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Chapter 2 Collection Of Data
In the previous chapter, we discussed the nature of economics and the importance of statistics in economic studies. This chapter focuses on a fundamental step in using statistics: the collection of data. Data is essential for providing evidence to understand and solve problems, particularly in economics, where it helps analyze variations in variables like production figures. For example, observing fluctuations in food grain production over different years demonstrates the need for data to understand these changes. Data is a tool that provides information, enabling us to grasp complex issues.
Introduction
This chapter builds upon the previous one by focusing on data collection. The primary purpose of collecting data is to gather evidence that can lead to a clear understanding and solution to a problem. In economics, data helps analyze variables and understand fluctuations, such as changes in food grain production over time (Table 2.1).
Production of Food Grain in India (Million Tonnes):
| X (Year) | Y (Production in Million Tonnes) |
|---|---|
| 1970–71 | 108 |
| 1978–79 | 132 |
| 1990–91 | 176 |
| 1997–98 | 194 |
| 2001–02 | 212 |
| 2015-16 | 252 |
| 2016-17 | 272 |
Data represents values of variables over different observations, providing information about economic phenomena.
What Are The Sources Of Data?
Statistical data can be obtained from two primary sources: primary data and secondary data.
Primary Data: This data is collected firsthand by the researcher through their own enquiry. For example, conducting a survey by asking questions directly to individuals to gather specific information, like the popularity of a film star among students, yields primary data. Primary data is original to the source that collects and processes it.
Secondary Data: This data has been collected and processed by another agency. It can be obtained from published sources such as government reports, documents, newspapers, books, or websites. Using secondary data saves time and cost compared to collecting primary data. Data becomes secondary for any source that uses it after it has been initially collected and processed by someone else.
How Do We Collect The Data?
Surveys are a common method for collecting data by gathering information from individuals. Surveys often utilize questionnaires or interview schedules as instruments.
Preparation Of Instrument
Designing an effective questionnaire or interview schedule requires careful consideration. Key points include:
- Keeping the questionnaire concise with minimal questions.
- Using easy-to-understand, unambiguous, and clear language.
- Arranging questions logically from general to specific.
- Avoiding biased questions, leading questions, or questions indicating alternatives to the answer.
Questions can be closed-ended (structured), offering predefined answer options (two-way or multiple choice), or open-ended (unstructured), allowing for more individualized responses. Closed-ended questions are easier to analyze but may limit responses, while open-ended questions provide richer detail but are harder to score.
Mode Of Data Collection
There are three basic ways to collect data through surveys:
Personal Interviews
Face-to-face interviews conducted by a researcher or investigator. Advantages include high response rate, ability to use all question types, clarification of ambiguities, observing reactions, and supplementary information. Disadvantages include high cost, time consumption, and potential for researcher bias or inhibiting responses.
Mailing Questionnaire
Sending questionnaires by mail (or online/SMS) for respondents to complete and return. Advantages include being less expensive, reaching remote areas, avoiding interviewer bias, and allowing thoughtful answers. Disadvantages include low response rates, difficulty clarifying instructions, and potential loss of questionnaires.
h3 class="yellowheading">Telephone Interviews
Asking questions over the phone. Advantages include being cheaper and quicker than personal interviews, allowing clarification, and being suitable for sensitive questions. Disadvantages include limited access for those without phones and inability to observe reactions.
Pilot Survey
Before the main survey, a try-out with a small group (**Pilot Survey** or **Pre-testing**) is advisable. This helps identify shortcomings in the questionnaire, assess question suitability and clarity, evaluate enumerator performance, and estimate the cost and time of the actual survey.
Census And Sample Surveys
When conducting a survey, a key decision is whether to collect data from the entire population or a representative sample.
Census Or Complete Enumeration
A **Census** (or the Method of Complete Enumeration) involves collecting information from every element or unit of the population. India conducts a Census every ten years, collecting comprehensive demographic data from all households.
Population And Sample
In statistics, the **Population** (or Universe) refers to the totality of items or individuals under study, possessing certain characteristics. A **Sample** is a smaller, representative group selected from the population. A good sample is smaller than the population but can provide reasonably accurate information at a lower cost and in less time. Most surveys are sample surveys due to these advantages, allowing for more intensive inquiries and effective training/supervision of enumerators.
Random Sampling
In **Random Sampling**, individual units are selected from the population by chance, giving every individual an equal probability of being chosen. Examples include the lottery method or using computer programs for random selection. Exit polls during elections are an example of sample surveys (usually random) to predict results, though they are not always accurate.
Non-Random Sampling
In **Non-Random Sampling**, the selection of units is not based on chance. The investigator's judgment, convenience, purpose, or quota influences the selection process. Not all units in the population have an equal chance of being selected, introducing potential bias.
Sampling And Non-Sampling Errors
Errors can occur in data collection, categorized as sampling errors and non-sampling errors.
Sampling Errors
**Sampling error** is the difference between an estimate obtained from a sample and the actual value of the corresponding characteristic in the population (population parameter). It arises because a sample is only a part of the population. The magnitude of sampling error can be reduced by increasing the sample size. Example: Calculating the average income of farmers in a region based on a sample will likely differ from the true average income of all farmers in the region.
Non-Sampling Errors
**Non-sampling errors** are more serious and difficult to minimize, even in a Census. These errors are not related to the sampling process itself. Some types of non-sampling errors include:
Sampling Bias
Occurs when the sample selection method systematically excludes certain members of the target population.
h3 class="yellowheading">Non-Response Errors
Arise when selected individuals cannot be contacted or refuse to participate in the survey, making the collected sample potentially unrepresentative.
h3 class="yellowheading">Errors In Data Acquisition
These errors occur during the process of collecting or recording data. They can be due to incorrect responses, mistakes in measurement instruments, carelessness in recording or transcribing data.
Census Of India And Nsso
In India, several national and state-level agencies are responsible for collecting, processing, and tabulating statistical data. Two prominent national agencies are the **Census of India** and the **National Sample Survey Office (NSO)**, previously known as NSS.
The Census of India provides detailed demographic records, conducted every ten years since 1881 (first post-Independence Census in 1951). It collects information on population size, density, sex ratio, literacy, migration, and urban/rural distribution, which is used to analyze various socio-economic issues.
The NSO conducts nationwide surveys on socio-economic issues through continuous rounds. Its reports and journal 'Sarvekshana' provide estimates on literacy, school enrollment, employment, unemployment, various enterprises, health indicators, and consumer expenditure. NSO data is vital for government planning purposes.
Conclusion
Economic facts, expressed numerically as data, are essential for understanding, explaining, and analyzing problems and their causes. Primary data is collected firsthand through surveys, which require careful planning and instrument design. Secondary data, already collected and processed by other agencies, can also be used, offering cost and time advantages. The choice of data source and collection method depends on the specific objectives of the study. Various agencies at national and state levels collect and disseminate statistical data.
Recap:
- Data provides a tool for understanding and solving problems.
- Primary data is collected directly from the source.
- Surveys can be conducted through personal interviews, mailing questionnaires, and telephone interviews.
- Census covers the entire population.
- A sample is a representative smaller group from the population.
- Random sampling gives every unit an equal chance of selection.
- Sampling error is the difference between sample estimate and population value.
- Non-sampling errors include bias, non-response, and data acquisition errors, and are harder to minimize.
- Census of India and National Sample Survey Office (NSO) are major data collection agencies in India.