NIOS Senior Secondary Economics

Lesson 17: Collection and Presentation of Data

Module 6: Presentation & Analysis of Data
NIOS Textbook Standard Notes

Lesson 17: Collection and Presentation of Data

Master the foundations of economic data, from statistical definitions and primary vs. secondary collection methods to classification (variables vs. attributes) and modern presentation techniques like tabulation and bar diagrams.

1

Meaning & Core Characteristics of Data (Statistics)

Core Definition

Data (or Statistics) refers to quantitative information providing facts in an aggregate manner. Data is a plural term (singular: datum). In economics, quantitative figures provide the objective foundation for decision-making and analytical research.

Crucial Distinction: Single Fact vs. Aggregate of Facts

A single isolated figure (e.g., "Monika secured 95 marks in Mathematics") is merely a fact, NOT statistics, because it offers no comparison. However, marks secured by all 18 students in a class form data because they constitute an aggregate of facts enabling meaningful evaluation.

5 Mandatory Features of Statistics
1. Aggregate of Facts: Single isolated figures cannot be analyzed or compared.
2. Numerically Expressed: Qualitative terms (good, handsome, ugly) are not statistics unless quantified.
3. Affected by Multiplicity of Causes: Influenced by multiple factors (e.g., inflation caused by supply fall, demand rise, taxes).
4. Reasonable Standard of Accuracy: 100% absolute perfection is neither achievable nor required (e.g., 90% medical cure rate).
5. Predetermined Purpose: Collected systematically with a clear prior goal in mind.
2

Importance of Statistical Data in Economics

1. Economic Planning & Forecasting

Past expenditure data guides future budgetary planning (e.g., primary education funding). National income and population growth figures allow economists to forecast per capita income growth accurately.

2. Determination of National Income

Determining a nation's total output requires compiling statistical factor income data: wages/salaries for labor, rent for land, interest for capital, and profits for entrepreneurs.

3. Government Policy Formulation

Census data revealing 938 females per 1000 males in India (and 848 in Haryana) triggered targeted female child protection policies. Poverty data led directly to the MGNREGA 100-day employment guarantee.

3

Types of Data & Primary Collection Methods

A. Primary Data

Original Source

Data originally collected for the first time by the investigator for a specific survey (e.g., interviewing villagers regarding tea/coffee consumption habits).

5 Primary Collection Methods:
  • 1. Direct Personal Investigation: Investigator directly interviews respondents. Highly reliable, but prone to investigator bias.
  • 2. Indirect Investigation: Information gathered from knowledgeable third parties or government commissions.
  • 3. Through Correspondents: Agents appointed in various locations pass data back (widely used by newspaper agencies).
  • 4. Mailed Questionnaire: Structured forms mailed to respondents. Suitable strictly when respondents are literate.
  • 5. Through Schedules: Enumerators/field workers carry schedules and fill in answers in their own handwriting. Essential when respondents are illiterate.

B. Secondary Data

Existing Source

Data that has already been collected and processed by another agency or person. Primary to the collecting agency, but secondary to all subsequent users.

Sources of Secondary Data:
Published Sources:
  • RBI & SEBI Bulletins
  • Trade Association reports
  • Newspaper & financial statements
  • International bodies (UNO, World Bank)
Unpublished Sources:
  • Internal government records
  • Institutional registers
  • University research theses
4

Variables vs. Attributes & Statistical Series

1. Variables vs. Attributes

Variable: Capable of being measured quantitatively in magnitude (e.g., height, weight, income, distance).

  • Discrete Variable: Takes exact whole numerical values with definite breaks (e.g., number of children per family: 0, 1, 2, 3).
  • Continuous Variable: Continuous scale measurement without breaks (e.g., height 60"–62", 62"–64").

Attribute: Qualitative characteristic that cannot be measured in numerical magnitude (e.g., beauty, bravery, intelligence, laziness).

2. Classification into 3 Statistical Series
Individual Series: Items listed individually as raw data or arranged in an Array (ascending or descending order).
Discrete Series: Shows discrete variable values alongside their exact frequencies ($f$). E.g., Marks 30, 40, 50 with frequencies 4, 6, 10.
Continuous Series: Variables grouped into class intervals (e.g., 0–10, 10–20) with frequencies. Constructed using Exclusive or Inclusive methods.
5

Data Presentation: Tabulation, Bar Diagrams & Graphs

A. Tabulation Structure

Tabulation is the systematic presentation of classified data in rows and columns.

TABLE TITLE
Stub (Row Headings)
Caption (Column Headings)
BODY OF TABLE (Data Cells)
Source: Official Report * Footnote
B. Diagrams & Graphs
Simple Bar Diagram (One-Dimensional):

Only the height/length of the bar represents magnitude. Width is uniform across all bars, with equal spacing between them. (e.g., Census birth rates).

Time Series Line Graph:

Plots variable changes over time. X-axis represents time units (years/months), while Y-axis represents the quantitative variable (e.g., student enrollment from 2007 to 2011).