Data Presentation Methods.pptx

data presentation

1. Data Presentation Methods

2. Learning objectives By the end of the session, students should be able to: Define the term “data presentation”. Describe the need for effective presentation of data. Explain the methods of data presentation. Present data.

3. Definition D ata presentation: R efers to the “organization of data into tables, graphs or charts, so that logical and statistical conclusions can be derived from the collected measurements”.  

4. Need for effective presentation of data To assess health system performance to determine levels of efficiency at each level of facilities and needs for additional health staff and equipment. T o portray the actual picture of burden of diseases pattern and trends over years. To identify problems that need new health interventions. B udget plan. International health comparison. Attracting financial support from international organizations, unilateral and bilateral donors.

5. Methods of data presentation Tables. Charts/graphs: Pie, bar, histogram/column etc. Maps/pictorial. Narrative/story/tale.

6. Summary tables

7. Summary tables (2x2 tables) Statistical information on two variables can be presented simultaneously in a form of a two-way table . This table makes the information easier to assimilate by showing many of the properties of the data at a glance . In a two-by-two table, data are presented in rows and columns. The format for a table depends upon the data and the aspects of the data which are important to portray.

8. Summary tables (2x2 tables)… A two-way table should include the following: A clear title. A caption for the rows and columns with units of measurement of the variable. Labels for each individual row or column i.e. the values taken by the variable concerned. Marginal and grand totals. A two-way table s hould be used for small datasets for comparison.

9. One categorical variable

10. Two categorical variables

11. Charts/Graphs

12. Histograms A histogram is a way of summarizing data that are measured on an interval scale (either discrete or continuous ). The various categories of a variable are represented on the horizontal axis and the frequency or relative frequency is represented on the vertical axis. The length of each column represents the number of observations (frequency) in each category or the relative frequency in percentage . Histograms should be used for any data where there are no gaps between the categories.

13. Histograms…

14. Bar charts A bar graph is a way of summarizing a set of categorical data. It displays the data using a number of rectangles, of the same width, each of which represents a particular category. Bar graphs can be displayed horizontally or vertically and they are usually drawn with a gap between the bars (rectangles ). Bar charts should be used for comparison between discrete categories.

15. Bar chart…

16. Pie charts These are used to express the distribution of individual observations into different categories. Note that the frequencies should be converted into percentages totaling 100 for a pie chart to be used. Pie charts should be used for 3-7 categories only.

17. Frequency d istribution “ A presentation of the number of times (or the frequency) that each value/category (or group of values/categories) occurs in the study population”. It helps to give a picture of the shape of the distribution of the data. A frequency distribution can be displayed as: A table/bar chart/histogram or a frequency polygon. Each method should be clearly labelled with the frequency number. The method usually depends on the type of variable being described.

18. Frequency distribution… Relative frequency distribution: A frequency taken by a value relative to total frequency of a variable . Cumulative relative frequency distribution: The accumulated relative frequency of distributions as the value of the variable increases.

19. Use of Tallies in making f requency d istribution A frequency distribution is normally formed (manually) by a process known as tallying. This involves the following steps: Scan the data and determine the categories. List the categories. Work through the data and allocate each observation to the category where it belongs using the tally marks to keep a count of the number in each category. Add the tally marks to give the frequency.

20. Using Tallies … The following data show a quantitative variable ‘ Results of sputum examination’. If : 1 = Smear negative (– ve ), culture negative (– ve ) 2 = Smear negative (– ve ), culture not done 3 = Smear positive (+ ve ), culture positive (+ ve ) 1 2 1 1 3 1 1 3 3 2 1 3 1 1 2 3 1 1 3 1 2 3 1 1 3 1 1 3 1 3 1 3 2 1 1 3 1 1 2 1 1 2 3 1 1 1 2 1 2 2 3 1 1 2 1 3 1 1 1 1 1 2 1 3 1 1 3 1 1 1 2 1 1 1 3 2 3 3 3 1 1 1 2 1 1 1… Category Tally Frequency S m ear- ve , cu l ture- ve IIII IIII … IIII 144 S m ear- ve , cu l ture not done IIII IIII … IIII 40 S m ea r +ve , culture+ve IIII IIII … IIII 45 Note: I I II i n dicates 4 observations

21. Relative frequency table Category Frequency R elative Frequency Cumulative Relative Frequency S m ear - ve , culture - ve 144 62.9 62.9 S m ear -ve, culture not done 40 17.5 80.4 S m ear +ve, culture +ve 45 19.6 100.0 Total 229 100.0 100.0

22. Frequency distribution Bar C hart

23. Frequency distribution Frequency polygon /Line graph

24. Maps/Pictorial

25. Meningitis belt of Sub Saharan Africa

26. Narrative/Story

27. Exercise - 1 How are you going to present the below data? FP methods Percentage (%) Abstinence 3 COC 32 Depo Provera 9 Loop 17 Spermicides 7 Condoms 26 Vasectomy 3 Hysterectomy 2 Norplant 1

28. Key points The term “data presentation” means organization of data into tables, graphs or charts, so that logical and statistical conclusions can be derived from the collected measurements.  There is a need to present epidemiological and biostatistical data effectively. Data can be summarized and presented by using a variety of methods such as tables, charts, graphs, maps, pictures and narratives depending on the type data and purpose.

29. Evaluation What does the term “data presentation” mean? Why do we need to present data effectively? What methods can be used to present data?

30. References Bonita R. et al. (2006). Basic Epidemiology (2nd ed.). Geneva, Switzerland: WHO. Jones D. et al. (2008). Biostatistics. Work Book-Field Epidemiology and Laboratory Training Programs (FELTP). McCusker J. (2001). Epidemiology in Community Health, Rural Health Series No. 9 (Revised Edition). Nairobi, Kenya: AMREF. Rosner B. (2006). Fundamentals of Biostatistics (6th ed.). Australia, Canada, Singapore, Spain, United Kingdom, United States: Thomson Brookes/Cole. Varkevisser et. al. (1995). Designing and Conducting Health Systems Research Projects, Volume 2 Part 2 Module 24. Health Systems Research Training Series . http:// academic.sun.ac.za/emergencymedicine/TRRM/module5/BS1-3.htm

31. Thank You!