Introduction and Sources of Data

Introduction

In the previous chapter, you learned about the role of statistics in economics. This chapter focuses on the sources of data and the methods of data collection. The primary purpose of collecting data is to gather evidence that helps in reaching a sound and clear solution to a problem.

Economic statements are often backed by data. For instance, a statement like, "The output of food grains rose to 272 million tonnes in 2016-17 from 108 million tonnes in 1970-71," shows that the production of food grains varies over time. These changing values are called variables, and each individual value is an observation. The values of these variables are the 'data' from which we can obtain information. 'Data' is a tool that helps in understanding problems by providing information.

What are the Sources of Data?

Statistical data can be obtained from two main sources: primary and secondary.

1. Primary Data

Primary data is data collected for the first time by the researcher or investigator by conducting an enquiry. It is based on first-hand information.

Example: If you want to study the popularity of a film star among school students, you would have to ask questions directly to a large number of students. The information you gather is primary data because you collected it yourself for your specific purpose.

2. Secondary Data

Secondary data is data that has already been collected and processed by some other agency. The researcher obtains this data from existing sources.

Example: If you use the report published by another researcher on the popularity of the film star for your own study, that data becomes secondary data for you.

Secondary data can be obtained from published sources like government reports, documents, newspapers, books, or websites. Using secondary data saves time and cost. Data that is primary for the original collector becomes secondary for all subsequent users.

How Do We Collect Data? The Survey Method and Instruments

The Survey Method

A survey is a method of gathering information from individuals. It is a common technique used by manufacturers to decide on a product, political parties to choose a candidate, or researchers to collect data. The purpose of a survey is to describe certain characteristics of a population, such as price, quality, usefulness, or popularity.

Preparation of the Instrument: Questionnaire/Interview Schedule

The most common instrument used in surveys is the questionnaire or interview schedule. This instrument is a list of questions designed to collect the desired data. When preparing a questionnaire, several key points should be kept in mind to ensure its effectiveness.

Principles of a Good Questionnaire

• Keep it Short: The questionnaire should not be too long. The number of questions should be as minimum as possible to avoid respondent fatigue.
• Use Simple Language: It should be easy to understand and should avoid ambiguous or difficult words.
• Logical Order of Questions: Questions should be arranged in a logical order, moving from general to specific, to make the respondent feel comfortable.
• Be Precise and Clear: Questions should be direct and unambiguous.
  • Poor Question: Do you spend a lot of money on books in a month?
  • Good Question: How much do you spend on books in a month? (with options like 'Less than ₹200', '₹200-₹300', etc.)
• Avoid Double Negatives: Questions starting with "Wouldn't you" or "Don't you" should be avoided as they can lead to biased responses.
• Avoid Leading Questions: A question should not give a clue about how the respondent should answer.
  • Poor Question: How do you like the flavour of this high-quality tea?
  • Good Question: How do you like the flavour of this tea?
• Do Not Indicate Alternatives: Questions should not restrict the respondent's choices unnecessarily.
  • Poor Question: Would you like to do a job after college or be a housewife?
  • Good Question: What would you like to do after college?

Types of Questions

A questionnaire may consist of two main types of questions:

1. Closed-ended (or Structured) Questions: These questions provide a set of options from which the respondent can choose.
   • Two-way question: Offers only two possible answers, typically 'Yes' or 'No'.
   • Multiple Choice Question (MCQ): Offers more than two options. Closed-ended questions are easy to score and analyze but can be difficult to design and may restrict the respondent's true feelings.
2. Open-ended (or Unstructured) Questions: These questions allow for more individualized and descriptive responses. They are difficult to interpret and score due to the wide variation in answers but can provide rich, qualitative insights.
   • Example: What is your view about globalisation?

Modes of Data Collection

There are three basic ways of collecting data through a survey: Personal Interviews, Mailing (questionnaire) Surveys, and Telephone Interviews. Each mode has its own advantages and disadvantages.

Pilot Survey

Once a questionnaire is ready, it is advisable to conduct a Pilot Survey, which is a try-out with a small group. The purpose of a pilot survey is:

• To get a preliminary idea about the survey.
• To pre-test the questionnaire to identify shortcomings, such as confusing questions or unclear instructions.
• To assess the suitability of questions and the performance of enumerators.
• To estimate the cost and time involved in the actual survey.

Census and Sample Surveys

Census or Complete Enumeration

A survey that includes every element of the population is known as a Census or the Method of Complete Enumeration. In a census, information is obtained from every single household or unit under study.

The Census of India is a prime example. It is carried out every ten years and involves a house-to-house enquiry covering all households in the country. It collects demographic data on birth and death rates, literacy, employment, life expectancy, and population size and composition. The last Census of India was held in 2011.

Population and Sample

In statistics, a Population or Universe refers to the totality of items or individuals under study. It is the group to which the results of the study are intended to apply.

When a survey of the entire population is not feasible (due to high cost, time constraints, etc.), a researcher selects a Sample. A sample is a group or section of the population from which information is obtained. A good sample, known as a representative sample, is smaller than the population but is capable of providing reasonably accurate information about it at a much lower cost and in a shorter time.

Most surveys are sample surveys because they are more practical and efficient.

Sampling Methods

There are two main types of sampling: random and non-random.

1. Random Sampling

In random sampling, individual units from the population are selected at random, giving every individual an equal chance of being selected. This ensures that the sample is representative of the population and free from investigator bias. The lottery method is a simple form of random sampling.

2. Non-Random Sampling

In non-random sampling, all units of the population do not have an equal chance of being selected. The selection is based on the convenience, judgment, purpose, or quota set by the investigator. This method is prone to bias as the investigator's personal judgment plays an important role.

Sampling and Non-Sampling Errors, and Important Data Sources

Sampling and Non-Sampling Errors

Errors in statistical studies can be broadly categorized into two types.

1. Sampling Errors

A sampling error refers to the difference between a sample estimate and the corresponding population parameter (the true value). It arises because a sample is only a part of the population and may not perfectly reflect its characteristics.

Sampling Error = Actual Value of Population Parameter – Sample Estimate

The magnitude of the sampling error can be reduced by taking a larger sample.

2. Non-Sampling Errors

Non-sampling errors are more serious than sampling errors because they cannot be minimized by simply taking a larger sample. Even a census can contain non-sampling errors. Some types of non-sampling errors are:

• Sampling Bias: This occurs when the sampling plan is designed in such a way that some members of the target population cannot be included in the sample.
• Non-Response Errors: This happens when an interviewer is unable to contact a person in the sample, or the person refuses to respond. This can make the sample unrepresentative.
• Errors in Data Acquisition: This type of error arises from recording incorrect responses. It can be due to carelessness, differences in measurement tools, or intentional misreporting by respondents or enumerators.

Important Agencies for Data Collection in India

Several agencies at the national and state level collect, process, and tabulate statistical data, which serve as important sources of secondary data.

1. Census of India

Conducted every ten years since 1881 by the Registrar General of India, the Census of India provides the most complete and continuous demographic record of the population. It collects information on size, density, sex ratio, literacy, migration, and other socio-economic aspects.

2. National Sample Survey (NSS)

The NSS (now part of the National Statistical Office, NSO) was established by the Government of India to conduct nationwide surveys on various socio-economic issues. The NSS conducts continuous surveys in successive "rounds".

It provides periodic estimates on literacy, school enrolment, employment, unemployment, manufacturing and service sector enterprises, morbidity, maternity, child care, and consumer expenditure. These data are released through reports and the quarterly journal Sarvekshana and are crucial for planning purposes.

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Multiple Choice Questions

Short Answer Type Questions

Long Answer Type Questions