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
 Data presentation:
– Refers 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.
 To portray the actual picture of burden of diseases
pattern and trends over years.
 To identify problems that need new health
interventions.
 Budget 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 should 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 distribution
 “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
frequency distribution
 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
Smear-ve, culture-ve IIII IIII …IIII 144
Smear-ve, culture not done IIII IIII …IIII 40
Smear+ve, culture+ve IIII IIII …IIII 45
Note: IIII indicates 4 observations

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

22. Frequency distribution
0
20
40
60
80
100
120
140
160
Smear -ve, culture -ve Smear -ve, culture not
done
Smear +ve, culture +ve
Frequency
Category
Results of sputum examination
Bar Chart

23. Frequency distribution
Frequency polygon/Line graph
0
20
40
60
80
100
120
140
160
Smear -ve, culture -ve Smear -ve, culture not
done
Smear +ve, culture +ve
Frequency
Category
Results of sputum examination

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