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Overview of Systematic Sampling

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  SYSTEMATIC SAMPLING By: UJJAINI DALAL CHRIST UNIVERSITY, BANGALORE S ystematic sampling is a commonly employed technique if the complete and up-to-date list of the sampling units in available. This consists in selecting only the first unit at random, the rest being automatically selected according to some predetermined pattern involving regular spacing of units. Systematic sampling is a statistical method that researchers use to zero down on the desired population they want to research.   Systematic sampling is an extended implementation of probability sampling in which each member of the group is selected at regular periods to form a sample. How Systematic Sampling Works When we  are sampling, ensure you represent the population fairly. Systematic sampling is a symmetrical process where the researcher chooses the samples after a specifically defined interval. Sampling like this leaves the researcher no room for bias regarding choosing the sample. Let us suppose that N samp
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Selection of samples:SRSWR vs SRSWOR(2048114)

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  Selection of samples(SRSWR VS SRSWOR) Anindita Sarkar(2048114) INTRODUCTION- SIMPLE RANDOM SAMPLING (with replacement Vs without replacement) :- The simplest of the methods of probability sampling which is usually called the method of random sampling. In this method, an equal probability of selection is assigned to each available units of the population at the first and each subsequent draw. Thus, if the number of units in the population is N, then the probability of selection of any unit at first draw is 1/N and at the second draw is 1/N-1 etc, which are ultimately equal to 1/N. The sample obtained using the above method is called “ Simple Random Sampling”. Since this result is independent of the specified unit it follows that every one of the units in the population has the same chance of being included in the sample under the procedure of simple random sampling. Difference Between SRSWOR and SRSWR: (i)                   If the selected units are not being replaced back
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Non Probability Sampling

By K Krishna Kumar Definition:   Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. It is carried out by observation, and researchers use it widely for qualitative research. Non-probability sampling is a sampling method in which not all members of the population have an equal chance of participating in the study, unlike probability sampling. Each member of the population has a known chance of being selected. Non-probability sampling is most useful for exploratory studies like a pilot survey (deploying a survey to a smaller sample compared to pre-determined sample size). Researchers use this method in studies where it is impossible to draw random probability sampling due to time or cost considerations. Types of non-probability sampling Here are the types o
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  Simple Random Sampling with Replacement                                                                                                                                      -- Anasua Dutta Simple Random Sampling Simple random sampling (SRS) is a method of selection of a sample comprising of n number of sampling units out of the population having N number of sampling units such that every sampling unit has an equal chance of being chosen. The samples can be drawn in two possible ways. · The sampling units are chosen without replacement in the sense that the units once are chosen are not placed back in the population.   · The sampling units are chosen with replacement in the sense that the chosen units are placed back in the population. Simple Random Sampling With Replacement (SRSWR): SRSWR is a method of selection of n units out of the N units one by one such that at each stage of selection, each unit has an equal chance of being selected, i.e., 1/ N . For Example:-
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