Economic analysis begins with data. To study prices, incomes, employment, output or consumption, the economist needs reliable numerical facts. Data are collected from various sources using different methods, and the quality of any statistical conclusion depends on the quality of the data on which it is based. This chapter explains what data are, how they can be collected, what instruments and methods are used, and the important concepts of census, sampling and the sources of error in data collection.
Statistical data may be classified in two broad ways. First, on the basis of their source, data may be primary or secondary. Second, on the basis of how they are gathered, data may be collected through a census or through a sample. The choice of the appropriate source and method depends on the nature of the problem, the time available, the cost involved and the degree of accuracy required. Poorly designed data collection produces misleading results, however sophisticated the later analysis may be.
In this chapter we study the meaning and types of data, the instruments used in collecting primary data, the choice between census and sample, the essentials of a good questionnaire, the concepts of sample, pilot survey, sampling error and non-sampling error, and finally the sources of secondary data in India such as government publications, journals, and international agencies.
2. Sources of Data: Primary and Secondary
Primary data are those that are collected afresh and for the first time by the investigator directly from the field, for a specific purpose. For example, when the NSSO interviews households to know their monthly consumption expenditure, the responses obtained are primary data. Primary data are original in character and are more reliable, but their collection is expensive, time-consuming and requires trained investigators.
Secondary data are those that have already been collected and processed by some other agency for its own purpose, and are used by a new investigator for a different purpose. Government publications, reports of the Reserve Bank of India, census reports, journals and international sources like the World Bank are typical sources of secondary data. Secondary data are cheaper and quicker to obtain, but they may not be fully relevant or reliable for the investigator's specific purpose, and the investigator cannot control their quality.
The choice between primary and secondary data depends on the nature and objective of the enquiry, the availability of time and money, and the accuracy required. An investigator should prefer primary data when the required information is not available from existing sources or is outdated, and secondary data when reliable and relevant published data already exist.
3. Methods of Collecting Primary Data
Primary data can be collected by several methods. The important methods are:
Direct personal investigation: The investigator personally visits the field and collects information from the respondents. It is suitable for intensive studies of small areas but is expensive and time-consuming.
Indirect oral investigation: Information is collected from third parties who are in contact with the persons from whom data are needed. It is used when the respondents themselves are unwilling to give information, for example in cases of illegal activities.
Information from correspondents: Local correspondents or agents are appointed to collect and send information from their areas. This method is useful for covering large areas at low cost, but the information may lack accuracy and uniformity.
Questionnaire or mailed questionnaire method: A well-designed questionnaire is sent to respondents by post, who fill it and return it. It is cheap and covers a wide area, but suffers from a low and slow response rate.
Telephone and online surveys: Information is collected through telephone calls or online forms, which are fast and cheap but limited to people with access to these facilities.
4. The Questionnaire and Schedule
The questionnaire is the most common instrument for collecting primary data. It is a set of questions, printed or typed in a definite order, relating to the problem under enquiry. A schedule is a set of questions printed on a sheet that is filled in by the investigator who personally interviews the respondents, whereas a questionnaire is generally filled by the respondents themselves.
The essentials of a good questionnaire are:
The number of questions should be as small as possible and exactly relevant to the enquiry.
Questions should be simple, clear and easy to understand.
Questions should be arranged in a logical order, from simple to complex.
Questions should not be vague, ambiguous or leading.
Questions should not be personal, embarrassing or offensive.
A pilot survey should be conducted before the final questionnaire is administered to test its working.
The questionnaire must be accompanied by clear instructions and should mention the purpose of the enquiry.
Pilot survey: Before the actual survey, a small-scale trial of the questionnaire is carried out on a few respondents to detect defects such as ambiguous questions, missing alternatives or excessive length. This trial run is called a pilot survey.
5. Census and Sample: Concepts and Choice
Census (complete enumeration): In a census, every unit of the universe or population is enumerated. The universe is the entire set of items from which data are collected; for example, all households of India. A census gives complete and accurate information about the whole population, as in the case of the decennial Census of India. But a census is expensive, time-consuming and requires a large trained staff, and in many cases it is impossible to reach every unit.
Sample survey: In a sample survey, only a part of the universe - called the sample - is studied, and the results are generalised to the whole population. Sampling saves time, money and effort, gives quicker results, and can achieve greater accuracy because a small number of units can be studied more intensively. In India, the NSSO uses sample surveys to estimate consumption expenditure, employment and poverty.
The choice between census and sample depends on:
The nature and size of the universe - a large universe usually favours sampling.
The degree of accuracy required - when complete enumeration is essential, census is preferred.
The time and money available.
The nature of the enquiry - destructive testing (e.g. checking the life of electric bulbs) always requires sampling.
The availability of trained staff.
6. Methods of Sampling
The important methods of sampling are:
Random or probability sampling: In this method every unit of the universe has an equal chance of being selected, and units are chosen by the principle of chance without any bias. The lottery method and the use of random number tables are the common techniques. Random sampling may be simple random sampling, systematic random sampling, stratified random sampling or cluster sampling.
Simple random sampling: Units are selected directly from the whole universe, each having an equal chance of selection.
Systematic sampling: After selecting the first unit at random, subsequent units are selected at a fixed interval, e.g. every 10th unit.
Stratified sampling: The universe is first divided into homogeneous strata or groups, and then a random sample is drawn from each stratum in proportion to its size. This improves the representativeness of the sample.
