# What is the difference between random sampling and simple random sampling?

According to my opinion, random sampling each unit of population has some specified probability (not necessary to be equal) of being selected in the sample. But in SRS each unit of population has equal probability of selected in the sample". Please give appropriate suggestion for this topic?

## Popular Answers

Johannes Schult· Universität des SaarlandesSimple random sample (SRS): every element of the population has the same (nonzero) probability of being drawn. SRS is thus a special case of a random sample.

The inverse of the selection probability can be used to weight the sampled data. The weighting is easier with SRS (than with other types of random samples) because all cases have the same weight.

Rick Edgeman· Utah State University## All Answers (42)

Dick J Brus· Wageningen University and Research Centre, Nanjing Normal University (China)Efstathios Demitriades· Eastern Macedonia and Thrace Institute of Technology (Greece-Kavala)Rick Edgeman· Utah State UniversityKolluru Srinivas· Macquarie UniversityRamesh Bharati· Indian Council of Agricultural ResearchSunil Kumar· Alliance UniversityEdward C Malthouse· Northwestern UniversityJohannes Schult· Universität des SaarlandesSimple random sample (SRS): every element of the population has the same (nonzero) probability of being drawn. SRS is thus a special case of a random sample.

The inverse of the selection probability can be used to weight the sampled data. The weighting is easier with SRS (than with other types of random samples) because all cases have the same weight.

DeletedBonnie Macfarlane· University of QueenslandAlma Dzib Goodin· Learning & Neurodevelopment Research CenterB M Golam Kibria· Florida International UniversityJean Vaillant· Université des AntillesJill M. Montaquila. "Sampling from Finite Populations" (version 2). StatProb: The Encyclopedia Sponsored by Statistics and Probability Societies.

It is freely available at http://statprob.com/encyclopedia/SamplingFromFinitePopulations.html

Venkata Prasad Palakiti· Indian Institute of Technology MadrasSimple Random Sample: You can select groups of size n from the entire population, and every possible group has the same chance of being selected.

Chennupati Ashok· GSL Medical College and General HospitalSmita Ghimire· International Centre for Integrated Mountain DevelopmentEach individual is chosen randomly and entirely by chance, such that each individual has the same probability of being chosen at any stage during the sampling process, and each subset of k individuals has the same probability of being chosen for the sample as any other subset of k individuals in simple random sampling.

Azubuike Victor Chukwuka· National Environmental Standards and Regulations Enforcement Agency (NESREA)Designs other than this one may also give each unit equal probability of being included, but only here does each possible sample of n units have the same probability.

Azubuike Victor Chukwuka· National Environmental Standards and Regulations Enforcement Agency (NESREA)Jeffrey E. Jarrett· University of Rhode IslandNana Celestin· Foundation of Applied Statistics and Data Management (FASTDAM)Under random sampling we can name simple random sampling, convenience sampling, incidental sampling, systematic sampling, and accidental sampling. But only two of them, that is simple random sampling and systematic sampling are unbiased. The application of simple random sampling and systematic sampling is generally limited to context where all subjects in the population are identified. That is the reason why in most countries where the database is poor or not updated, it is at time difficult to apply such sampling techniques and we then resort to close substitute such as convenience sampling. Below is explained the various random sampling techniques.

Simple random sampling

Simple random samples are selected using chance random numbers. Practically, numbers are distributed to the subjects of a population that are all identified in a data base. These numbered cards will be collected and thoroughly mixed in a bowl, and the quantity of needed cards will be selected. The subjects whose numbers have been picked from the bowl will be part of the sample. Instead of mixing, random number can be generated with a computer, table of random numbers or slate lottery. Statistical software can assist us in selecting the desired number of cases at random from a given data base. In SPSS, you can access the command ‘New query’ through the ‘File’ main menu and the ‘Open Database’ sub-menu. The inconvenience of simple random sampling lies on the fact that if the population is large and an initial listing (data base) not done, a lot of time may be wasted doing that. To solve this problem, convenient sampling is used as the closest alternative.

Systematic sampling

Systematic sampling is obtained by selecting any Kth number of the population. Let us consider a situation where we have a population of 4000 students in the Highlands University of Bangangte and FASTDAM needs only 100 subjects for malaria test. K = 4000/100 = 40. Every 40th student will be selected. The first subject (which number is between 1 and 100) will be selected at random. If subject 15 is the first subject to be selected, then subjects 55, 95, 135, 175…..will be selected subsequently. As for simple random sampling, the students shall be numbered or if appropriate, their matriculation numbers can be used. This technique of sampling is also conducive in selecting households where houses are numbered and can apply to many similar situations where targeted subjects or individuals are numbered. Here also we absolutely need a data base.

Simple random sampling and systematic sampling are typical examples of probabilistic sampling because every individual is given equal chance of being selected. But the sample size shall be properly estimated for the criteria of probabilistic sampling to really stand. The opposite of probabilistic sampling is non-probabilistic sampling, and in this line we can name as typical examples, convenience, incidental, snowball and accidental samplings.

Let us consider a situation whereby a researcher would like to sample people’s opinions in Fako division. If every member of the population of Fako is listed, and the researcher proceeds by simple random sampling, he would have to struggle to meet those sampled even if this may entail time, financial and effort constraints. But in convenience or incidental sampling, the researcher may avoid this potential burden and deal only with readily available participants, therefore reducing the chance of some people to be involved in the study. In convenience sampling, the sampling bias is somehow reduced if the sample is representative in characteristic and size. This bias is corrected by increasing the DEFF to 2 or 3, which improves the variability.

