Presentation of data

Data:

A set of values recorded on one or more observational units i.e. Object, person etc

Types of data:

1. Qualitative/ Quantitative data
2. Discrete/ Continuous data
3. Primary/ Secondary data
4. Nominal/ Ordinal data

• Qualitative data:
• also called as enumeration data .
• Represents a particular quality or attribute.
• There is no notion of magnitude or size of the

characteristic, as they can't be measured.

• Expressed as numbers without unit of measurements . Eg: religion, Sex, Blood group etc.
• Quantitative data:
• Also called as measurement data.
• These data have a magnitude.
• Can be expressed as number with or without unit of measurement. Eg: Height in cm, Hb in gm%, BP in mm of Hg, Weight in kg.

• Discrete / Continuous data:

Discrete data: Here we always get a whole number. Eg. Number of beds in hospital, Malaria cases .

Continuous data : it can take any value possible to measure or possibility of getting fractions. Eg. Hb level, Ht, Wt.

• Primary/ Secondary data:

Primary data : Obtained directly from an individual , it gives precise information .

Secondary data : Obtained from outside source ,Eg: Data obtained from hospital records, Census.

• Nominal/ Ordinal data:

Nominal data: the information or data fits into one of the categories, but the categories cannot be ordered one above another . E.g. Colour of eyes, Race, Sex.

Ordinal data: here the categories can be ordered, but the space or class interval between two categories may not be the same. E.g.. Ranking in the class or exam

Collection of data

• Collect data carefully and thoroughly.
• Units of measurements should be clearly defined.
• Record should be correct , complete, clear, sufficiently concise and arranged in a manner that is easy to comprehend.
• Collected data should be
• Accurate (i.e. Measures true value of what is under study)
• Valid( i.e. Measures only what is supposed to measure)
• Precise(i.e. Gives adequate details of the measurement)
• Reliable(i.e. Should be dependable)

Presentation of data

• 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.

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

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• Tabulation
• Charts and diagram

Rules and guidelines for tabular presentation

1. Table must be numbered
2. Brief and self explanatory title must be given to each table.
3. The heading of columns and rows must be clear, sufficient, concise and fully defined.
4. The data must be presented according to size of importance, chronologically, alphabetically or geographically
5. If data includes rate or proportion, mention the denominator.
6. Table should not be too large.
7. Figures needing comparison should be placed as close

as possible.

Continued..

8. The classes should be fully defined, should not lead to any ambiguity.
9. The classes should be exhaustive i.e. should include all

the given values.

10. The classes should be mutually exclusive and non overlapping.
11. The classes should be of equal width or class interval

should be same

12. Open ended classes should be avoided as far as possible.
13. The number of classes should be neither too large nor too small.Can be 10-20 classes.
14. Formula for number of classes(K):

K=1+3.322 log10 N, where N is total frequency

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 various diseases in Nair hospital in 2009

Frequency distribution table with qualitative data:

• Title: Cases of malaria in adults and children in the months of June and July 2010 in Nair Hospital.

Frequency distribution table with quantitative data:

• Fasting blood glucose level in diabetics at the time of diagnosis

Chart and diagram

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

The diagram should be:

• Simple
• Easy to understand
• Save a lot of words
• Self explanatory
• Has a clear title indicating its content
• Fully labeled
• The y axis (vertical) is usually used for frequency

Various charts and diagrams

• Bar Diagram
• Histogram
• Frequency polygon
• Cumulative frequency curve
• Scatter diagram
• Line diagram
• Pie diagram

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.
• Spacing between the various bar should be equal to half of the width of the bar.
• 3 types of bar diagram:

Simple

Multiple or compound Component or proportional

Simple bar diagram:

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P.Vivax

P.Falciparum

Mixed malaria

Total No cases Male

• Multiple bar chart: 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.

Multiple or Compound diagram

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P.Vivax

P.Falciparum

Mixed malaria

Male

Female

• 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.

Component or proportional bar diagram

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90%

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80%

Poor Community

Rich Community

% of energy obtained Fats

% of energy obtained

Protein

% of energy obtained

Carbohdrate

Histogram:

• It is very similar to the bar chart with the difference that the rectangles or bars are adherent (without gaps).
• It is used for presenting class frequency table (continuous data).
• Each bar represents a class and its height represents the frequency (number of cases), its width represent the class interval.

Histogram

Distribution of studied group according to their height

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100-

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number of individuals

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height in cm

Frequency Polygon

• Derived from a histogram by connecting the mid points of the tops of the rectangles in the histogram.
• The line connecting the centers of histogram rectangles is called frequency polygon.
• We can draw polygon without rectangles so we will get simpler form of line graph.
• A special type of frequency polygon is the Normal Distribution Curve.

Frequency polygon

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120- 130- 140- 150- 160- 170- 180-

129 139 149 159 169 179 189

Fasting blood glucose level in diabetics at the time of

diagnosis

No of diabetics

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.

O’give:

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120-129

130-139

140-149

150-159

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170-179

180-189

No of diabetics

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.

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june, 89

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⁴⁵⁰ august, 450

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₃₀₀ july, 304

Malaria cases

Line diagram:

It is diagram showing the relationship between two numeric variables (as the scatter) but the points are joined together to form a line (either broken line or smooth curve. Used to show the trend of events with the passage of time.

Changes in body temperature of a patient after use of antibiotic

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time in hours

temperature

Pie diagram:

• Consist of a circle whose area represents the total frequency (100%) which is divided into segments.
• Each segment represents a proportional composition of the total frequency.

Pie diagram:

P.Vivax 53%

P.Falciparum

32%

Mixed malaria 15%

Thank you