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# Presenting Data

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### Presenting Data

1. 1. Summer Conference 2010<br />Collaborative & Individual Investigations<br />Presenting data<br />Using statistics to analysedata- how far do you go?<br />Geoff Slater<br />
2. 2. Need to keep in mind…….<br />A lot of our students do not have a maths/science background<br />They should not be disadvantaged<br />SO….statistical analysis should be kept to a basic level of understanding<br />to what is stated in the curriculum statement<br />
3. 3. Presentation<br />RESULTS<br />To what extent are the data appropriately<br />organised and presented by the student?<br />For numerical data<br />Tables, graphs, histograms etc are presented and labelled appropriately<br />Use graphs-statistics appropriate to the question in the proposal<br />Raw data not is required<br />For Qualitative Data<br /> Provide a summary of themes, their frequencies, relevant quotes and excerpts (illustrative comments from focus group data)- can be in tabular form. <br />
4. 4. Presentation- numerical data<br />COMPARISON OF SCORES<br />compare mean scores, or<br />compare median scores or<br />use both- will depend on data<br />eg- there may be some extreme scores (outliers)<br />Could use other statistics (but usually not necessary)<br />eg- standard deviation, box plots (quartiles etc…), normal distributions (data is usually not normally distributed anyway)<br />Note- important not to penalise those students who do not<br />have a good statistical background.<br />
5. 5. Presentation- numerical data<br />Example- Comparing scores<br />Data from the “Assertiveness Research Program”<br />eg-“ Does pre-exposure to an assertive situation influence ones’ assertiveness? ”<br />Spreadsheet<br />title<br />Don’t graph scores with different scaling on the same graph<br />Label vertical axis<br />Appropriate scale -according to scores<br />Indicate scores<br />or<br />Label horizontal axis<br />Depending on the detail shown on graph, there may be no necessity to show a table of results <br />
6. 6. Presentation- numerical data<br />Relationships between 2 scores<br />Use a scatter plot<br />Can<br />comment on degree and direction of scatter and/or<br />use line of best fit (no need for equation) and/or<br />use R2 or r values<br />If using R2 or r values, then need to understand their relevance (otherwise no point in using)<br />
7. 7. Presentation- numerical data<br />What are r and R2 values?<br />r - The correlationcoefficient<br />Measures the strength and direction of a linear relationship<br />Is<br />&lt; 0.5 generally described as weak<br />&gt; 0.8 generally described as strong<br />R2 - The coefficient of determination<br />a measure of how well the regression line represents the data<br />represents the %age of the data that is closest to the line of best fit<br />If r = 0.4385, then r 2 = 0.1923, which means that 19% of the total variation in y can be explained by the linear relationship between x and y (as described by the regression equation) The other 81% of the total variation in y remains unexplained.<br />
8. 8. Presentation- numerical data<br />Example- Relationships<br />Data from the “Assertiveness Research Program”<br />eg-“ Does family size influence the relationship between ones’ assertive cognitions and behaviour? ”<br />Spreadsheet<br />title<br />Label vertical axis<br />Appropriate scale -according to scores<br />Label horizontal axis<br />No necessity to show a table of scores being plotted <br />