Presentation of data

Presentation of data

1. PRESENTATION OF DATA Dr Ratti Ram Meena MBBS, MD (Community medicine)

2. Principles of presentation of data Data should be arranged in such a way that it will arouse interest in reader. The data should be made sufficiently concise without losing important details. The data should presented in simple form to enable the reader to form quick impressions and to draw some conclusion, directly or indirectly. Should facilitate further statistical analysis . It should define the problem and suggest its solution

3. Methods of presentation of data The first step in statistical analysis is to present data in an easy way to be understood. The two basic ways for data presentation are Tabulation Charts and diagram

4. Rules and guidelines for tabular presentation Table must be numbered Brief and self explanatory title must be given to each table. The heading of columns and rows must be clear, sufficient, concise and fully defined. The data must be presented according to size of importance, chronologically, alphabetically or geographically If data includes rate or proportion, mention the denominator.

5. Table should not be too large. Figures needing comparison should be placed as close as possible. The classes should be fully defined, should not lead to any ambiguity. The classes should be exhaustive i.e. should include all the given values. The classes should be mutually exclusive and non overlapping.

6. The classes should be of equal width or class interval should be same Open ended classes should be avoided as far as possible. The number of classes should be neither too large nor too small. Can be 10-20 classes. Formula for number of classes(K): K=1+3.322 log10 N, where N is total frequency

7. Tabulation Can be Simple or Complex depending upon the number of measurements of single set or multiple sets of items. Simple table : Title: Numbers of cases of Non communicable diseases in P B M hospital in 2019 Disease Cases Hypertension 25,000 Diabetes 38,000 Cancer 2,000 Total 65,000

8. Frequency distribution table with qualitative data Title: Cases of Covid-19 in adults and children in the months of July and August 2020 in P B M Hospital Covid-19 cases July 2020 August 2020 Total Adult children Adult children Symptomatic 120 90 320 80 610 Asymptomatic 416 114 612 88 1230 Total 536 204 932 168 1840

9. Frequency distribution table with quantitative data Systolic b lood pressure level in Hypertensive patients at the time of diagnosis Systolic Blood pressure (mm of Hg) No of cases Male Female Total <140 14 13 27 140-160 8 6 14 >160 6 4 10 Total 28 23 51

10. Chart and diagram Graphic presentations used to illustrate and clarify information. are essential in presentation of scientific data and diagrams are complementary to summarize these tables in an easy, attractive and simple way.

11. Charts and diagrams are useful methods of presenting simple data. They have powerful impact on imagination of people. Gives information at a glance. Diagrams are better retained in memory than statistical table.

12. However graphs cannot be substituted for statistical table, because the graphs cannot have mathematical treatment where as tables can be treated mathematically. Whenever graphs are compared , the difference in the scale should be noted. It should be remembered that a lot of details and accuracy of original data is lost in charts and diagrams, and if we want the real study, we have to go back to the original data

13. Common diagrams Pie chart Simple bar diagram Multiple bar diagram Component bar diagram or subdivided bar diagram Histogram Frequency polygon Frequency curve

14. O give curve Scatter diagram Line diagram Pictogram Statistical maps

15. Bar diagram Widely used, easy to prepare tool for comparing categories of mutually exclusive discrete data. Different categories are indicated on one axis and frequency of data in each category on another axis. Length of the bar indicate the magnitude of the frequency of the character to be compared.

16. Spacing between the various bar should be equal to half of the width of the bar. 3 types of bar diagram: Simple Multiple compound Component proportional

17. Simple Bar charts

18. Multiple diagram Each observation has more than one value, represented by a group of bars. Percentage of males and females in different countries, percentage of deaths from heart diseases in old and young age, mode of delivery (cesarean or vaginal) in different female age groups.

19. Multiple or Compound diagram

20. Component bar chart subdivision of a single bar to indicate the composition of the total divided into sections according to their relative proportion. For example two communities are compared in their proportion of energy obtained from various food stuff, each bar represents energy intake by one community, the height of the bar is 100, it is divided horizontally into 3 components (Protein, Fat and carbohydrate) of diet, each component is represented by different color or shape

21. Histogram Used for Quantitative, Continuous, Variables. It is used to present variables which have no gaps e.g age, weight, height, blood pressure, blood sugar etc. It consist of a series of blocks. The class intervals are given along horizontal axis and the frequency along the vertical axis.

22. Frequency polygon Frequency polygon is an area diagram of frequency distribution over a histogram. It is a linear representation of a frequency table and histogram, obtained by joining the mid points of the hitogram blocks. Frequency is plotted at the central point of a group

23. Cumulative frequency diagram or O’give Here the frequency of data in each category represents the sum of data from the category and the preceding categories. Cumulative frequencies are plotted opposite the group limits of the variable. These points are joined by smooth free hand curve to get a cumulative frequency diagram or Ogive .

24. Scatter/ dot diagram Also called as Correlation diagram ,it is useful to represent the relationship between two numeric measurements, each observation being represented by a point corresponding to its value on each axis. In negative correlation, the points will be scattered in downward direction, meaning that the relation between the two studied measurements is controversial i.e. if one measure increases the other decreases  While in positive correlation, the points will be scattered in upward direction.