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Collection of Data

NCERT Class 11 · Economics Based on NCERT Class 11 Economics textbook · Free CBSE study kit

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Chapter Notes

Overview
This chapter introduces the fundamental concepts of data collection in statistics for economics. It explains why data is essential for economic analysis, distinguishes between primary and secondary data, and details the various methods of collecting data through surveys. The one big idea a student must walk away with is that the quality and reliability of any economic conclusion depend entirely on how the data was collected, and that choosing between census and sample surveys, and between random and non-random sampling, directly affects the accuracy of the results.
Key Concepts (26)
Variable
A variable is a quantity that varies from one observation to another, typically represented by letters X, Y, or Z.
e.g. Food grain production in India varies from 108 million tonnes in 1970-71 to 272 million tonnes in 2016-17; the years are variable X and production is variable Y.
Observation
Each value of a variable is called an observation.
e.g. The food grain production of 132 million tonnes in 1978-79 is one observation.
Data
Data is a tool which helps in understanding problems by providing information; it consists of the values of variables.
e.g. The table showing production of food grains in India for different years is data.
Primary Data
Primary data are based on first-hand information, collected by the researcher directly through an enquiry.
e.g. Asking school students about the popularity of a filmstar to collect information directly from them.
Secondary Data
Secondary data are data that have been collected and processed by some other agency and are used by a researcher for a different purpose.
e.g. Using the report published by the researcher on filmstar popularity for a similar study.
Survey
A survey is a method of gathering information from individuals by asking questions.
e.g. A manufacturer asking a large group of people about a product's price, quality, and usefulness.
Questionnaire
A questionnaire is the most common type of instrument used in surveys; it can be self-administered or administered by the researcher.
e.g. A set of printed questions given to respondents to fill out about their spending habits.
Interview Schedule
An interview schedule is a questionnaire that is administered by the researcher or trained investigator in a face-to-face setting.
e.g. An enumerator reading questions to a respondent and recording their answers.
Closed-ended (Structured) Questions
Questions that have a fixed set of possible answers, either two-way (yes/no) or multiple choice.
e.g. How much do you spend on books in a month? (i) Less than Rs 200 (ii) Rs 200–300 (iii) Rs 300–400 (iv) More than Rs 400.
Open-ended (Unstructured) Questions
Questions that allow respondents to answer in their own words without being restricted to pre-set options.
e.g. What is your view about globalisation?
Pilot Survey
A pilot survey is a try-out of the questionnaire with a small group before the actual survey, also known as pre-testing.
e.g. Testing a questionnaire on 10 students before conducting a full survey on 500 students.
Census (Complete Enumeration)
A survey that includes every element of the population, covering all units.
e.g. The Census of India conducted every ten years covering all households in India.
Population (Universe)
Population or Universe in statistics means the totality of items under study, to which the results of the study are intended to apply.
e.g. All agricultural labourers in Churachandpur district of Manipur.
Sample
A sample refers to a group or section of the population from which information is to be obtained; a good sample is representative and smaller than the population.
e.g. Ten per cent of the agricultural labourers in Churachandpur district.
Representative Sample
A sample that is capable of providing reasonably accurate information about the population at a much lower cost and shorter time.
e.g. Selecting 30 households from 300 in a locality to study the impact of petrol price rise.
Random Sampling
A sampling method where every individual in the population has an equal chance of being selected.
e.g. Writing names of all 300 households on paper, mixing them, and drawing 30 names.
Lottery Method
A method of random sampling where names or numbers are written on slips, mixed, and drawn by chance.
e.g. Selecting 3 students out of 10 by writing their names on slips and drawing from a bowl.
Non-Random Sampling
A sampling method where all units of the population do not have an equal chance of being selected; convenience or judgement of the investigator plays a role.
e.g. Selecting 10 households out of 100 that are conveniently situated or known to the researcher.
Sampling Error
The difference between the sample estimate and the corresponding population parameter (actual value).
e.g. Population average income is 600, sample average is 550; sampling error = 50.
Non-Sampling Errors
Errors that arise from sources other than sampling, such as sampling bias, non-response errors, and errors in data acquisition.
e.g. Recording 13 instead of 31 due to a mistake by the enumerator.
Sampling Bias
Sampling bias occurs when the sampling plan is such that some members of the target population could not possibly be included in the sample.
e.g. Only surveying people who own telephones when studying all households.
Non-Response Errors
Errors that occur when an interviewer is unable to contact a person listed in the sample or a person refuses to respond.
e.g. A selected household is not at home when the enumerator visits.
