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*STATISTICAL UNIT:* Statistical unit may be defined as the unit in terms of which the investigator measures the variable selected for enumeration, analysis and interpretation. S…
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*STATISTICAL UNIT:* Statistical unit may be defined as the unit in terms of which the investigator measures the variable selected for enumeration, analysis and interpretation. Statistical units can be classified into two, namely, Units of Enumeration and Units of Analysis and Interpretation. *OBJECTIVES:* Units of Enumeration Units of Enumeration are those units in terms of which the data are collected. Eg: A metre, a tonne, an hour, a worker, Kilowatt hour, etc. Units of Enumeration are divided in to simple units, composite units and Hypothetical units. (i) Simple Units A simple unit is one which represents a single condition without any qualification. Eg: Production of rice in quintals, Height of a person in centimeter, weight in kilogram, marks obtained in a test, a worker, a house, etc. (ii) Composite Units A composite unit is one which is formed by adding a qualifying word to a simple unit. Eg: Skilled worker, Kilowatt hour, Tonne Kilometre, etc. (iii) Hypothetical Units A Hypothetical unit is one which is used for comparison. Eg: Horse power, Multi- specialty hospital, Multi-plus cinema theatre, etc. 2. Units of Analysis and Interpretation Units of analysis and interpretation are those units which are used for comparison and interpretation of statistical data. These units include ratios, percentages, coefficient, etc. *Methods of Collecting Primary Data:* The following are the important methods of collecting primary data: 1. Observation Method It is a method under which data is collected with the help of observation by the observer or by personally going to the field. The basis of observation is curiosity and there is no person in the society who is not curious. Therefore, in observation the basic thing is to curiously observe the object to be observed. If there is no such curious observation perhaps the purpose of observation will not be served. Features of Observation Following are the important features of observation: 1) It is an eye affair: The investigator should not depend on here say and should depend on his eyes. 2) Definite aim: Observation without aim is just useless. The aim of the observation should be clearly determined. 3) Planning: The investigator should go to the field for observation with proper planning. 4) Noting down of observation: As soon as the observation work is over things having importance should be recorded immediately. 5) Direct method of study: It is a direct method of study under which the investigator is personally required to go to the field and also personally observe the situation and object with his own eyes. 6) Collection of primary data: This method can be used only for collecting primary data. 2. Interview Method Interview is one of the important methods of collecting primary data. It may be defined as "a two-way systematic conversation between an investigator and an informant, initiated for obtaining information relevant to a specific study". It is a direct method of collecting data. Through this method the investigator can know the views and ideas of the respondents. *Essentials of a Good Questionnaire* The following are the essentials of a good questionnaire: Questionnaire used for data collection has two important parts: (a) A covering letter, and (b) The questionnaire. The covering letter is a formal request for getting co-operation and this will state the 1. Precautions for the covering letter i) It should be politely worded. ii) It should highlight the need and importance of the study. ill) It should emphasise upon the sincere co-operation of the respondents. iv) It should ensure that the information supplied by the informants will be kept strictly confidential. V) It should contain a promise that a copy of the result of the enquiry will be sent to them if they so desire. vi) It should be enclosed by a self-addressed and stamped envelope for the convenience of the respondents for sending back the questionnaire. 2. Precautions for drafting the questionnaires (Qualities of a & d questionnaire) 1. The language of the questions should be simple and clear. 2. It should be brief. 3. Technical words and jargons should be avoided. 4. Objective type questions or "Yes or No" questions should be framed. 5. Long questions should be avoided. 6. The principle of one issue, one question should be followed. 1. The questions should be so sequenced that the respondents are motivated and answer all questions. 8. The vocabulary employed in the questions should be appropriate to the back ground of the respondents. oi Non-sensitive and easily answerable questions should be kept in the beginning whereas difficult and sensitive questions should be kept at the end. 10. Questions should be so worded that ego of the respondents is not injured in any way. I1. All the questions should be analytical. 12. The amount of writing required on the questionnaire should be kept to the minimum. 13. It is always better to add a few questions to check the accuracy and consistency of the answers being given. 14. Questions which call for the responses towards socially accepted norms and values should be avoided. 15. Only such questions should be asked about which it is believed that the people have sufficient information *Secondary Data* Secondary data are those which are collected from published and unpublished sources. Such data have been already collected by some other persons for some other purpose. Considerable amounts of data are available in the published and unpublished documents, reports, manuscripts, letters and diaries and so on. The reason why secondary data are being increasingly used is that published statistics are now available from various agencies and there is no necessity of employing any of the methods of data collection that are used in primary data. *Sources of Secondary Data* The sources of secondary data are: 1. Official reports of the Government and Reserve Bank of India. 2. Reports and publications of Trade Associations and Chamber of commerce. 