Non-random sampling: In this method units are selected not by chance but by the judgement of the investigator or by convenience. Judgement sampling, convenience sampling and quota sampling are non-random methods. Such samples are easy to draw but may be biased.
7. Errors in Data Collection
Errors in statistical data may arise at any stage of the enquiry. They are broadly of two types:
Sampling errors: These are the differences between the value of a parameter of the universe and the value obtained from the sample, arising purely because a part, and not the whole, of the universe is studied. Sampling errors arise only in sample surveys, are not due to carelessness, and can be reduced by increasing the size of the sample, or by adopting a more refined method of sampling, such as stratified sampling. A larger and more representative sample gives results closer to the universe values.
Non-sampling errors: These are errors that arise during collection, recording, tabulation and analysis of data, and they occur both in census and sample surveys. They may be due to the faulty selection of the sample, the carelessness of the investigator, incorrect answers by respondents, non-response, measurement errors or mistakes in tabulation and processing. Non-sampling errors cannot be eliminated by increasing the sample size; they can be reduced only by careful planning and execution of the survey.
8. Sources of Secondary Data
The important sources of secondary data in India are:
Government publications: Publications of the Central Statistical Office, National Sample Survey Organisation, Ministry of Finance (Economic Survey), Registrar General and Census Commissioner, and various ministries and state governments.
Reserve Bank of India: Reports on Currency and Finance, the Handbook of Statistics on Indian Economy and other bulletins.
Semi-government publications: Publications of institutions like the State Financial Corporations, the Food Corporation of India and various boards and corporations.
Publications of international organisations: Reports of the World Bank, International Monetary Fund, World Trade Organisation, United Nations and its agencies.
Trade and professional associations: Publications of the Federation of Indian Chambers of Commerce and Industry (FICCI), the Associated Chambers of Commerce (ASSOCHAM) and similar bodies.
Journals, newspapers and research reports: Economic and Political Weekly, Indian Journal of Economics, newspapers and reports of research institutions.
Quick Revision Tables
Basis
Primary Data
Secondary Data
Meaning
Collected afresh by the investigator
Already collected by someone else
Originality
Original in character
Not original
Cost
Expensive
Cheap
Time
Time-consuming
Quick
Reliability
More reliable
Less reliable
Control over quality
Investigator controls
Investigator cannot control
Method of Sampling
Meaning
Remark
Simple random
Every unit has equal chance
Unbiased
Systematic
First unit random, then fixed interval
Every kth unit
Stratified
Universe divided into strata, random from each
Most representative
Cluster
Whole groups chosen at random
Useful for large areas
Judgement
Selected by investigator's judgement
Non-random, biased
Mind Map
graph TD
A["COLLECTION OF DATA"] --> B["Sources"]
A --> C["Methods of collection"]
A --> D["Census vs Sample"]
A --> E["Errors"]
B --> B1["Primary data"]
B --> B2["Secondary data"]
C --> C1["Direct personal investigation"]
C --> C2["Indirect oral investigation"]
C --> C3["Correspondents"]
C --> C4["Questionnaire / schedule"]
D --> D1["Census - complete enumeration"]
D --> D2["Sample - part of universe"]
D --> D3["Random sampling - equal chance"]
D --> D4["Non-random sampling - judgement"]
E --> E1["Sampling error - only in sample, reduced by larger sample"]
E --> E2["Non-sampling error - in both, reduced by care"]
Important Diagrams (SVG)
Diagram 1: Classification of Data and Methods of Collection
Diagram 2: Census vs Sample and Types of Errors
Common Mistakes
Confusing primary and secondary data; primary data are collected afresh for the first time, while secondary data are already collected by others.
Mixing up a questionnaire with a schedule; a questionnaire is filled by the respondent, while a schedule is filled by the investigator in an interview.
Thinking that a census is always better; a census is accurate but costly, slow and impractical for large universes and destructive testing.
Believing that sampling errors arise in census surveys; sampling errors arise only in sample surveys.
Assuming that increasing the sample size removes all errors; it reduces sampling error but cannot remove non-sampling errors.
Overlooking the pilot survey; a questionnaire must be pre-tested on a few respondents before being used on a large scale.
Using secondary data without verifying their relevance and reliability for the specific enquiry.
Exam Tips
Define primary and secondary data and give at least one example of each.
List the methods of collecting primary data with a one-line description of each.
Distinguish between questionnaire and schedule, and state the essentials of a good questionnaire.
Differentiate census from sample with examples, and state when each is preferred.
Explain random, systematic and stratified sampling and why stratified sampling is more representative.
Define sampling error and non-sampling error, and state how each can be reduced.
Mention at least four sources of secondary data in India, including the CSO, NSSO, RBI and Economic Survey.
Conclusion
This chapter explained the entire process of data collection, which is the foundation of all statistical work. We distinguished primary data, collected afresh for a specific purpose, from secondary data, which are already available from existing sources. We examined the methods of collecting primary data - direct and indirect investigation, correspondents, and questionnaires - and the essentials of a good questionnaire, including the role of the pilot survey. We compared census with sample surveys and saw when each is appropriate, studied the principal sampling methods, and distinguished sampling errors, which arise only in samples, from non-sampling errors, which can occur in any survey. Finally, we listed the important sources of secondary data in India. Together with the next chapter, where we learn to organise the collected data into frequency distributions, this chapter gives the student a complete picture of how reliable economic information is produced.