Errors in Data Acquisition
Errors arising from recording of incorrect responses, including measurement differences and recording mistakes.
e.g. Students measuring the same table get different lengths due to different measuring tapes.
Census of India
The most complete and continuous demographic record of population, conducted every ten years since 1881.
e.g. The 2011 Census recorded India's population as 121.09 crore.
National Sample Survey (NSS)
An agency established by the Government of India to conduct nationwide surveys on socio-economic issues in successive rounds.
e.g. NSS 68th round (2011-12) surveyed consumer expenditure.
Sarvekshana
The quarterly journal of the National Sample Survey (NSS) that releases collected data.
e.g. NSS data on employment and unemployment are published in Sarvekshana.
Formulas & Equations (2)
Sampling Error = Population Parameter – Sample Estimate
Reads as: Sampling error equals population parameter minus sample estimate
Variables: Population Parameter: the actual value of the characteristic (e.g., average income); Sample Estimate: the value calculated from the sample
Use when: When calculating the difference between the true population value and the estimate obtained from a sample
⚠ Students often write Sample Estimate – Population Parameter, which gives the wrong sign; the correct formula is Population Parameter minus Sample Estimate.
Average (Mean) = Sum of all observations / Number of observations
Reads as: Average equals sum of all observations divided by number of observations
Variables: Sum of all observations: total of all values; Number of observations: count of values
Use when: Calculating the central tendency of a dataset, e.g., average income of farmers
⚠ Forgetting to divide by the correct number of observations, especially when some values are repeated.
How It Works
Conducting a Survey
  1. Step 1: Identify the purpose of the survey and the characteristics to be studied (e.g., price, quality, popularity).
  2. Step 2: Prepare the instrument — a questionnaire or interview schedule — following guidelines (short, clear, non-leading, etc.).
  3. Step 3: Choose the mode of data collection: personal interview, mailing questionnaire, or telephone interview.
  4. Step 4: Conduct a pilot survey (pre-testing) with a small group to identify shortcomings in the questionnaire.
  5. Step 5: Revise the questionnaire based on pilot survey feedback.
  6. Step 6: Administer the final survey to the selected sample or population.
  7. Step 7: Collect, process, and tabulate the data for analysis.
💡 The pilot survey is often forgotten by students — it is a crucial step to pre-test the questionnaire and assess cost, time, and clarity of instructions.
Selecting a Random Sample using the Lottery Method
  1. Step 1: Identify the sampling frame — list all units in the population (e.g., all 300 households).
  2. Step 2: Write each unit's name or identifier on a separate slip of paper.
  3. Step 3: Place all slips into a container and mix them thoroughly.
  4. Step 4: Draw the required number of slips (e.g., 30) one by one without looking.
  5. Step 5: The units whose slips are drawn constitute the random sample.
💡 Every unit must have an equal chance of being selected; mixing thoroughly is essential to ensure randomness.
Conducting a Census
  1. Step 1: Define the population to be covered (e.g., all households in India).
  2. Step 2: Train a large team of enumerators.
  3. Step 3: Conduct a house-to-house enquiry covering every single unit.
  4. Step 4: Collect demographic data (birth, death, literacy, employment, etc.).
  5. Step 5: Process, tabulate, and publish the data (e.g., by Registrar General of India).
💡 Census is complete enumeration — it covers every element, unlike a sample survey. It is conducted every ten years in India.
Designing a Good Questionnaire
  1. Step 1: Keep the questionnaire short with minimum number of questions.
  2. Step 2: Use simple, unambiguous language — avoid difficult or ambiguous words.
  3. Step 3: Arrange questions from general to specific (e.g., start with regularity of electricity supply, then ask about increase in charges).
  4. Step 4: Make questions precise and clear (e.g., 'What percentage of your income do you spend on clothing?' not '...in order to look presentable?').
  5. Step 5: Avoid double negatives (e.g., 'Do you think smoking should be prohibited?' not 'Don't you think...').
  6. Step 6: Avoid leading questions that suggest an answer (e.g., 'How do you like the flavour of this tea?' not '...this high-quality tea?').
  7. Step 7: Do not indicate alternatives in the question (e.g., 'What would you like to do after college?' not '...job or housewife?').
  8. Step 8: Decide on closed-ended (structured) or open-ended (unstructured) questions as appropriate.
💡 The most common mistake students make in exams is confusing 'leading questions' with 'double negatives' — they are different errors. A leading question gives a clue about the desired answer; a double negative uses 'wouldn't you' or 'don't you'.
Exam Traps — Don't Make These Mistakes
⚠ Confusing Primary and Secondary Data
✗ Data collected by a researcher from a published government report is primary data.
→ Primary data is first-hand information collected directly by the researcher. If the data is already published by another agency, it becomes secondary data for the researcher who uses it.
✓ Data collected by a researcher directly from respondents through a survey is primary data. Data obtained from published government reports is secondary data.
⚠ Thinking Census is Always Better than Sample
✗ Census is always better than a sample survey because it covers everyone.