3. Technical Journals, Newspaper, Books, Periodicals and Annual Reports of Companies 4. Research abstracts 5. Year Book, Personal Documents, etc. *Characteristics of a good sample* A good sample is that which fulfils the objectives of statistical investigation. It should possess the following features: 1) The sample selected should truly represent the characteristics of the population. 2) The size of the sample should be adequate 3) There should be homogeneity in the nature of all the units selected for the sample. 4) The selection of one item as sample should not influence the selection of another item in any manner. 5) The sample selected should fill the needs of the subject matter under study. 6) Selection of sample should be based on past and practical experiences. *Sample Survey (Sampling)* Sampling is a process of selecting few units from the population, analysing the sample data and making conclusion about the population. Sampling or sample survey is a technique of inspecting or studying only a selected representative and adequate fraction of the population. Under this method some representative units are selected from the universe. The study of the sample reveals the characteristics of the universe. For example, if we want to know the average height of students of a particular college, it is sufficient if the required measurements are taken from a few students selected at random. The process of sampling involves three elements: a) Selecting the sample, b) Collecting the information from the sample, and c) Making inference about the population. *Advantages of Sampling* Following are the important advantages of sampling: 1) This method saves a lot of time. 2) It is cheaper than census method. 3) The size of the sample can be increased or decreased according to the size of the population. 4) It is a scientific method. 5) It ensures more administrative convenience. 6) The result of sample enquiry is dependable if it is conducted scientifically. 7) This method is indispensable if the population is infinite or destructive in nature. *Disadvantages* Some of the important disadvantages are: 1) It becomes expensive if the size of the sample is large. 2) This method is not suitable if information is needed from each and every unit of population. 3) If a sample enquiry is not carefully planned and executed, the conclusion may be inaccurate and misleading. 4) Sampling requires the services of experts. 5) It is very difficult to select perfectly representative samples. *Random Sampling (Probability Sampling)* This type of sampling is also known as probability sampling because such sampling design is based on probability for the selection of each item. Each item in the population has an equal chance of being selected or excluded. Random sampling may be classified as simple random sampling or complex random sampling. a) Simple Random Sampling: In this case samples are selected from the population in such a manner that every item of the population has an equal chance of being selected and the selection of any item does not influence the selection of any other. Simple random selection may be conducted either through lottery method or through Random Number method. b) Complex Random Sampling: Random sampling under restricted sampling technique may result in complex random sampling. Systematic sampling, stratified sampling, cluster sampling, multistage sampling, sequential sampling are examples of complex sampling. (i) Stratified Sampling It is a restrictive type of random sampling. This method is applied when the population is heterogeneous. The entire universe is divided into certain number of strata on the basis of certain criteria known as stratifying factors such as age, sex, income, education, geographical area etc. The next step is to decide the size of the sample required. After this, allocate the sample size to the various strata from which the data are to be collected. (i) Systematic Sampling It is a restrictive method of random sampling. Under this method the initial unit of the sample is selected at random and the others are selected on a predetermined basis of space interval. So, before starting with the sampling procedure it is necessary to arrange the population systematically on the basis of alphabetical or numerical or geographical or any other order. Now, the size of the sample is to be decided. Next step is to locate space interval of the population. Suppose there are 100 students from whom 5 students are to be selected for the sample, then all the 100 students will be assigned numbers from 1 to 100. The space interval will be 100/5=20. After this, one student is selected first at random from the first 20 students. If his number comes out to be 9, then other 4 students are taken corresponding to the numbers 29(9+20), 49 (29+20), 69 (49+20) and 89 (69+20). (ili) Multistage Sampling Under this method, samples are drawn stage by stage. In the first stage the universe is divided into some clusters from which certain clusters are selected as the first stage. In the second place, the selected first stage samples are again subdivided into some clusters from which again certain clusters are selected at random as the second stage samples. In the third place, the selected second stage samples are again subdivided into some clusters from which certain clusters are again selected at random as the third stage samples. In this way the process of division and subdivision of the clusters and the selection of the multistage samples are carried out till the sample size is reduced to a reasonable extent. (iv) Cluster Sampling Cluster Sampling is a type of restricted random sampling in which the whole universe is divided into certain subgroups called clusters. Certain such clusters are selected at random to provide for sample. All the units belonging to these selected clusters constitute the sample designed. This type of sampling is used when there would be no reliable list of the units of the population. For example, to conduct an opinion poll on the reservation: issue, the whole country may be divided into certain clusters say 5000 blocks. 