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To define data. To enumerate various types of data with examples. To know about the various scales of measurement. To enumerate the various sources for collection of data. To explain various methods of presentation of data. To select appropriate method of presentation depending upon the type
of data.
- Facts or figures from which conclusions can be drawn.
- Data can relate to an enormous variety of aspects.
e.g.:Weight and height measurements
of students in a class.Blood pressure and pulse
recording of patients attending medicine opd.Temperature of a city (measured
every hour), for a period of 1 week.
QUALITATIVE DATA and QUANTITATIVE DATA PRIMARY DATA and SECONDARY DATA GROUPED DATA and UNGROUPED DATA
Also called as categorical data. It represents a particular quality or
attribute.
e.g. Colour of hair, Cured or not cured, Religion, Gender, Smoking status, etc.
It represents Numerical Data. E.g. Height in cms, Weight in kgs, Hb in gm%, Serum Bilirubin in gm/dl, BP in mm/hg etc.
It may be Continuous or Discrete
Values are distinct and separate. Values are invariably whole
numbers.
e.g. Age in completed years, Number of OPV vials opened in an immunization session, Number of children in a family, etc.
Those which have uninterrupted range of values. Possibility of getting fractions like
.23, .89, .99Depending on our requirement,
we can express the weight as 51 kg or 50.96 kg.
Presented in groups.Example:Blood Pressure of 9 men can be represented as follows:
1. 120/80 mm Hg (2)2. 140/90 mm Hg (4)3. 150/100 mm Hg (3)
Presented individually.Example: Name Blood Pressure
Person1 140/90 mm Hg
Person2 150/100 mm Hg
Person3 150/100 mm Hg
Person4 140/90 mm Hg
Person5 140/90 mm Hg
Person6 150/100 mm Hg
Person7 140/90 mm Hg
Person8 120/80 mm Hg
Person9 120/80 mm Hg
These are the data directly obtained from the individual.Ex:- Height, Weight, Sex, Religion etc. directly asked from the individual.
These are the data obtained from secondary source.Ex : Census data, Hospital records, etc.
Defined as the application of rules to assign numbers to objects (or attributes). Values made meaningful by quantifying into specific units. Measurements act as labels which make those values more useful in terms of details.
Mr. X is Tall.
Mr. X is 6 feet tall
NOMINALORDINALINTERVALRATIO
When one measures by this scale, one simply names or categorizes the responses.They do not imply any orderingamong the responses.Example:Gender, Religion, Blood group etc.
Characteristics can be put in ordered “natural categories”.There are distinct classes.Can be ordered on the basis of their magnitude.Example:Disease status (advanced, moderate, mild). Pain status ( mild, moderate, severe).Socio economic status, etc.
Observations are made in a scale.Differences between any two successive numbers is fixed and equal.Absolute zero doesn’t exist.Example: Dates, Body Temperature, etc.
Absolute zero exists.Permits the comparison of difference of values. Example:Heart beats per minute, weight, etc.
Quantitative Qualitative
Hb levels in gm% Anemic or Non-anemic
Height in cms Tall or Short
Blood Pressure in mm hg Hypo, Normo or Hypertensive
IQ scores Idiot, Genius or Moron
1. Census2. Registration of vital events3. Sample Registration system4. Notification of diseases5. Hospital records6. Epidemiological Surveillance7. Surveys8. Research Findings
Tabulation
Charts and Diagrams
Usually the first step of presentation and analysis of data.Can be :1. Simple2. Complex(depending on the number of measurements of a single set or multiple sets of items)
Table must be numbered.Brief and self explanatory title.Headings of columns and rows : clear, concise, sufficient and fully defined.Presentation of data : acc. to size of importance, chronologically, alphabetically and geographically.Mention the number of observations from which proportions are derived.
Details of deliberate exclusions must be given. Shouldn’t be too large. Figures needing comparison must be placed as close as possible. Arrangement to be vertical. Footnotes to be given wherever possible.