→ While census gives complete information, it is extremely expensive, time-consuming, and may still contain non-sampling errors. Sample surveys can provide reasonably accurate information at lower cost and shorter time, and allow more detailed enquiry.
✓ Sample surveys are often preferred because they provide reasonably reliable and accurate information at lower cost and shorter time, and allow intensive enquiry with a smaller, better-trained team.
⚠ Mistaking Non-Sampling Error as Less Serious
✗ Sampling error is more serious than non-sampling error.
→ Non-sampling errors are more serious because they cannot be minimised even by taking a larger sample, and they can occur even in a census. Sampling error can be reduced by increasing sample size.
✓ Non-sampling errors are more serious than sampling errors because they cannot be minimised by taking a larger sample and can affect even a complete census.
⚠ Confusing Random and Non-Random Sampling
✗ Selecting households that are conveniently located is an example of random sampling.
→ In random sampling, every unit has an equal chance of being selected. Selecting conveniently located households involves the investigator's judgement or bias, which is non-random sampling.
✓ Selecting households based on convenience or judgement is non-random sampling. In random sampling, every unit must have an equal chance of selection, e.g., using the lottery method.
⚠ Misidentifying Leading Questions
✗ The question 'Don't you think smoking should be prohibited?' is a leading question.
→ This question is actually an example of a double negative ('Don't you...'), not a leading question. A leading question gives a clue about how the respondent should answer, e.g., 'How do you like the flavour of this high-quality tea?'
✓ The question 'Don't you think smoking should be prohibited?' is a double negative, not a leading question. A leading question example is 'How do you like the flavour of this high-quality tea?' which suggests the tea is high-quality.
Compare & Contrast
Source of data
Primary Data
Secondary Data
Primary data is first-hand information collected directly by the researcher for the first time; secondary data is data already collected and processed by another agency and used by a different researcher.
Coverage of population
Census (Complete Enumeration)
Sample Survey
Census covers every element of the population, while a sample survey covers only a representative section of the population.
Chance of selection
Random Sampling
Non-Random Sampling
In random sampling, every unit has an equal chance of being selected; in non-random sampling, convenience or judgement of the investigator determines selection, and units do not have equal chance.
Cause of error
Sampling Error
Non-Sampling Error
Sampling error arises from the difference between the sample estimate and the population parameter and can be reduced by taking a larger sample; non-sampling error arises from bias, non-response, or data acquisition mistakes and cannot be minimised by increasing sample size.
Exam Focus
Marks pattern: 2 MCQs (1 mark each), 1 Very Short Answer (1 mark), 1 Short Answer (3 marks), 1 Long Answer (5 marks) — total approximately 11 marks
Most asked: Difference between primary and secondary data · Difference between census and sample survey · Random vs non-random sampling · Sampling error vs non-sampling error · Characteristics of a good questionnaire · Modes of data collection (personal interview, mailing, telephone)
Don't skip: Distinguish between primary and secondary data with examples (3-5 marks); Explain the difference between census and sample survey, giving advantages and disadvantages (5 marks); What is random sampling? Explain the lottery method (3 marks); Distinguish between sampling error and non-sampling error (3 marks)
One-Line Revision (read aloud before the exam)
  1. Data is a tool that helps in understanding economic problems by providing information.
  2. Primary data is first-hand information collected directly by the researcher.
  3. Secondary data is data already collected and processed by another agency.
  4. A survey is a method of gathering information from individuals by asking questions.
  5. A questionnaire should be short, clear, and arranged from general to specific questions.
  6. Leading questions give a clue about how the respondent should answer and must be avoided.
  7. Double negatives like 'Don't you think...' should be avoided in questionnaires.
  8. Closed-ended questions have fixed options; open-ended questions allow free responses.
  9. A pilot survey is a pre-testing of the questionnaire on a small group before the actual survey.
  10. Census covers every element of the population; a sample survey covers only a representative part.
  11. In random sampling, every unit has an equal chance of being selected.
  12. The lottery method involves writing names on slips, mixing them, and drawing randomly.
  13. Non-random sampling uses convenience or judgement of the investigator.
  14. Sampling error is the difference between the sample estimate and the population parameter.
  15. Non-sampling errors are more serious because they cannot be minimised by taking a larger sample.
  16. Sampling bias occurs when some members of the population cannot be included in the sample.
  17. Non-response error occurs when a selected person cannot be contacted or refuses to respond.
  18. Errors in data acquisition arise from recording incorrect responses or measurement mistakes.
  19. The Census of India is conducted every ten years since 1881, providing demographic data.
  20. The National Sample Survey (NSS) conducts nationwide surveys on socio-economic issues in successive rounds.
  21. NSS data is published through reports and its quarterly journal Sarvekshana.

