12ENC - 2.3.3

What is the purpose of data visualisation?

• To transform raw data into visual formats like charts, graphs, and dashboards, allowing users to interpret and understand complex information quickly.

• To store large amounts of data in a database for future retrieval.

• To perform complex calculations and data analysis without visual representation.

• To create textual reports that summarize data findings.

Why is evaluating the effectiveness of data visualisations important?

• It helps in reducing the amount of data presented.

• It ensures that users can understand and engage with the data meaningfully, meeting their needs.

• It allows for the creation of more complex visualisations.

• It is only necessary for academic research.

What methods can be used to measure the effectiveness of data visualisations?

• Scoring based on criteria

• Quantitative measures

• Qualitative feedback

• User engagement metrics

What aspects should be considered for accuracy in data visualisation?

• Accurate representation of data

• Use of colorful graphics

• Inclusion of irrelevant information

• Data sourced from social media

What aspects should be considered for clarity in data visualisation?

• Ease of understanding

• Complexity of design

• Accurate and appropriate labelling

• Use of bright colors

What are some quantitative measures for evaluating data visualisations?

• Time on task

• Error rate

• Engagement

• User satisfaction

What is involved in data analytics for data visualisation evaluation?

• Monitoring user interaction, frequently viewed parts, and areas where users struggle or abandon the visualisation.

• Analyzing the color scheme and design of the visualisation.

• Counting the number of visualisations created by users.

• Evaluating the performance of the data processing algorithms.

What are some heuristic principles for data visualisation?

• Avoiding chartjunk

• Using inconsistent scales and axes

• Ensuring appropriate colour usage

• Using 3D effects for clarity

What is involved in user testing for data visualisation evaluation?

• Observation

• Surveys and feedback

• Task-based testing

• Data analysis techniques

What aspects should be considered for accessibility in data visualisation?

• Accessibility for users with disabilities

• Use of bright colors only

• Complexity of data representation

• Sufficient contrast between elements