Data Handling in Science

1. Data Handling in Science by Naomi Hiscock & Renny Harrop Summer 2007

2. Aims To be familiar with the different types of data handling To be able to use science investigations as a starting point for data handling activities To consider strategies for teaching children how to present and interpret data To plan science investigations with a focus on data handling

3. Types of data handling What types of data handling/visual representation can you think of? Brainstorm types of graphs etc. Handout 5 day maths course data poster Did they miss any out?Brainstorm types of graphs etc. Handout 5 day maths course data poster Did they miss any out?

4. Types of data handling

5. Data – what is it? Collection of information Usually gathered by observation, questioning or measuring in response to a posed question or to test a hypothesis Often organised into graphs and charts for analysis

6. Data handling cycle Specify the problem Formulate questions in terms of the data needed and the types of inferences that may be made from them. Plan Decide what data should be collected, including sample size and data format, and what statistical analysis needs to be carried out. Collect data Obtain data from a variety of appropriate sources, including experiments and surveys, and primary and secondary sources. Process and represent Reduce the raw data into summary information, including lists, tables and charts, to provide insight into the problem. Interpret and discuss Relate summarised data to the initial questions. Children’s performance in the KS2 national tests suggests that they need more opportunities in the first, second and last of these five aspects of the data handling cycle. Although children do carry out data collection and presentation, too often they spend little or no time on the other important aspects. Data can come from first hand (primary) and second hand ( secondary) sources. Examples of secondary sources are prepared databases, reference books, newspapers, census data, registers, tables of weather statistics etc Children’s performance in the KS2 national tests suggests that they need more opportunities in the first, second and last of these five aspects of the data handling cycle. Although children do carry out data collection and presentation, too often they spend little or no time on the other important aspects. Data can come from first hand (primary) and second hand ( secondary) sources. Examples of secondary sources are prepared databases, reference books, newspapers, census data, registers, tables of weather statistics etc

7. Gathering data to solve a question Classification Survey Observation Fair test Research Problem solving

8. Classification Progression Sorting using one property Groups/Venn diagrams Sorting using two properties Venn diagrams/Carroll diagrams Using questions to sort Branching databases

9. Fair tests Progression Ramp investigations Direct comparison Bar chart Tables, scale selectors and human bar charts ITP http://www.standards.dfes.gov.uk/primary/teachingresources/mathematics/nns_itps/data_handling/datahandling_3_0.swf Line graph Human line graph and sticky dots

10. Observation Progression Growing plants Drawing Direct measurements Using strips of paper and leaves Line graph Measure height each day/week Use ITP http://www.standards.dfes.gov.uk/primary/teachingresources/mathematics/nns_itps/line_graph/num_itp_line_graph_1_1.swf

11. Survey Progression Pictogram Example – What is your favourite drink? Using children’s photos Scattergram Example – Do taller children have longer arms? Frequency chart Example – How much do children drink in a day? Pictogram Example – What is your favourite drink? Laminated photos or self portraits on classroom wall Pictogram Example – What is your favourite drink? Laminated photos or self portraits on classroom wall

12. What type of graph?

13. Interpretation of data Graph stories Devising questions Answering questions Drawing conclusions

14. Drawing conclusions Children find it difficult to:- describe the whole relationship in a clear precise way explain why They often restate the results without offering an explanation Use correct scientific vocabulary but do not explain what it means or how it relates to their results Sometimes add irrelevant scientific knowledge