MCQs — 10 Questions with Answers

Q1. Which of the following is an example of primary data?

  • A. Data published in a government census report
  • B. Data collected directly by a researcher through a survey of school students ✓
  • C. Data from a newspaper article about inflation
  • D. Data found in a textbook about historical food grain production

Answer: B — Primary data is collected first-hand by the researcher through direct enquiry; options A, C, and D are secondary data already published by other sources.

Q2. Which statement correctly distinguishes primary and secondary data?

  • A. Primary data is always more accurate than secondary data
  • B. Secondary data is collected by the researcher; primary data is obtained from published sources
  • C. Primary data is collected first-hand; secondary data is already collected and processed by another agency ✓
  • D. Primary data is cheaper to collect than secondary data

Answer: C — This is the correct definition: primary data is original first-hand collection, while secondary data is already processed by someone else; option B reverses the definitions.

Q3. What is a variable in statistics?

  • A. A fixed value that does not change
  • B. A characteristic or quantity that changes from one observation to another ✓
  • C. The total number of observations collected
  • D. The average value of all observations

Answer: B — A variable is any quantity that varies across observations; for example, food grain production (Y) varies across years (X).

Q4. A researcher wants to study the average monthly income of farmers in a district with 50,000 farms. Which approach would be MORE practical?

  • A. Census survey of all 50,000 farms
  • B. Sample survey of 2,000 randomly selected farms ✓
  • C. Secondary data from government reports only
  • D. Open-ended questionnaire sent to all farms

Answer: B — A sample survey is practical, faster, and cheaper than a census of all 50,000 farms while still providing reliable results; option A is too costly and time-consuming.

Q5. Which of the following is a problem with a LEADING QUESTION?

  • A. It is too long and difficult to understand
  • B. It hints at the expected answer and biases the respondent's reply ✓
  • C. It contains a double negative like 'Don't you think'
  • D. It uses technical jargon that respondents cannot understand

Answer: B — A leading question suggests a desired answer (e.g., 'How do you like this high-quality tea?'), influencing respondents to answer in that direction rather than expressing true opinion.

Q6. Which questionnaire design principle is CORRECTLY applied?

  • A. Start with specific personal questions, then move to general questions
  • B. Arrange questions from general to specific, moving from comfortable to sensitive topics ✓
  • C. Use double negatives like 'Don't you think smoking should be prohibited?'
  • D. Include leading phrases like 'this high-quality product' in the question

Answer: B — Correct questionnaire design arranges questions from general to specific to build comfort; options A, C, and D violate questionnaire design principles.

