1. Summer Conference 2010 Collaborative & Individual Investigations Presenting data Using statistics to analysedata- how far do you go? Geoff Slater

2. Need to keep in mind……. A lot of our students do not have a maths/science background They should not be disadvantaged SO….statistical analysis should be kept to a basic level of understanding to what is stated in the curriculum statement

3. Presentation RESULTS To what extent are the data appropriately organised and presented by the student? For numerical data Tables, graphs, histograms etc are presented and labelled appropriately Use graphs-statistics appropriate to the question in the proposal Raw data not is required For Qualitative Data Provide a summary of themes, their frequencies, relevant quotes and excerpts (illustrative comments from focus group data)- can be in tabular form.

4. Presentation- numerical data COMPARISON OF SCORES compare mean scores, or compare median scores or use both- will depend on data eg- there may be some extreme scores (outliers) Could use other statistics (but usually not necessary) eg- standard deviation, box plots (quartiles etc…), normal distributions (data is usually not normally distributed anyway) Note- important not to penalise those students who do not have a good statistical background.

5. Presentation- numerical data Example- Comparing scores Data from the “Assertiveness Research Program” eg-“ Does pre-exposure to an assertive situation influence ones’ assertiveness? ” Spreadsheet title Don’t graph scores with different scaling on the same graph Label vertical axis Appropriate scale -according to scores Indicate scores or Label horizontal axis Depending on the detail shown on graph, there may be no necessity to show a table of results

6. Presentation- numerical data Relationships between 2 scores Use a scatter plot Can comment on degree and direction of scatter and/or use line of best fit (no need for equation) and/or use R2 or r values If using R2 or r values, then need to understand their relevance (otherwise no point in using)

7. Presentation- numerical data What are r and R2 values? r - The correlationcoefficient Measures the strength and direction of a linear relationship Is < 0.5 generally described as weak > 0.8 generally described as strong R2 - The coefficient of determination a measure of how well the regression line represents the data represents the %age of the data that is closest to the line of best fit 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.

8. Presentation- numerical data Example- Relationships Data from the “Assertiveness Research Program” eg-“ Does family size influence the relationship between ones’ assertive cognitions and behaviour? ” Spreadsheet title Label vertical axis Appropriate scale -according to scores Label horizontal axis No necessity to show a table of scores being plotted