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  "Title": "Two-Level Sample Selection with Optimal Site Replacement",
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  "Date": "2022-11-29",
  "Authors@R": "c(\nperson(\"Elena\", \"Badillo-Goicoechea\", , \"egoicoe1@jhu.edu\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"\")),\nperson(\"Robert\", \"Olsen\", , \"robolsen@gwu.edu\", role = \"aut\",\ncomment = c(ORCID = \"\")),\nperson(\"Elizabeth\", \"Stuart\", , \"estuart1@jhu.edu\",role = \"aut\",\ncomment = c(ORCID = \"\"))\n)",
  "Description": "Carries out a two-level sample selection where the\npossibility of an initially selected site not wanting to\nparticipate is anticipated, and the site is optimally replaced.\nThe procedure aims to reduce bias (and/or loss of external\nvalidity) with respect to the target population. In selecting\nunits and sub-units, 'sitepickR' uses the cube method developed\nby 'Deville & Tillé', (2004)\n<http://www.math.helsinki.fi/msm/banocoss/Deville_Tille_2004.pdf>\nand described in Tillé (2011)\n<https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2011002/article/11609-eng.pdf?st=5-sx8Q8n>.\nThe cube method is a probability sampling method that is\ndesigned to satisfy criteria for balance between the sample and\nthe population. Recent research has shown that this method\nperforms well in simulations for studies of educational\nprograms (see Fay & Olsen (2021, under review). To implement\nthe cube method, 'sitepickR' uses the sampling R package\n<https://cran.r-project.org/package=sampling>. To implement\nstatistical matching, 'sitepickR' uses the 'MatchIt' R package\n<https://cran.r-project.org/package=MatchIt>.",
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