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# SAMPLING

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SAMPLING BASICS

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### SAMPLING

1. 1. WELCOME
2. 2. SAMPLING
3. 3. Statistical Analysis In Statistics collecting information for statistical analysis is called collection of Data and the aggregate of the objects of study is called the population . There are two methods of collecting data: 1. Census Method: It is the study of whole population. 2. Sampling Method: It is the study of the sample of the population.
4. 4. What is Sampling? Sampling is the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population. •The sample is representative of the population.
5. 5. i. Time: Studying a sample out of whole population saves time of studying each and every element of population. ii. Cost: Studying a sample out of whole population saves cost involved in studying each and every element of population. iii. Reliability: Results obtained from sampling are often more reliable than results obtained from study of whole population. iv. Details of information: Again as the size of a sample is small, every member of the sample can be studied rigorously and detailed information can be obtained about it . Advantages of Sampling
6. 6. TERMINOLOGIES  Population: the aggregation of the specified elements as defined for a given survey.  Sample or Target population: the aggregation of the population from which the sample is actually drawn.  Sample frame: a specific list that closely approximates all elements in the population—from this the researcher selects units to create the study sample.  Sample: a set of cases that is drawn from a larger pool and used to make generalizations about the population.  Sample element: a case or a single unit that is selected from a population and measured in some way.  Interface: It is the result of the sampling process. It is the data collected from samples which can depict the characteristics of the population.
7. 7. SampleSampling Frame Sampling Process What you want to talk about What you actually observe in the data Inference
8. 8. There are 2 methods of sampling: Non-Probability sampling Probability sampling Types of Sampling
9. 9. Non-Probability sampling • In Non-Probability sampling an item is included in the sample on the basis of personal judgment of the investigator.
10. 10. Types of Non-Probability sampling Judgement sampling: the selection of respondents is predetermined according to the characteristic of interest made by the researcher. eg: If we want to investigate expenditure pattern of 900 students on roll, say we study 100 students at our will. So there is no rule for going against our own will.
11. 11. Quota sampling: To avoid the expenses of approaching the chosen people, in quota sampling the investigator interviews all the people he meets up to a certain number called his Quota. E.g.: age, sex, working class,etc.
12. 12. Cluster sampling: in this case we first classify all the members into several groups or clusters wherein the individual members belonging to the cluster will be chosen from. Next, we perform a simple random sampling of size k from the K clusters formed. Finally, all the members from each of the k clusters will be enumerated and be a part of the sample.
13. 13. Convenience sampling: As the name suggests in this method items for sample are selected according to the convenience of the investigator. E.g.: If a person wants to study problems of the higher education, he may choose a college nearer to his residence and interview students & teachers over there.
14. 14. Sequential sampling: Here a number of sample are drawn one after other. If the result obtained from the first sample are satisfactory then further samples are not drawn. But if the results obtained from first sample are not satisfactory the first sample is rejected. Now a second sample is drawn and so on…. similarly
15. 15. Simple Random sampling : the technique of obtaining the sample by giving each member of the population an equal chance of being included in the sample. Example: If we want to select 100 boys out of 900.  Lottery method: We can write names of all boys on chits of paper and select 100 chits giving names of selected boys.  Table of random numbers: Random numbers table, drawing out of a hat, random timer, etc. Types of Probability sampling
16. 16. Systematic sampling : easier alternative to simple random sampling in the sense that there will be less pieces of paper to prepare and to be drawn. E.g.: If we want to select 100 girls out of 900 girls. We need to make random list of there names. And then say we decide to choose every 9th girl out of first 10. then girls listed 19,29,39….will be choose for sample to be studied .
17. 17. Multi-stage sampling : - is used when the population is very large and coming from a wide area. E.g.: If we are conducting survey for teachers from Maharashtra. Then at first stage we will divide state into different districts and select few districts. Then at second stage districts may be divided in talukas and select few districts. Then at third stage talukas may be divided into villages and after selecting few villages we reach final stage where we get small concentrated areas where detailed enquiry is done.
18. 18. Stratified sampling  Divide the population by certain characteristics into homogeneous subgroups (strata) (e.g., UI PhD students, Masters Students, Bachelors students).  Elements within each strata are homogeneous, but are heterogeneous across strata.  A simple random or a systematic sample is taken from each strata relative to the proportion of that strata to each of the others E.g.: If we conduct sample survey of lung cancer for 1000 smokers, out of which • 300 smoke pipe • 500 smoke cigarette • 200 smoke bidi Suppose we have to select a sample of 250 i.e one fourth of total SMOKERS . So we need to select one forth of every strata i.e. 75,125 and 50
19. 19. It is generally some difference between a static value (the value obtained from samples) and its corresponding parameter (the value obtained from population) This difference is called SAMPLING ERROR. Sampling Error
20. 20. THANK YOU!!!