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# Presenting data & stats

Published on: Mar 4, 2016

#### Transcripts - Presenting data & stats

• 1. Presenting Data in Charts,Graphs and TablesMDLB 5100 – Principles of Research DesignCatherine Loc-Carrillo, Ph.D.
• 2. Learning Objectives identify the types of charts and graphs and when the use of each is appropriate
• 3. Purpose of displaying data Figures and graphs help the reader visualize the story Can make information easier to understand and remember
• 4. General rules for displayingdata K.I.S.S. Use clear descriptive titles and labels Provide a narrative description of the highlights
• 5. A good graph Includes: axes, labels, scales, symbols and legend Should draw attention to data not itself Data points should be easy to read Labels are easy to read and distinguish The scales used should match the range of the data The legend is clear and concise The data deserved to be graphed Visually balanced axes (i.e. ratio of 1.0 to 1.3)
• 6. Line graphs &scattergrams Use horizontal and vertical axis Illustrates the relationship between 2 or more variables  Independent variable (plotted on x axis) can be manipulated or changed by the investigator  Dependent variable (plotted on y axis) depends on the value of the independent variable Use for continuous variables
• 7. Scattergrams When displaying data that is distributed over an area, bar graphs can have limitations  The ‘mean’ and SD, or median and 95% CI can be easier to display with a scattergram Source: Annesley (2010) ClinChem 56(8): 1394-1400
• 8. Example 1 Line graph  Symbols representing doses are clear and easy to distinguish  Connecting lines are clear  The scale is proportional to the range of values  Minimal wasted space throughout graph  Legend is concise and easy to understand Source: Annesley (2010) ClinChem 56(8): 1229-1233
• 9. Exercise 1: How can thegraph be improved? Symbols Lines Text size Use of decimal point Wasted space The ratio of axis Source: Annesley (2010) ClinChem 56(8): 1229-1233
• 10. Bars graphs and pies charts Used when variables are discontinuous Bar graphs are useful for visual comparisons of data
• 11. Bar graphs Used to plot a set of discontinuous independent variables versus continuous dependent variables Consider:  The space between the bars should be narrower than the width of the bars  Use shading or patterns that help distinguish bars from one another  Avoid use of a suppressed-zero scale  Y-axis represents frequency  X-axis may represent time or different classes
• 12. Exercise 2: Which graph isbest and why? Source: Annesley (2010) ClinChem 56(8): 1394-1400
• 13. Pie charts A circular drawing divided into segments  Each segment reflects proportion of total area Pie charts are most understandable if the number of categories is limited to <6 These charts are most accurate when all available data or outcomes are included Source: Annesley (2010) ClinChem 56(8): 1394-1400
• 14. Tables A rectangular arrangement of data in which the data are positioned in rows and columns. Each row and column should be labelled. Rows and columns with totals should be shown in the last row or in the right-hand column. Region Adults and adolescents Children <15 Total ≥ 15 years years 1 14 800 200 15 000 2 400 000 20 000 420 000 3 997 000 3 000 1 000 000 4 985 000 15 000 1 000 000 5 1 460 000 40 000 1 500 000 6 465 000 35 000 500 000 7 940 000 10 000 950 000 8 380 000 220 000 600 000 9 900 000 600 000 1 500 000 10 545 000 5 000 550 000 Total 7 086 800 948 200 8 035 000
• 15. Making friends with yourdata Statistical results should be clear and suitable for the typical reader The statistics conducted should be appropriate  There isn’t a ‘right’ one  The data should be analyzed in several ways  Do the results convey meaningful information Don’t be afraid to learn new statistical procedures  Don’t try to impress the readers with statistical complexities
• 16. Presenting the results A 2nd year undergraduate should be able to read the results section and know what the main findings are Don’t report the same stats more than once ’Statistical significance’  Is a result where chance is an unlikely explanation  i.e. p> 0.05 When p> 0.001, report the actual number, but when p< 0.001 then just report as is  Don’t report p=0
• 17. Take home message Researchers spend months/years collecting their data then throw it into a computer and try to analyze in minutes The data deserves better! Quick and reckless approach to data analysis often fails to identify important aspects of the data