Table 1: Number of cases of various bites attending ARC, MKCGMCH in Jan2016
Type of bite Cases
Dog 650
Monkey 120
Cat 87
Bear 2
Others 5
TOTAL 864
Table 2: Cases of malaria in adults and children in the months of June and July 2010 MKCG Medical College and Hospital.
ADVANTAGES: Simple Easy to understand Save a lot of words Self explanatoryHas a clear title indicating its content Fully labeled
PRINCIPLES :
Simple (consistent with the purpose) Self explanatory. Title of graph should be written below the graph. Scale Lines should be drawn heavier than coordinate lines. Frequency- Vertical scale.Classification – Horizontal scale.
Qualitative data
• Bar diagram.• Pie or sector diagram.• Venn diagram.• Pictogram or picture diagram.• Map diagram or spot map.
Quantitative data
• Histogram.• Frequency polygon.• Frequency curve.• Line chart or Graph.• Cumulative frequency
diagram or ‘Ogive’.• Scatter diagram.
Widely used.Comparing categories of mutually exclusive discrete data.Different categories represented on one axis. Frequencies of data in each category represented in other axis. Length of bars indicate magnitude of the frequencies of categories to
be compared and spacing should be half of width of the bars Bars arranged in ascending or descending order or any order.. Not
mandatory
Simple bar diagrams
Multiple or compound bar diagrams
Component or proportional bar diagrams
102
62
29
0
20
40
60
80
100
120
P.Vivax P.Falciparum Mixed malaria
Malaria cases in MKCG Hospital in July 2010
Total No cases Male
102
62
29
57
3119
0
20
40
60
80
100
120
P.Vivax P.Falciparum Mixed malaria
Distribution of malaria cases in MKCG Hospital in July 2010
MaleFemale
Used to represent proportionsAreas : represents proportionsAngles : denotes frequencies
monkey37%
dog60%
cat3%
others0%
ANIMAL BITE CASES
For lay manOne form of bar graphs Each picture represents a fixed
no of happenings.
Geographic coordinate chartsUsed for geo coded dataMap of an area within a location
representing the particular area of interest Example: Branding cases found
within rayagada district, Goitreendemic areas of India etc.
Pictorial diagram of frequency distribution Consist of a series of bars. Similar to the bar chart with the
difference that the rectangles or bars are adherent (without gaps). Area of each bar is proportional to the
frequency. Horizontal : Class intervals (abscissa) Vertical : Frequencies (ordinate)
Can be obtained by joining the mid points of blocks or rectangles of histogram More useful than histogram X axis : categories of data Y axis : frequency of data in each
category Representation of distribution of
categories of continuous and ordered data
No of observations are very largeClass intervals reduced Frequency polygon gives rise to a
smooth curve aka frequency curve Ex: Birth weights or height in a
population
Shows trends of events with passage of time Frequency polygon showing
variations via a lineClass intervals chosen can be
hours, days ,weeks, months, etc. May not start from zero.
Represents distribution of continuous and ordered data. Frequency of data in each
category represents sum of data from the category and from preceding categories. Points joined to get cumulative
frequency diagram or ogive.
Age (years) Frequency Cumulative Frequency
10 5 5
11 10 5+10 = 15
12 27 15+27 = 42
13 18 42+18 = 60
14 6 60+6 = 66
15 16 66+16 = 82
16 38 82+38 = 120
17 9 120+9 = 129
A histogram depicting the time course of an illness, disease or abnormality of a particular condition in a particular population in a specified location and time period X axis : Time intervals Y axis : Number of cases in each
time intervalHelps in determining outbreak
characteristics e.g. incubation or latency period, type of disease propagation
Represents quartiles and range of a continuous and ordered set
Show relationship between two variables Also called correlation diagram Clustering of scatter points gives evidence of positive, negative or no corelation
Strong Positive Correlation
All the points lie close to the line of best fit
Weak Positive Correlation
The points are well spread out from the line of best fit but still follow the trend
Shows degrees of overlap and exclusivity between-
2 or more characteristic within a same population
1 characteristic between 2 or more samples
Size of circles need not be equal
Represents the relative size for each factor or population
