PPDAC – Conclusions Stage

Conclusions: Explain, interpret, decide, communicate, persuade and act

In the PPDAC – Conclusions Stage , the learner uses the results from the analysis to answer the question from the Problem stage. They decide how to explain and communicate the results to others, and form new questions which could be answered in the next PPDAC cycle. The Conclusions stage arguably involves the most important skills for data citizenship because most people are required to interpret claims about data made by other people more often than they conduct analyses themselves. Data interpretation skills enable critical thinking about claims in the newspaper articles, election campaigns, adverts and in the workplace.

The key skills for the PPDAC – Conclusions Stage are related to interpretation of analytical findings, and can often be learned using results from pre-existing analysis. For example, the learners could examine graphs in a newspaper story to see if they come to the same conclusions as the reporter who wrote the article. If a politician is quoted in the article, the learners can decide whether the politician’s explanation for some new results is likely to be correct given the data.

In some cases, the data may have been collected in order to cast light on an issue within the local community. Imagine that a group of learners has collected data on the frequency of visits to the local park by various groups of local people. They could use the data to recommend how the local authority should decide to spend money refurbishing the park to meet the needs of the different users. The skills of clearly communicating results, storytelling with data and persuasion are required here.

If the learner has conducted a study about a topic within their daily life – such as personal health or fitness – in the conclusions stage they can use the data to make decisions about whether and how they should change their behaviour to meet their goal. For example, if they were using a sleep tracker to see if they get enough sleep per night for their age, they could compare a visualisation of their data to a recommended figure for young people of their age. With support they could arrive at a plan to improve their sleeping patterns by changing their bedtime habits.

Conducting a PPDAC project often raises as many new questions as it answers- and this is a good thing! At the final stage of the first cycle, learners can reflect on what they have learned to decide what new or refined questions they should ask if they or other researchers were to go through another cycle in the data problem solving process.

Learners should know how to:

• Use results from data analysis to answer the question;
• Explain and interpret what output from analysis means;
• Evaluate whether an explanation is likely to be correct, given the data;
• Use analysis results to decide, recommend and communicate to others;
• Act on data to change their behaviour;
• Form new questions;