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Data is the quantitative or qualitative information about some particular characteristic(s) under consideration.


On the basis of its characteristics, data is classified as follows:

  • Quantitative data or cardinal data: A data which can be expressed numerically is called quantitative data.
    The quantitative characteristic which varies for different data is known as a variable.


    Example: Marks of a student, height and weight of a person, population of a town, etc.


  • Qualitative data or ordinal data: A data which is not numerically measurable is called qualitative data. The qualitative characteristic which varies for different data is known as an attribute.

    Example: Colour of a person, nationality of a person, gender of a baby, etc.

Collection of data

Collection of data is the method involved in collecting data through censuses and surveys, or in a routine manner or through other sources.


Data can be broadly classified as primary data and secondary data as described below:
  1. Primary data
    The data which are collected for the first time by the investigator or agency is known as primary data.

Methods of Collecting Primary Data

  1. Observation method: The required data is collected by direct observation by the investigator, using the necessary instruments. Most of the surveys in various scientific, social and economic fields are done by this method.
  2. Interview method: There are two types of interview methods. They are as follows:
  • Personal interview method: In this method, the investigator meets the respondents directly and collects the required information from them. This method is in common use in the study of social and economic problems. This method is suitable for enquiry of limited fields and there is no constraint on the utilization of resources, like time and money.
  • Indirect interview method: This method is used when there are some practical problems in reaching the respondents directly. Rather, the investigator collects the necessary information from a third party who is supposed to possess the information on the problem under investigation.
    This method is suitable where direct sources do not exist or cannot be relied upon.
  1. Mailed questionnaire method: It comprises of a well-drafted and soundly sequenced questionnaire, covering all the important aspects of the problem under consideration and sending them to the respondents with pre-paid stamp after providing all the necessary guidelines for filling the questionnaire.
    This method is employed when the area under investigation is vast. It is inexpensive and consumes less time.
  2. Telephone interview method: It is a quick, rather a non-expensive way to collect primary data where the relevant information can be gathered by the researcher himself by contacting the interviewee over the phone.
    This method is suitable when it is very difficult to meet the respondents personally.

  1. Secondary data
    The data which is not collected by the investigators directly, but is obtained through any other sources is called secondary data.
    Important sources of secondary data are as follows:
    1. Published sources
    • Information collected through newspapers and periodicals
    • International sources like WHO, IMF, World Bank
    • Government sources like CSO, ICAI, etc.
    1. Unpublished sources of various research institutes, researchers, etc.

The data that collected is thoroughly reviewed and presented in a neat and condensed form.

Scrutiny of Data

The data collected should be complete, accurate and reliable.


While collecting secondary data, their validity, completeness and reliability should be verified.

In case of primary data, the investigator, to the best of his judgment, should verify the accuracy of the data.

Error may creep onto the part of the investigator, while writing or copying the information. A keen observer or an experienced investigator can easily detect the type of errors. There may be two or more series of figures which are in some way or other related to each other. If the data for all the series are provided, they may be checked for internal consistency.

Classification of Data

It is the process of arranging data on the basis of the characteristics under consideration into a number of groups or classes according to the similarities of the observations.

  • Objectives of classification of data are:It presents the data in a neat, precise and condensed form, so that it can be easily understood and interpreted
  • It makes comparison possible between various characteristics
  • Statistical analysis is possible only for classified data
  • It eliminates unnecessary details and makes data more readily understandable
    Data may be classified as follows:
  • Quantitative classification: Classification of data on the basis of quantitative characteristics, such as height, weight, etc.
  • Qualitative classification: Classification of data on the basis of qualitative characteristics, such as sex, literacy, etc.
  • Chronological or temporal or time series classification: Classification of data with respect to successive time points or intervals.
  • Geographical classification: Classification of data with respect to locality, country, city, etc.

Presentation of Data

Once the data is collected and verified for their homogeneity and consistency, we need to present them in a neat and condensed form.

Modes of Presentation of Data

The data can be presented in the following ways:


  1. Textual presentation:
  • Presentation of data with the help of a paragraph or a number of paragraphs.
  • This method is very simple and easy to present.
  • Observations with exact magnitude can be presented by this method.
  • Comparison between different observations is not possible by this method.
  • Textual presentation is not preferred by statisticians as it is time consuming, monotonous and not easy to interpret.
  1. Tabular presentation or tabulation:
    Tabulation may be defined as the systematic presentation of data with the help of a statistical table having a number of rows and columns and complete with reference number, title, description of rows as well as columns and foot notes, if any.
    The parts of a table are as follows:
  • Box head
  • Caption
  • Stub
  • Body
  • Foot note
  • Source note

Merits of Tabular Presentation over Textual Presentation

  • It facilitates the comparison between rows and columns
  • Complicated data can also be represented using tabulation
  • Without tabulation, statistical analysis of data is not possible
  1. Diagrammatic representation of data: Presentation of data using graphs is called diagrammatic representation of data.
    Line diagram: When data varies over time, we take recourse to the line diagram. It is used for time related data.