Lack of reliable and updated population data base, financial and time constraints often push researcher to shift from simple random sampling to convenience sampling.

Convenience sampling

Convenient sampling is a form of random sampling whereby participants are not known, are not initially identified and are met and involved at random when they are available in the course of the study till the initial targeted sample size is met. To make a clear cut difference between random sampling whereby the subjects were identified, numbered or listed drawn at random and a situation whereby they were not identified or numbered, the former is often termed simple random sampling whilst the latter is termed convenience sampling.

The concept of convenience sampling is advised to be used in order to make a clear cut difference with simple random sampling.

Incidental sampling

When an initial number of individuals to meet is not set and where the sample size will depend on how available the targeted participants will be as well as on the chance of meeting them during the period covered by the study, we are then faced with incidental sampling. Here the researcher has no set objective in relation to the number of people he may sample and the sample size will depend essentially on opportunistic circumstances.

Convenient sampling is different from incidental sampling in the sense that in convenient sampling, the number of people to sample for the study is initially set.

Accidental sampling

This sampling technique applies if the identity of the subject of interest is not known in advance and the chance of meeting them assumed slim; e.g., looking for people who are aged 100 years and above in an area. Such key informant is rare and can be met only accidentally.

Accidental sampling is different from incidental sampling in the sense that in incidental sampling, the participants are not rare and the chance of meeting them not all that slim. The similarity is that in both cases, the number of people to meet is not initially set and will be determined by research’s circumstances.

Jeffrey E. Jarrett· University of Rhode IslandNana Celestin· Foundation of Applied Statistics and Data Management (FASTDAM)Jeffrey E. Jarrett· University of Rhode IslandAtif Akbar· Bahauddin Zakariya UniversityObviously, the sample is drawn from some population and if population is homogenous then no matter what unit is selected, in such case simple random sampling implied.

Jeffrey E. Jarrett· University of Rhode IslandPankaj Agrrawal· University of Mainehttp://correctcharts.com/returnfinder/etfs

Hemanta K. Baruah· Bodoland UniversityJeffrey E. Jarrett· University of Rhode IslandJoseph L Alvarez· Alpha Beta GamutRandom means selected by chance. The reason for random sampling is because one is sampling a stochastic process. Note that any definition of random, chance, or stochastic is circular and any of the three can be used to define the other two. The word random does not confer any blessing on sampling. The words simple, stratified and systematic do not confer any blessing on sampling. They are all a part of statistical language and indicate effort made in the sampling process. The most important effort is ensuring representative sampling for the posed argument. One should explain the process of the representative sampling without relying on stock phrases.

Random, chance, and stochastic all mean unknown. The previous statement, "Random means selected by chance," is not only circular, but it conveys that chance performed selection. Neither chance, stochastic, nor random are active participants in the process. Representative sampling is an active process and language and quibbles about language should not detract from that process.

Michael Paul Cohen· American Institutes for ResearchRafael Maria Roman· University of ZuliaArumugam P· Sri Muthukumaran Medical College And Research InstituteEddie Seva See· Bicol Universityhttps://www.researchgate.net/publication/260082130_R_D_Management_in_State_Universities_and_Colleges_in_the_Philippines_Sampling_in_Business_and_Management_Research?ev=prf_pub

## Dataset: R& D Management in State Universities and Colleges in the Philippines: Sampling in Business and Management Research

ABSTRACT:The study sought to determine commonly employed sampling designs in student business/management research. It made use of documentary analysis to 64 research books, 72 statistics books, and 150 undergraduate and graduate student researches. Results show that at most, only 58 of 136 research and statistics books discuss probability sampling designs, the most common of which are simple random sampling, cluster sampling and stratified sampling. Seventy three (73) out of 150 thesis/dissertations that have sampling design utilized proportionate stratified sampling (45) and simple random sampling (28). A maximum of 19 books (out of 136) presentedsampling formulae, most frequent (19) of which is that one that estimates population mean, with infinite population followed by the formula that estimates population proportion with infinite population. All 73 student manuscripts used the sampling formula that was specified in only three books, the formula that requires only population size and margin of error, which is a derivative of the sampling formula that estimates population proportion in a finite population.Jerry Miller· Texas Children's HospitalNana Celestin· Foundation of Applied Statistics and Data Management (FASTDAM)Emilio José Chaves· University of NariñoSarjinder Singh· Texas A&M University - KingsvillePlease read the review of the textbook, "Thinking Statistically (Elephants Go to School)", published in Technometrics to get answer to your question! See the attachment for your convenience!

Murali Dhar· International Institute for Population SciencesRandom sampling refers to the method in which each of the sampling unit (units in the population) has a non-zero probability of being selected into the sample. Simple random sampling is a special case of random sampling when each of the sampling unit has equal chance of being selected. In practice however, when we say random sampling without specifying which one, usually we mean simple random sampling. Other forms of random sampling are: 1) stratified random sampling to account for heterogeneity in the population, 2) systematic random sampling to account for non-availability of sampling frame in the beginning of the study, and cluster random sampling to be used when your population is spread over a large geographic area and consists of some sort of naturally occurring divisions.

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