FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W302110178> ?p ?o ?g. }

• W302110178 endingPage "284" @default.
• W302110178 startingPage "282" @default.
• W302110178 abstract "Introduction The, most prestigious analysis generally assumes hard number theory and multiple regression. This procedure fogs the findings. When 2 of the 4 independent variables are ordinal, one is ratio, and one is nominal, all are summated together as beta weights. Nominal, ordinal, interval, and ratio are separate from each other and when combining the various number powers, it has to appear that all are ratio. That means the findings have a zero base line, can be added, subtracted, divided, and multiplied. Each integer is completely exclusive and equidistant from each other and related. That means apples and oranges can be divided by pears. It could be junk science. Further the researchers generally have medical or related terminal degrees are doing the research. The mystique is added given the credentials of the researchers and the size and power of the corporation or university indicate prestige and authenticity. The final copy is embellished y a professional writer who intertwines science and exotic connotations that suggests complexity. (Snell, J. & Marsh, M 2012) The other is chi-square.(math.huis.edu/ javamath/ryan/ChiSquare.html) Chi-Square is easy to learn and understand. Thus the glow of science is lessened. Chi-Square is thought to be much less robust and analytical than say StepWise Multiple Regression. However, what appears counter intuitive is that Chi Square is less robust and more blunt and yet can cover more numerical territory. The formula is rather simplistic looking and yet it can handle all number powers from nominal to ratio. The outcomes of the two (step-wise and chi-square) are not exactly the same. Stepwise may show differences when given a measure on a dependent variable which has ordinal power. However, that does not necessarily make it superior, rather a number of variables including the structure of the formula may lessen or increase differences. Further, using step-wise, we take all kinds of risks, blur number properties and may create false outcomes, (www.socialresearchmenthods.net/kb/dummyvar.php) The Chance The Article(S) Will Be Published As a thought experiment, two papers on related subjects (one using step-wise and the other chi-square) are released to the same editorial board. All editors have backgrounds in the social or soft science. Without hard evidence, this author can surmise that many go into the field to teach students in their areas of expertise. To them, statistics is to be endured and understood. However, both the editors and the review boards know their stats, but do not cherish the field. They may not catch the scam. The author is comparing one of the most sophist iced statistical analysis and one of the most elemental ones. When the two papers are sent which manuscript is more likely to be accepted? Is it the one with the nominal chi-square, or, the step-wise analysis? Which lends more prestige and the ability to attract even more analytical essays from top schools? This author's money is on the step- wise. It looks great. It glows when Greek, English acronyms, and numbers flourish across the page if the formula is included in the article. The valid outcome could have been with chi-square, because step-wise has so many risks that can be covered and enhanced by the nature of the complexity of reading the statistical strategy. Chi-square is dated and simple. It was replaced with step-wise when the computer could be used to make thousands of calculations in a short time. This is the onset of Big Data. Scholars who may have been intimidated are likely to approve the article. (Silver, N. 20129-12) Are there any errors with chi-square? Here the author believes that there is a metaphorical wind at his back. Nathan Silver (2012) leaning on Gauss and Taleb, N. (2007) assuming Mandelbrot both come to similar, but not same conclusions. The research methodologies used now are flawed. Nearly all the researchers did what they thought was right, but did not have right outcomes. …" @default.
• W302110178 created "2016-06-24" @default.
• W302110178 creator A5065432398 @default.
• W302110178 date "2014-03-22" @default.
• W302110178 modified "2023-09-24" @default.
• W302110178 title "Deconstructing Statistical Analysis." @default.
• W302110178 hasPublicationYear "2014" @default.
• W302110178 type Work @default.
• W302110178 sameAs 302110178 @default.
• W302110178 citedByCount "0" @default.
• W302110178 crossrefType "journal-article" @default.
• W302110178 hasAuthorship W302110178A5065432398 @default.
• W302110178 hasConcept C105795698 @default.
• W302110178 hasConcept C114614502 @default.
• W302110178 hasConcept C135692309 @default.
• W302110178 hasConcept C138885662 @default.
• W302110178 hasConcept C144237770 @default.
• W302110178 hasConcept C2524010 @default.
• W302110178 hasConcept C2778329345 @default.
• W302110178 hasConcept C33923547 @default.
• W302110178 hasConcept C41895202 @default.
• W302110178 hasConceptScore W302110178C105795698 @default.
• W302110178 hasConceptScore W302110178C114614502 @default.
• W302110178 hasConceptScore W302110178C135692309 @default.
• W302110178 hasConceptScore W302110178C138885662 @default.
• W302110178 hasConceptScore W302110178C144237770 @default.
• W302110178 hasConceptScore W302110178C2524010 @default.
• W302110178 hasConceptScore W302110178C2778329345 @default.
• W302110178 hasConceptScore W302110178C33923547 @default.
• W302110178 hasConceptScore W302110178C41895202 @default.
• W302110178 hasIssue "3" @default.
• W302110178 hasLocation W3021101781 @default.
• W302110178 hasOpenAccess W302110178 @default.
• W302110178 hasPrimaryLocation W3021101781 @default.
• W302110178 hasRelatedWork W1566130020 @default.
• W302110178 hasRelatedWork W1603301621 @default.
• W302110178 hasRelatedWork W1620616780 @default.
• W302110178 hasRelatedWork W1980001641 @default.
• W302110178 hasRelatedWork W1989597095 @default.
• W302110178 hasRelatedWork W1995629585 @default.
• W302110178 hasRelatedWork W1996684865 @default.
• W302110178 hasRelatedWork W2022732643 @default.
• W302110178 hasRelatedWork W2041609527 @default.
• W302110178 hasRelatedWork W2081828561 @default.
• W302110178 hasRelatedWork W2093168205 @default.
• W302110178 hasRelatedWork W2180707604 @default.
• W302110178 hasRelatedWork W221642113 @default.
• W302110178 hasRelatedWork W2324456953 @default.
• W302110178 hasRelatedWork W2335463976 @default.
• W302110178 hasRelatedWork W245100862 @default.
• W302110178 hasRelatedWork W2520979043 @default.
• W302110178 hasRelatedWork W2548177687 @default.
• W302110178 hasRelatedWork W52826563 @default.
• W302110178 hasRelatedWork W2157910794 @default.
• W302110178 hasVolume "134" @default.
• W302110178 isParatext "false" @default.
• W302110178 isRetracted "false" @default.
• W302110178 magId "302110178" @default.
• W302110178 workType "article" @default.