Types of line diagram

  1. Simple line diagram: Here, we plot each pair of values of (t, y). The y(t) values represent time series at time point ‘t’ in the t-y plane. The time variable is taken on the x-axis and other variable is taken on the y-axis and the points are joined successfully using line segments.
  2. Logarithmic or ratio chart: When time series exhibits a wide range of fluctuations, we use logarithmic chart. On y-axis, the logarithmic values of other variables are taken and the points are joined by line segments.
  3. Multiple line diagram: We use this chart when we have to represent two or more related time series data expressed in same units.
  4. Multiple axes diagram: We use this chart when we have to represent two or more related time series data expressed in different units.

Example: Plot the given data of production of a company on a single line diagram.

Time 1st Qtr. 2nd Qtr. 3rd Qtr. 4th Qtr.
Production (tons) 20.4 27.4 90 20.4


Description: 62689.png 


Example: Plot the given data of production of the North, East and West branches of a company on a multi-line diagram.



Description: 62705.png 

The above chart shows the production in three different branches of a company.


Bar diagram: Bar diagrams are of two types:

  1. Horizontal bar diagram: It is used for qualitative data or data varying over space.
  2. Vertical bar diagram: It is used for quantitative data.

Classifications of the bar diagrams on the basis of their use

  • Simple bar diagram: It is used to represent only one characteristic or variate. In this case, the data is presented using vertical bars with their heights proportional to the arranged frequency of the variate. Time is shown on the x-axis and the rectangles are drawn directly according to the frequencies.

    Example: Represent the following data regarding population of a country by simple bar chart.

    City A B C D E F G H
    Population (crore) 28 32 36 44 55 68 85 103

    Description: 62730.png 

  • Multiple or grouped bar diagram: When the bar diagram is used for two or more sets of related data, then it is called a multiple bar diagram.

    Example: Represent the following data by multiple bars.


    Amount in Crores of Rupees
    Imports Exports
    India 20096 12452
    Japan 22244 15674
    Australia 28235 20323
    France 35416 27681


    Description: 62741.png 

  • Sub-divided bar diagram: When a bar diagram represents more than one character of a single variable, then it is called a divided bar diagram. These diagrams are applied for representing data divided into a number of components.
    Represent the following data regarding expenses of three families by sub-divided bars.
    Items​ Monthly Expenses (rupees)
    Family A Family B Family C
    Food 500 1400 2000
    House rent 300 750 1000
    Fuel 150 200 200
    Others 750 1350 2000
    Here, on adding the figures in each of the columns, the total expenditure can be obtained. They are ?1,700, ?3,700 and ?5,200 respectively. The given figures are the components of these total figures. Thus, three bars are sub-divided according to the magnitude of the components. The division of the bars can be easily done by using the cumulative totals of the expenses.

    Description: 62759.png
  • Percentage or divided bar charts: We use this diagram for comparing different components of a variable and also relating the components to the whole. It is also called as divided bar diagram. In this diagram, the only difference is that the total of variable is taken as 100% and each component is expressed in the same bar with different shades of colours. This diagram is used for internal as well as external comparison.
    Following data gives the students strength of a college in different years. Represent the data by percentage bars.
    College Strength
    Boys Girls Total
    A 1200 800 2000
    B 1300 1300 2600
    C 1350 1650 3000
    D 1440 2160 3600
    E 1600 2400 4000
    Here, the total strength has two components. They must be expressed as percentages of the totals. Thus, we have,
    Years Strength (%)
    Boys Girls Total
    A Description: 60827.png 40 100
    B 50 50 100
    C 45 55 100
    D 40 60 100
    E 40 60 100
    Percentage bar diagram showing strength of students
    Description: 63015.png
  • Pie chart or circle diagram: A pie chart is a circular graph which represents the total value with its components. A circle is divided into a number of different sectors representing different components of a variable. We use this for comparing different components of a variable and also relating the components on the whole.
    Represent the following data regarding expenditure of a family by a pie diagram.
    Items Expenditure
    Food 1800
    House rent 900
    Fuel 450
    Education 450
    Misc. 1800
    Total 5400
    Here, the total magnitude is ?5400. This magnitude is represented by a circle with some radius. The circle is drawn taking a suitable scale.

    The circle is divided sector-wise. The angles at the centre, in each case are as follows:
    Items Expenditure Angle (degrees)
    Food 1800 Description: 60879.png
    House rent 900 60
    Fuel 450 30
    Education 450 30
    Misc. 1800 120
    Total 5400 360
    Description: 62769.png 

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