Q7. A poor questionnaire asks: 'Do you spend a lot of money on books every month?' Which revision BEST improves this question?

  • A. Don't you think spending a lot on books is wasteful?
  • B. How much do you spend on books in a month: (i) Less than Rs 200 (ii) Rs 200-300 (iii) More than Rs 300? ✓
  • C. Do you like reading books and spend money on them?
  • D. Isn't it true that books are expensive and you avoid buying them?

Answer: B — Option B removes ambiguity by providing clear, measurable categories instead of vague terms like 'a lot'; it prevents misinterpretation and is easy to analyse.

Q8. Which is NOT a correct statement about closed-ended questions?

  • A. They offer fixed options like yes/no or multiple choices
  • B. They are easy to analyse and codify for statistical processing
  • C. They always capture the complete true response of every respondent ✓
  • D. They may omit valid responses not listed in the alternatives

Answer: C — Closed-ended questions may miss true responses if the actual answer is not among the given options; options A, B, and D correctly describe closed-ended questions.

Q9. According to the material, India's food grain production was 108 million tonnes in 1970–71 and rose to 272 million tonnes in 2016–17. In this data, which of the following is correctly identified? (i) Year (X) is a variable (ii) Production (Y) is a variable (iii) Each production value (e.g., 108) is an observation (iv) Variables are represented by numbers only, never letters

  • A. (i) and (ii) are correct
  • B. (i), (ii), and (iii) are correct ✓
  • C. Only (ii) is correct
  • D. All four statements are correct

Answer: B — Both year and production are variables (they change), and each value is an observation; statement (iv) is false because variables are represented by letters like X and Y.

Q10. A researcher collects data by directly interviewing 300 farmers about their annual income. Later, an economist uses this same data from the researcher's published report. For the researcher, this data is PRIMARY, but for the economist, it is SECONDARY because:

  • A. The data becomes less accurate when reused
  • B. The researcher collected it first-hand, but the economist obtained it from an already published source ✓
  • C. The economist is studying a different topic than the researcher
  • D. Secondary data is always older than primary data

Answer: B — Data is primary to the source that collects it first and secondary to anyone else who uses the already-processed data from a published source; the same data changes status based on who is using it and when.

Flashcards

What is primary data?

Data collected directly by the researcher through first-hand enquiry or survey.

What is secondary data?

Data already collected, processed, and published by another agency or source.

Define a variable in statistics.

A characteristic or quantity that changes or varies from observation to observation.

What is an observation?

A single value or measurement of a variable in a dataset.

What is a questionnaire?

A set of carefully designed questions used to collect data from respondents in a survey.

Distinguish between closed-ended and open-ended questions.

Closed-ended questions offer fixed options (yes/no or multiple choice); open-ended questions allow respondents to write their own answer.

Why should a questionnaire avoid double negatives?

Double negatives (like 'Don't you think...') confuse respondents and can bias answers.

What is a leading question and why is it problematic?

A leading question hints at the expected answer, biasing the respondent's response instead of capturing true opinion.

Name three sources of secondary data in India.

Government reports, census documents, newspapers, books by economists, and official websites like RBI or Ministry of Statistics.

Why is a questionnaire arranged from general to specific questions?

This order makes respondents comfortable by starting easy, building rapport before asking sensitive or detailed questions.

Important Board Questions

Define primary data and secondary data with one example each from the Indian economic context. [2 marks]

Primary = first-hand collection by researcher (e.g., survey of farmers about crop yield); Secondary = already published by others (e.g., RBI inflation data or census reports).

A researcher wants to design a questionnaire to study the awareness of government agricultural schemes among rural farmers in Maharashtra. Identify and correct THREE common questionnaire design errors shown below: (i) 'Don't you think government schemes are helpful?' (ii) 'How much do you earn annually?' (iii) 'Would you prefer job training or subsidy assistance?' (shows alternatives) Explain why each error must be corrected and rewrite the improved versions. [5 marks]

Error (i) = double negative (bias); Error (ii) = vague with no options (ambiguous); Error (iii) = leading with pre-set alternatives; rewrite using simple language, offer realistic response categories, and avoid bias-inducing phrasing.

