Presenting Data
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Transcript of Presenting Data
Summer Conference 2010
Collaborative & Individual Investigations
Presenting dataUsing statistics to analyse data- how far do you go?
Geoff Slater
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
Presentation
RESULTSTo 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.
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.
Presentation- numerical data
Example- Comparing scores– Data from the “Assertiveness Research Program”
eg-“ Does pre-exposure to an assertive situation influence ones’ assertiveness? ”
– Spreadsheet
Control Group Treatment Group
Mean Cognition Scores
35.6 35.5
12.5
17.5
22.5
27.5
32.5
37.5
42.5
47.5
Mean Cognition ScoresM
ean
Sco
res
title
Label vertical axisAppropriate scale -according to scores
Label horizontal axis
Indicate scores
or
Depending on the detail shown on graph, there may be no necessity to show a table of results
Don’t graph scores with different scaling on the same graph
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)
Presentation- numerical data
What are r and R2 values?
r - The correlation coefficient– 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.
2R
10
102
R
r
15 20 25 30 35 40 4510
15
20
25
30
35
40
45
50
R² = 0.192317634774063
Scatter plot of Cognition - Behaviour Scores for students from a large family
Behaviour Scores
Cogn
ition
Scor
es
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 axisAppropriate scale -according to scores
Label horizontal axis
No necessity to show a table of scores being plotted