Method understanding charts
How to read and understand trend data
Improving risk literacy among consumers.
Risk literacy can be understood as a collection of abilities and skills that enable a critical understanding of uncertain events and the use of these in a way that is beneficial to one's own life. These abilities and skills include:
- to correctly distinguish risk from certainty,
- to correctly distinguish problems of risk from problems of uncertainty,
- to correctly interpret numeric probability expressions,
- to question reference conditions of probability expressions,
- to correctly distinguish between representations of absolute and relative risk.
- to correctly calculate conditional probabilities,
- to critically use depictions of risk (charts).
Why is it relevant to improve consumers' risk literacy?
The aim is for the consumer to be able to make better decisions. For many of these decisions, statistical information is available in the form of graphical risk charts. Charts make it easier to identify relationships and patterns that would remain hidden in tables. Since comprehending graphic depictions is easier for many people than reading tables, charts can also be a gateway for manipulation. Actors who want consumers to make a certain decision, do so by designing graphic information in a certain way. Consumers must therefore be able to become aware of manipulations. However, the ability to critically reflect on information from a standard graphical representation that are relevant for a risk decision needs to be practiced (as it is already the case with mathematics lessons in schools).
Why is it problematic to improve risk literacy regarding graphic depictions?
If you want to increase consumers' risk competence regarding the critical reflection on data from charts, you will face various challenges:
- conveying how data is to be interpreted, aside from reading,
- conveying how manipulations can be detected, aside from reading,
- sparking interest to engage with these learning contents.
The interest of many learners in dealing with statistics can be activated by the performance motive, i.e. the progress that could be achieved. This can be illustrated by an applicable increase in competence. Accordingly, the learning visualisation is implemented on the basis of a relevant consumer example: Line charts with financial data as data source for common decisions with high financial stakes.
In this learning visualisation, test tasks are used. Their difficulty increases gradually. It follows the steps of learning how to read off a chart, how to interpret it, and how to manipulate it.
(1) Point values, which require an understanding of axes, their labels and the axis ticks in relation to the data and their legend, are read off the chart.
(2) Trends that can rise, fall or express volatility are interpreted. Here, the complete line paths with regard to the axes, their labels and the axis ticks as well as the legend are to be compared with each other.
(3) Manipulation should be learned in order to be able to examine charts for certain strategies of manipulation in the real world. Here not only axes, labels and axis ticks must be set in relation to the data and their legend. It is also necessary to understand their interaction and to achieve a given goal by examining the data section and magnification factor on two axes.
Interactivity can be used to nurture interest. It should be used in an economical, targeted and coherent way with the necessary controls and content. Since this is a playful transfer of knowledge from the adult world (Barth, 2018), this interest can be reinforced among certain consumers by addressing the motive of power (competition, status) (Sailer et al., 2014) by comparing their own risk assessment abilities with those of others. The reduced design refrains from presenting further details that motivate interest. These could contradict the actual goal, particularly if they do not fit harmoniously into the content, or even distract or simply lead to confusion (Eitel & Kühl, 2019).
- Barth, R. (2018). Möglichkeiten der Nutzung von Game Design Prinzipien in der Erwachsenenbildung. digital. innovativ|# digiPH, 109–118.
- Eitel, A., & Kühl, T. (2019). Harmful or helpful to learning? The impact of seductive details on learning and instruction. Applied Cognitive Psychology, 33(1), 3–8.
- Sailer, M., Hense, J., Mandl, J., & Klevers, M. (2014). Psychological perspectives on motivation through gamification. Interaction Design and Architecture Journal, (19), 28–37.
Option 1: You can embed the given visualisation
It is possible to embed the visualisation from our website including the frame text via iframe. To do this, use the following html-code for your website: <iframe frameborder="0" height="650px" src="https://static.risikoatlas.de/modules/understanding-graphs/" width="1024px"></iframe>
Option 2: You can adapt the given visualisation
If you use your own data as a multiplier, your web developers can enter it into your own consumer fact box with experience-based learning.
We will provide the person responsible for your website with the documented code for download via github. You can then edit the material. The link to the repository is available on request. Contact details can be found here.
Option 3: You can apply the scientific principle independently
If you require assistance, please consult the final report on the RiskAtlas project from July 2020 or contact us. Contact details can be found here.
When using the instruments, please mention the funding agency, which is the German Federal Ministry of Justice and Consumer Protection, and the Harding Centre for Risk Literacy as the responsible developers.
Logos can be downladed here.
What do I need this visualisation?
If you want to learn something. The purpose of this learning visualisation is to practice your ability to critically use graphic depictions of risk. This enhances your own risk competence. However, this learning visualisation is not supposed to support you in making a concrete decision.
What does the visualisation show?
The visualisation shows three investment funds. Specifically, it shows how their value (y-axis) has changed over the quarters of several years (x-axis). Learner are guided by multiple-choice questions. They always receive immediate feedback on what was correct and what was wrong, and thus work linearly through the complete visualisation. In addition, social feedback is always given, i.e. how many other learners were able to correctly answer a certain question.
What is the quality of the data?
The numbers are published and can be found on all financial websites that list investment funds. For market participants, the quality of the published numbers is considered to be factually correct, i.e. very high.
Empirical evaluation with consumers
All research results on the fundamentals and on the effectiveness of the RiskoAtlas tools in terms of competence enhancement, information search and risk communication will be published together with the project research report on 30 June 2020. If you are interested beforehand, please contact us directly (Felix Rebitschek, rebitschek@mpib-berlin.mpg.de).
Last update: 14 October 2019.