Discuss the practical advantages and limitations of using a SAMPLE SURVEY instead of a CENSUS for studying the income and employment patterns of 2 lakh workers in India. Use relevant examples and explain when a researcher might choose one over the other. [6 marks]

Sample Survey: cheaper, faster, practical for large populations but has sampling error and may not capture all subgroups; Census: complete and accurate but very costly and time-consuming; justify choice based on research objective, budget, and time constraints with Indian economic example (e.g., Labour Force Survey vs Population Census).

Assertion-Reason Questions for Collection of Data

Q1. Assertion (A):

Primary data collected through surveys is always more accurate than secondary data obtained from published government reports.

Reason (R):

Secondary data have already been processed and scrutinized by government agencies before publication.

Answer: D — A is false but R is true — Both statements are false. Primary data accuracy depends on survey design quality, not on being primary. Secondary data can be highly accurate, and the reason provided does not validate the assertion—processing doesn't necessarily reduce accuracy. The relationship between accuracy and data type is more nuanced than stated.

Q2. Assertion (A):

A questionnaire should move from general to specific questions.

Reason (R):

This sequence ensures the respondent feels comfortable and is able to answer subsequent questions more clearly.

Answer: A — Both A and R are true, and R is the correct explanation of A — Both the assertion and reason are true, and the reason correctly explains why this principle is important. Starting with general questions builds rapport and context, making respondents comfortable for progressively specific questions that may require detailed information.

Q3. Assertion (A):

The use of 'Any other' option in multiple-choice questions is unnecessary because all possible responses can be anticipated.

Reason (R):

All respondents can be forced to select from a fixed set of predetermined options without limitation.

Answer: D — A is false but R is true — Both are false. 'Any other' is necessary because researchers cannot anticipate all possible responses. Option B from the chapter explicitly states it allows respondents to provide unanticipated responses. Forcing respondents into predetermined categories without flexibility reduces data quality.

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Case-Based Questions (CBQ) for Collection of Data

Case Study:

The National Sample Survey Office (NSSO) of India conducted a survey to estimate average household expenditure across urban and rural areas. Instead of surveying all 300 million Indian households (census), they selected 50,000 representative households randomly. The enumerators used a standardized questionnaire asking about food, healthcare, education, and transportation expenses. The questionnaire used closed-ended questions with predetermined expense ranges (e.g., 'How much do you spend on food per month?' with options: Rs 2,000–5,000, Rs 5,000–10,000, etc.). After collection, the data were processed, tabulated, and published in an official government report titled 'Household Consumption in India.'

Q1. Is the data collected by NSSO enumerators directly from the 50,000 households classified as primary or secondary data?

  • A. Secondary data, because it was processed and published in a government report
  • B. Primary data, because it was collected directly for the first time through the survey ✓
  • C. Both primary and secondary, depending on who uses the data
  • D. Census data, because it covers a large population sample

Answer: B

Q2. If a researcher in 2025 uses the NSSO's published household expenditure report for analyzing consumption patterns, what type of data does this researcher use?

  • A. Primary data, because the researcher conducts a new analysis
  • B. Census data from 50,000 households
  • C. Secondary data, because it was previously collected and processed by NSSO ✓
  • D. Qualitative data unsuitable for statistical analysis

Answer: C

Q3. The NSSO used a questionnaire with predetermined expense ranges instead of asking open-ended questions like 'How much do you spend on food?' Why is this approach more efficient?

  • A. Open-ended questions are never appropriate in economic surveys
  • B. Closed-ended questions with categories are easier to code, tabulate, and compare across households, enabling faster data analysis ✓
  • C. Predetermined ranges prevent respondents from giving honest answers
  • D. Closed-ended questions eliminate the need for trained enumerators

Answer: B

Q4. Why did NSSO select 50,000 households instead of surveying all 300 million households in India?

  • A. To ensure that only wealthy households are included in the analysis
  • B. To avoid collecting primary data on household expenditure
  • C. Because census surveys are prohibited in India
  • D. To save time and cost while obtaining representative data through sampling ✓

Answer: D

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