to use.

Survey Questions

There are many different question types in Qualtrics, and each question type has different visualizations it’s compatible with. Some question types have only one data source option (i.e., only one way to represent the data), but for other question types, you will have additional options. Possible options include the following:

Text Entry: For form field questions, you can choose which field you want to display.
Choices: For questions with multiple statements and choices (matrix, rank order, slider, etc.), you can choose to display either all the possible choices together or just one particular choice alone.
NPS®: For NPS® questions, you can view the NPS® groups (Detractor, Passive, and Promoter, all calculated for you) or you can view the actual numeric values (0–10) the question displays to respondents.

Embedded Data

Embedded data is any extra information you’ve recorded in addition to your question responses. These include embedded data fields set in the survey flow and custom columns added in the Data & Analysis tab.

Embedded data fields don’t have multiple data source options, but the type of data captured in the field can affect what types of visualizations are available.

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Survey Metadata

Survey metadata is information automatically collected when a respondent takes a survey. Survey metadata fields include the following:

Start Date: The date and time the survey was started.
End Date: The date and time the survey was completed.
Response Type: The type of response collected (survey preview, IP address, offline app, etc.).
IP Address: The IP address collected from the respondent’s browser.
Progress: The percent to which the survey was completed. For most respondents this will be 100.
Duration: The number of seconds it took the respondent to complete the survey.
Finished: The status of the respondent’s survey (true = completed, false = incomplete).
Recorded Date: The date and time the survey was recorded in the dataset.

For some visualizations, you can select the metric (count, percentage, mean, etc.) being displayed. The exact options available will depend on both the particular data source and the visualization type selected.

Types of Metrics

Mean: Display the average, or mean, of the collected responses.
Min: Display the minimum value response. This will always be the lowest point on your scale if you have a multiple choice question, whereas a question where respondents can enter values in will be less predictable.
Max: Display the maximum value response. This will always be the highest point on your scale if you have a multiple choice question, whereas a question where respondents can enter values in will be less predictable.
Net Promoter® Score: This is a calculation of the Net Promoter® Score. Although you can technically select this metric for other question types, this metric is best for when your survey actually contains an NPS® question.
Response Count: The number of people who responded to a question.
Qtip: If your questions don’t have validation on them forcing respondents to answer, then this number can be lower than your number of total survey respondents.
Choice Count: The number of times each choice was selected by respondents.
Qtip: If you have questions where respondents can provide multiple answers, this number may be much larger than your respondent count.
Sum: The total sum of all the respondents’ answers to the questions added together.
Percentage: The percentage of respondents who chose each choice.

You can determine how many decimal places display in your visualization.

To adjust this globally, for all visualizations in your report, open your global options.

Have you ever wanted to display data from different samples side by side? Maybe you want your satisfaction bar graph to show how women responded and how men responded right next to each other. Maybe you want to show each store location’s data in the same graph. In that case, you may want to use a visualization breakout.

For additional information, visit the Results-Reports Breakouts support page.

Every visualization allows you to set a custom title for your table or graph.

Select the box next to the Title field to make the default title automatically appear. Type into the field to create your own custom title.

To return to the default title, just erase what you wrote.

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You can adjust the colors used in your chart type visualizations. Click the dropdown under Color Palette to select from a list of premade palettes.

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Add your own custom palettes by clicking the dropdown and selecting Add Custom Palette. See Adding Custom Palettes page for more information.

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