Level 1 Lesson 1 Quiz

Which of the following is a characteristic of ordinal variables?

They categorize data without any inherent order.
They have a true zero point.
They have a specific order, such as rankings.
They measure physical dimensions like length.

According to Tamara Munzner’s famework, what is the most effective encoding method for representing quantitative data?

Color saturation
Position on a common scale
2D area
Shape

What is the main difference between categorical and quantitative variables?

Categorical variables have distinct groups without numerical value, while quantitative variables are measurable data expressed numerically.
Categorical variables represent continuous data, while quantitative variables represent discrete data.
Categorical variables are used for ordered data, while quantitative variables are used for unordered data.
Categorical variables always use numerical scales, while quantitative variables use labels or categories.

Which of the following best describes an interval variable?

It categorizes data into distinct groups without any specific order.
It measures quantities with a consistent interval between values but does not have a true zero point.
It represents data with both consistent intervals and a true zero point.
It differentiates between categories using attributes like color or shape.

In a scatter plot, what are the points representing data called?

Variables
Marks
Encodings
Scales

The “pop-out effect” in data visualization is primarily used to do what?

Confuse the viewer with too much information.
Highlight critical information by making it stand out.
Measure physical dimensions.
Call out significant values through labeling and annotation.

Fill in the blank:

A true zero point is a characteristic of a scale variable.

Fill in the blank:

In data visualization, changing the color or size of a mark to convey additional information in the data is known as .

True or False: The height (or length) of the bars in a bar chart is an example of an encoding.

True
False

Which of the following best explains the difference between magnitude channels and identity channels in Tamara Munzner’s framework?

Magnitude channels are for categorical data, while identity channels are for ordered data.
Magnitude channels are for ordered variables while identity channels are for unordered ones.
Magnitude channels rely on color and shape, while identity channels rely on position and area.
Magnitude channels are less effective than identity channels for data visualization.

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Data Literacy Level 1 v2.0

Level 1 Lesson 1 Quiz

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