Test: Collection of Data - SSC CGL MCQ

The primary difference between primary data and secondary data lies in their originality and source. Primary data is original data collected firsthand by the researcher for a specific research purpose, ensuring uniqueness and relevance to the study. On the other hand, secondary data already exists and can be readily accessed, often from sources like books, journals, or databases. Primary data tends to be more costly and time-consuming to collect compared to secondary data, which is generally more affordable and quicker to obtain.

Indirect oral investigation offers a crucial advantage in its ability to provide wide coverage and reduce the risk of investigator bias. By collecting data from knowledgeable individuals or experts, this method can gather information from a broad area while minimizing personal biases that could influence the results. Although there may be some drawbacks such as potential accuracy issues due to second-hand information, the reduced risk of bias makes this method valuable for obtaining diverse perspectives.

Utilizing local correspondents or individuals for data collection offers an economical approach compared to extensive fieldwork. This method allows for wide coverage across large geographical areas while ensuring ongoing data collection. Despite the advantages, data may lose some originality as it is second-hand, and there might be variations in quality and collection methods due to personal biases of the correspondents involved.

The enumerator's method is deemed appropriate when a detailed and skilled investigation is required, necessitating the investigator to be well-versed in local language and cultural norms. This approach is vital for ensuring the accuracy and depth of data collected, especially in scenarios where comprehensive insights are crucial for the research or study at hand.

Random sampling aims to minimize selection bias and ensure representativeness by giving every member of the population an equal chance of being selected. This method helps in creating a sample that is reflective of the entire population, reducing the risk of bias in the study.

Systematic sampling involves selecting every nth item from a list of the population, starting from a random point. This method ensures a systematic and structured approach to sampling, reducing the potential for bias in the selection process.

Duplicate data poses a substantial challenge in data collection processes. In modern businesses, managing various data sources can lead to significant duplication and overlap, impacting data quality and user experience. Duplicate data, such as repeated contact details, can create confusion and hinder data analysis efforts. Resolving and preventing duplicate data is crucial for maintaining accurate and reliable databases.

Determining what data to collect is crucial as it aligns the data collection process with the specific goals or objectives of data usage. By identifying the subjects to be covered, data sources, and required information, organizations can ensure that the collected data serves its intended purpose effectively. This strategic approach helps in generating meaningful insights and making informed decisions based on the collected data.

Direct personal investigation, while offering advantages such as detailed information and high reliability, is limited in its coverage capabilities. This method can be challenging to apply to large or dispersed populations, making it less suitable for studies that require data from a wide geographic area or diverse group of individuals. Despite its strengths in ensuring accurate and detailed data, the coverage limitation of direct personal investigation highlights the importance of considering alternative methods for comprehensive data collection.

The sample method involves collecting data from a subset (sample) of the population and drawing conclusions about the entire population based on this sample. This approach is economical, time-saving, and allows for large-scale investigations. However, it does require careful consideration in selecting a representative sample to ensure the validity and reliability of the conclusions drawn.