LEADER 03922nam 2200637 450 001 9910798685203321 005 20230125200059.0 010 $a1-63157-551-1 035 $a(CKB)3710000000831060 035 $a(BEP)4648714 035 $a(OCoLC)957560388 035 $a(CaBNVSL)swl00406797 035 $a(Au-PeEL)EBL4648714 035 $a(CaPaEBR)ebr11249678 035 $a(CaONFJC)MIL949216 035 $a(OCoLC)957318032 035 $a(CaSebORM)9781631575518 035 $a(MiAaPQ)EBC4648714 035 $a(EXLCZ)993710000000831060 100 $a20160829d2016 fy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 12$aA primer on nonparametric analysis$hVolume II /$fShahdad Naghshpour 205 $aFirst edition. 210 1$aNew York, New York (222 East 46th Street, New York, NY 10017) :$cBusiness Expert Press,$d2016. 215 $a1 online resource (85 pages) 225 1 $aEconomics collection,$x2163-7628 300 $aIncludes index. 311 $a1-63157-550-3 327 $aSection I. K related samples -- 1. Three or more related populations: one-way analysis of variance -- 2. K unrelated samples tests -- Section II. Measures of association -- 3. Correlation coefficient -- Index. 330 3 $aNonparametric statistics provide a scientific methodology for cases where customary statistics are not applicable. Nonparametric statistics are used when the requirements for parametric analysis fail, such as when data are not normally distributed or the sample size is too small. The method provides an alternative for such cases and is often nearly as powerful as parametric statistics. Another advantage of nonparametric statistics is that it offers analytical methods that are not available otherwise. In social sciences, often, it is not possible to obtain measurements, which renders customary analysis impossible. For example, it is not possible to measure utility but is possible to rank preference, which is based on the unmeasurable utility. Nonparametric methods provide theoretically valid options for analysis, making the use of unscientific methods unnecessary. Nonparametric methods are intuitive and simple to comprehend, which helps researchers in the social sciences understand the methods in spite of lacking mathematical rigor needed in analytical methods customarily used in science. The only prerequisite for this book is high school level elementary algebra. This book is a methodology book and bypasses theoretical proofs while providing comprehensive explanations of the logic behind the methods and ample examples, which are all solved using direct computations as well as by using Stata. The book is arranged into two integrated volumes. Although each volume, and for that matter each chapter, can be used separately, it is advisable to read as much of both volumes as possible; because familiarity with what is applicable for different problems will enhance capabilities. It is recommended that everyone read the Introduction and Chapter 1 because determining whether data are random or normally distributed is essential in the selection of parametric versus nonparametric methods. 410 0$aEconomics collection.$x2163-7628 606 $aNonparametric statistics 610 $aNonparametric statistics 610 $amedian 610 $aorder statistics 610 $arank 610 $aone sample 610 $atwo samples 610 $aseveral samples 610 $amultiple comparison 610 $anormality 610 $askewness 615 0$aNonparametric statistics. 676 $a519.5 700 $aNaghshpour$b Shahdad.$0890738 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910798685203321 996 $aA primer on nonparametric analysis$93730749 997 $aUNINA