50 blocks may be selected at random. Now all the inhabitants of these 50 blocks will constitute the sample units to provide with the desired information. *Non-Random Sampling (Non- Probability Sampling)* (1) Convenience Sampling: The most common type of non-probability sampling is convenience sampling. In this the researcher has the freedom of choosing any respondent based on his convenience. Convenience sampling is an economical method of sampling. It is generally used in exploratory phase of survey. But the result of this method will largely be affected by the personal bias of the investigator. (2) Judgement Sampling: In this method of sampling the investigator himself makes the choice of sample from the given universe according to his own skill and judgement which he thinks to be the best representative of the universe. The selection is purely based on the organiser's information about the representativeness of the sample unit. These units are studied in detail and conclusions are drawn. 3) Quota Sampling: This is a type of purposive sampling in which the whole universe is divided first into certain parts and the total sample is allocated among these parts. Each part of the population is assigned to an investigator for whom the quota of the units to be examined by him is fixed in advance according to certain specified characteristics such as sex, age, occupation, income, political involvement, religion etc. The investigator is asked to select the required number of units of the sample of his own accord and examine them to get the desired information as quickly as possible. He can substitute new units in the quota if he finds that any unit of the sample so selected is not responding upto the mark. (4) Snow Ball Sampling: In this a set of respondents are selected initially and interviewed. These respondents are asked to list the names of other people who in their opinion are a part of the target population. Under this method, number of respondents increase stage by stage as in the case of a snow ball which keeps on growing in size as it rolls down. (5) Sequential Sampling: It is also a purposive type of sampling. A number of sample lots are drawn one after another in the order of a sequence till a satisfactory sample lot is obtained. Such technique is generally adopted in SQC (Statistical Quality Control). In such case if the first sample is clearly acceptable no further sample is drawn. If the first sample lot is of doubtful nature, a second and if necessary a third lot is drawn to decide upon the final acceptance or rejection of the lot. (6) Multiphase Sampling: Multi-phase sampling is a type of sample design in which some information is collected from the whole sample and additional information is collected either at the same time or later from sub-sample of the full sample. To conclude, in multiphase sampling, the first step is to select samples as first phase and from it to collect data on some suitable characteristics. Then a sub sample of these units is selected for the main survey. *CLASSIFICATION OF DATA * The process of arranging data in groups or classes according to resemblances and similarities is technically called classification. *TABULATION* Tabulation may be defined "as a systematic arrangement of data in columns and rows". It is designed to simplify the presentation of data for the purpose of analysis and interpretation. According to Horace Secrist, "Tables are means of recording in permanent form the analysis that is made through classification and by placing juxtaposition things that are similar and should be compared" Objectives or importance of Tabulation The following are the objectives of Tabulation: 1. Tabulation simplifies the complexity of data. 2. It facilitates comparison of data. 3. It presents quantitative data in a concise and condensed form. 4. It provides a basis for analysis and interpretation of such data. 5. It ensures economy of space and time. 6. It indicates the trend and pattern of the data. 7. It helps to detect errors of omission and commission. 8. It facilitates source reference for future studies, if secondary data are used in the present investigation These are my quantitative techniques subject presentation so make all topics short but without loosing important points or examples and add a short introduction and conclusion page and add one page for all topics names given below • Classification of Data • Tabulation and its objectives • Sampling - merits and demerits. • Random and non-random sampling methods. • Essentials of drafting a questionnaire. • Law of Inertia of large numbers and Law of Statistical Regularity. • Characteristics of a good sample • Statistical units and their characteristics. • Various methods of collecting primary data. • Secondary data & its various source And add presented by page and add names given below Muhammed Ashraq Sruthy Nandha Lucia Athul Ashita Alan Milna Nayana Follow Design: {"palette":["Oxford Ink #1A202C — dominant typography and classical headers","Warm Vellum #FAF7F2 — editorial reading background","Research Ochre #B45309 — focal callouts and essential laws","Archive Sage #475569 — secondary comparisons and notes","Parchment Container #F3EFE6 — distinction cards and formula panels"],"fonts":{"Lora":"https://fonts.googleapis.com/css2?family=Lora:ital,wght@0,400..700;1,400..700&display=swap","Merriweather Sans":"https://fonts.googleapis.com/css2?family=Merriweather+Sans:ital,wght@0,300..800;1,300..800&display=swap"},"type":"Lora in medium and bold (32-40pt) for authoritative editorial titles; Merriweather Sans (13-15pt) with generous line height for analytical body paragraphs, sub-points, and itemized qualities of good questionnaires.","layout":"Symmetrical two-column scholarly layout reminiscent of a research journal, featuring prominent left-aligned section markers, distinct marginal definition blocks for statistical units, and ample negative space separating conceptual categories.","framework_treatment":"Fine horizontal dividing rules, elegant quotation containers for Horace Secrist definitions, numbered pill badges for sampling demerits and merits, paired comparison tables for primary versus secondary data, and an unhurried, minimalist layout on the team introduction page.","feels_like":"An authoritative university press economics journal transformed into high-legibility lecture slides"}
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