Statistics for Economics for Class 11 Chapter 2 Collection of Data
1. Primary Data – Primary data is the data that is collected for the first time through personal experiences or evidence, particularly for research. It is also described as raw data or first-hand information. The mode of assembling the information is costly, as the analysis is done by an agency or an external organisation and needs human resources and investment. The investigator supervises and controls the data collection process directly.
The data is mostly collected through observations, physical testing, mailed questionnaires, surveys, personal interviews, telephonic interviews, case studies, focus groups, etc.
2. Secondary Data – Secondary data is second-hand data that is already collected and recorded by some researchers for their purpose, and not for the current research problem. It is accessible in the form of data collected from different sources such as government publications, censuses, internal records of the organisation, books, journal articles, websites, reports, etc.
This method of gathering data is affordable, readily available, and saves cost and time. However, the one disadvantage is that the information assembled is for some other purpose and may not meet the present research purpose or may not be accurate.
3. Questionnaire – A questionnaire is an exploration instrument consisting of a progression of inquiries to accumulate data from respondents. They can be considered as a sort of composing a written interview or meeting. They can be completed up close and personal, by phone, post, or computer.
Questionnaires supply a fast, productive, and cheapest method of acquiring a lot of data from an enormous example of individuals.
A questionnaire test alludes to a factual or a statistical technique for inspecting where a typical survey is given to a huge piece of the populace to take their perspective and idea on the connected issues.
4. Enumerator – An enumerator is a person who gathers all the information and data required for a specific statistical investigation, either primarily or secondarily. An enumerator is a trained person to do the necessary fieldwork. Basically, an enumerator is a person who collects the required statistical data and information from the respondents and processes it further for statistical analysis.
5. Open-ended Questions – An open-ended question without a right or wrong answer is a sort of overview question that doesn’t limit respondents to already given answer choices. An open-ended question that could go either way requires the respondent to completely communicate for themselves as they give replies to questions.
Questions that could go either way have a wide concentration and permit respondents to respond broadly. They likewise give one better knowledge of the considerations, assumptions, and encounters of the respondent since they can openly put themselves out there.
6. Close-ended Questions – A close-ended question is a kind of question in a survey that limits respondents to a proper arrangement of foreordained reactions. As such, it requires the respondent to pick a response from the restricted response choices recorded in the inquiry or question; the respondent can’t give replies outside these choices.
Close-ended questions are regularly utilised in quantitative examination or research to accumulate factual data from respondents. Additionally, the quantity of answer choices in close-finished questions isn’t fixed; there can be at least 2 recorded choices or options relying upon the goal and objective of the survey.
7. Personal Interviews – Personal interview is the data collection method suitable for open-ended questions and to avoid ambiguities. Owing to direct personal contact, there is a lesser chance of occurrence of ambiguities. In this method, a person known as an interviewer is required to ask questions face to face to the other person. The personal interview can be structured or unstructured, direct investigation, focused conversation, etc.
8. Mailing Survey – Under this method, questionnaires are mailed to the informants. A letter is attached with the questionnaire giving the purpose of the inquiry. It is also assured that the information will be kept private. The informants note the answers to the questions and return the completed questionnaire to the investigator.
9. Telephone Interview – In this method, an interviewer obtains information by contacting people on the telephone to ask questions or views verbally.
10. Pilot Survey – Pilot surveys help in assessing the suitability of questions, clarity of instructions, the performance of enumerators, and the cost and time involved in the actual survey. It has no effect on the response rate of the actual survey. A pilot survey is a sample survey that is done to know whether a questionnaire is understandable to the public or not.
11. Census – A census method is the statistical list process where all population members are analysed. The population relates to the set of all observations under concern. For instance, if one wants to carry out a study to find out students’ feedback about the amenities of a school, then all the students of the school would form a component of the ‘Population’ for one’s study.
12. Population or Universe – In statistics as well as in quantitative methodology, the set of data is collected and selected from a statistical population with the help of some defined procedures. There are two different types of data sets, namely population and sample.
It includes all the elements from the data set and measurable characteristics of the population, such as mean and standard deviation are known as a parameter. For example, All people living in India indicate the population of India.
13. Sample – A sample includes one or more observations that are drawn from the population, and the measurable characteristic of a sample is a statistic. Sampling is the process of selecting the sample from the population. For example, some people living in India are the sample of the population.
14. Random Sampling – Random sampling is a method of choosing a sample of observations from a population to make assumptions about the population. It is also called probability sampling. The primary feature of probability sampling is that the choice of observations must occur in a ‘Random’ way such that they do not differ significantly from observations that are not sampled. One can assume here that statistical experiments contain data that is gathered through random sampling.
15. Non-random Sampling – Non-random sampling or non-probability sampling is a sampling technique where the sample selected will be based on factors such as convenience, judgement, and experience of the researcher and not on probability. The non-probability sampling method is a technique in which the researcher selects the sample based on subjective judgement rather than random selection. In this method, not all the members of the population have a chance to participate in the study.
16. Sampling Errors – Sampling error is defined as the amount of inaccuracy in estimating some value, which occurs due to considering a small section of the population, called the sample, instead of the whole population. It is also called an error. Sample surveys take into account the study of a tiny segment of a population, so there is always a particular amount of inaccuracy in the information obtained. This inaccuracy can be defined as error variance or sampling error.
In other words, sampling error refers to the difference between the sample estimate and the actual value of a population characteristic. This type of error occurs when one makes an observation from the sample taken from the population. It is possible to reduce the magnitude of sampling error by taking a larger sample.
17. Non-sampling Errors – The errors that are related to the collection of data are termed non-sampling errors. If the field of investigation or the population size is large, then the possibility of non-sampling errors also increases. So, non-sampling errors can be maximised by taking large samples.
In other words, non-sampling errors are more serious than sampling errors because a sampling error can be minimised by taking a larger sample, but it is difficult to minimise non-sampling error, even by taking a large sample. Even a census can contain non-sampling errors. These include errors in data acquisition, non-response errors, and sampling bias.
18. Sampling Bias – Bias is a statistical term that means a systematic deviation from the actual value. It is a sampling procedure that may show some serious problems for the researcher as a mere increase cannot reduce the sample size. Bias is the difference between the expected value and the real value of the parameter.
19. NSSO – The National Sample Survey Office (NSSO), formerly called the National Sample Survey Organisation, was the largest organisation in India to conduct periodic socio-economic surveys. The NSSO was set up in 1950 to conduct large-scale sample surveys throughout India. The employees of the NSSO are from the Indian Statistical Service and the Subordinate Statistical Service (appointed through the Staff Selection Commission).
We hope that the offered Statistics for Economics Index Terms for Class 11 with respect to Chapter 2: Collection of Data will help you. In case of any queries with respect to CBSE Class 11 Statistics for Economics, Collection of Data Index Terms, drop a remark underneath, and we will hit you up at the most punctual.
Related Links:
- Class 11 Statistics for Economics Terms – Chapter 1: Introduction
- Class 11 Statistics for Economics Terms – Chapter 3: Organisation of Data
- Class 11 Statistics for Economics Terms – Chapter 4: Presentation of Data
- Class 11 Statistics for Economics Terms – Chapter 5: Measures of Central Tendency
- Class 11 Statistics for Economics Terms – Chapter 7: Correlation
- Class 11 Statistics for Economics Terms – Chapter 8: Index Numbers
- Class 11 Statistics for Economics Terms – Chapter 9: Use of Statistical Tools
