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Posted on Aug 05, 2021

A subdivision of the humanitarian organization, doctors without borders, based in Paris has 246 personnel in its database according to the table below. This database

A subdivision of the humanitarian organization, doctors without borders, based in Paris has 246 personnel in its database according to the table below. This database gives in alphabetical order the name of the staff members, gender, age at last birthday, years with the organization, the country where the staff member is based, and their training in the medical field. You wish to get information about the whole population included in this database including criteria such as job satisfaction, safety concerns in the country of work, human relationships in the country, and other qualitative factors. For budget reasons you are limited to interviewing a total of 40 people and some of these will be done by telephone but others will be personal interviews in the country of operation.

Develop a sampling plan to select the 40 people. Consider total random sampling, cluster sampling and strata sampling. In all cases use the random number function in Excel to make the sample selection. For each of the plans that you have selected draw conclusions. Which do you believe is the best experiment? Explain your reasoning.

No. Name Gender Age Years with agency F 26 2 Country where based Chile Medical training Nurse FF FF FF MF 45 41 29 46 46 46 41 40 217 346 22 29 MF FM FF FM MM MF MM MF F28 34 32 23 34 31 32 37 36 53 28 81 111 10 97 26 28 330 Brazi F FF MF FF MM FF 31 23 30 54 34 31 50 45 25 33 53 63 133 10 725 11 510 19 Brazil Radiographer Kenya Nurse Vietnam Nu 33 31 46 45 33 MM FM MM MF FF FM 30 38 45 49 35 47 55 13 32 85 39 27 14 16 35 Chile Brazil Thailand Cambodia Brazil 5 16 224 14 150 Monnot, Julien 151 Montfort, Laura 152 Morgue, Francois 153 Nauwelaers, Emmanuel 154 Nddalla-Ella, Claude 15 M FF FM MM FF FF MM MM FF MM MM FF FF 55 45 31 51 32 48 47 54 33 54 53 39 46 47 47 51 36 34 26 50 38 22 20 523 2 238 Willot, Mathieu 239 Wlodyka, Sebastien 240 Wurm, Debora 241 Xheko, Eni 242 Xu, Ning 243 Yuan, Zhiyi 244 Zairi, Leila 245

Medical training No. Name Gender Age Years with Country where agency 2 based Chile Abissa, Yasmina Murielle 1 F 26 Nurse Adekalom, Maily 3 F 45 17 Brazil Gen. Med. Adjei, Abena 4 F 41 16 Chile Nurse Ahihounkpe, Ericka Akintayo, Funmilayo 6 F 29 Kenya Brazil Physiotherapy Nurse F 46 12 Alexandre, Gaelle Alibizzata, Mylene Ama, Eric 9 F 46 19 Kenya Chile Brazil Nurse Radiographer Nurse F 31 30 7 2 8 M 2 Angue Assoumou, Melodie F 47 18 Chile Nurse 10 Arfort, Sabrina 11 Aubert, Nicolas 12 Aubery, Olivia 13 Aulombard, Audrey 14 Awitor, Euloge 15 Ba, Oumy 16 Bakouan, Aminata Banguebe, Sandrine 18 Baque, Nicolas 19 Batina, Cedric 20 Batty-Ample, Agnes 21 F 47 12 Cambodia Costa Rica Nurse M 50 20 Nurse F 34 12 Thailand Nurse F 49 18 Brazil Physiotherapy Nurse M 36 12 Kenya Chile F 27 1 Nurse Nurse 24 3 Vietnam 17 F 41 1 Costa Rica Nurse M 42 14 2 10 Kenya Kenya Chile Nurse M 32 Nurse F 31 Nurse Baud, Maxime F 44 18 Costa Rica Nurse

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answer Random Number Name Gender Age Years with agency Country where based Medical training 104 Gesrel Valentin M 40 11 Nigeria Nurse 59 Dansou Joel M ... blur-text-image