Proportion Reduction Error
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of making observations which are possibly subject to errors of all types. Such measures quantify how much having the observations available has reduced the loss (cost) of the uncertainty about proportional reduction in error lambda the intended quantity compared with not having those observations. Proportional reduction in error
The Proportionate Reduction In Error Is A Measure Of The Quizlet
is a more restrictive framework widely used in statistics, in which the general loss function is replaced by a proportionate reduction in error symbol more direct measure of error such as the mean square error. Examples are the coefficient of determination and Goodman and Kruskal's lambda.[1] The concept of proportional reduction in loss was proposed
Proportional Reduction Calculator
by Bruce Cooil and Roland T. Rust in their 1994 paper. Many commonly used reliability measures for quantitative data (such as continuous data in an experimental design) are PRL measures, including Cronbach's alpha and measures proposed by Ben J. Winer (1971). It also provides a general way of developing measures for the reliability of qualitative data. For example, this framework provides several possible measures proportional reduction in error stata that are applicable when a researcher wants to assess the consensus between judges who are asked to code a number of items into mutually exclusive qualitative categories (Cooil and Rust, 1995). Measures of this latter type have been proposed by several researchers, including Perrault and Leigh (1989). References[edit] ^ Upton G., Cook, I. (2006) Oxford Dictionary of Statistics, OUP. ISBN 978-0-19-954145-4 Cooil, B., and Rust, R. T. (1994), "Reliability and Expected Loss: A Unifying Principle," Psychometrika, 59, 203-216. (available here) Cooil, B., and Rust, R. T. (1995), "General Estimators for the Reliability of Qualitative Data," Psychometrika, 60, 199-220. (available here) Rust, R. T., and Cooil, B. (1994), "Reliability Measures for Qualitative Data: Theory and Implications," Journal of Marketing Research, 31(1), 1-14. (available here) Winer, B.J. (1971), Statistical Principles in Experimental Design. New York: McGraw-Hill. Perreault, W.D. and Leigh, L.E. (1989), “Reliability of Nominal Data Based on Qualitative Judgments,” Journal of Marketing Research, 26, 135-148 Retrieved from "https://en.wikipedia.org/w/index.php?title=Proportional_reduction_in_loss&oldid=735653331" Categories: Comparison of assessments Navigation menu Personal tools Not logged inTalkContributionsCreate accountLog in Namespaces Article Talk Variants Views Read Edit View history More Search Navigation Main pageContentsFeatured contentCurrent eventsRandom articleDonate to
of making observations which are possibly subject to errors of all types. Such measures quantify how much having the observations available has reduced the loss (cost) of the uncertainty about
Proportionate Reduction In Error Can Be Symbolized By
the intended quantity compared with not having those observations. Proportional reduction in
Proportional Reduction In Error Spss
error is a more restrictive framework widely used in statistics, in which the general loss function is replaced by regression to the mean occurs because extreme scores tend to become: a more direct measure of error such as the mean square error. Examples are the coefficient of determination and Goodman and Kruskal's lambda.[1] The concept of proportional reduction in loss was https://en.wikipedia.org/wiki/Proportional_reduction_in_loss proposed by Bruce Cooil and Roland T. Rust in their 1994 paper. Many commonly used reliability measures for quantitative data (such as continuous data in an experimental design) are PRL measures, including Cronbach's alpha and measures proposed by Ben J. Winer (1971). It also provides a general way of developing measures for the reliability of qualitative data. For example, this framework provides several https://en.wikipedia.org/wiki/Proportional_reduction_in_loss possible measures that are applicable when a researcher wants to assess the consensus between judges who are asked to code a number of items into mutually exclusive qualitative categories (Cooil and Rust, 1995). Measures of this latter type have been proposed by several researchers, including Perrault and Leigh (1989). References[edit] ^ Upton G., Cook, I. (2006) Oxford Dictionary of Statistics, OUP. ISBN 978-0-19-954145-4 Cooil, B., and Rust, R. T. (1994), "Reliability and Expected Loss: A Unifying Principle," Psychometrika, 59, 203-216. (available here) Cooil, B., and Rust, R. T. (1995), "General Estimators for the Reliability of Qualitative Data," Psychometrika, 60, 199-220. (available here) Rust, R. T., and Cooil, B. (1994), "Reliability Measures for Qualitative Data: Theory and Implications," Journal of Marketing Research, 31(1), 1-14. (available here) Winer, B.J. (1971), Statistical Principles in Experimental Design. New York: McGraw-Hill. Perreault, W.D. and Leigh, L.E. (1989), “Reliability of Nominal Data Based on Qualitative Judgments,” Journal of Marketing Research, 26, 135-148 Retrieved from "https://en.wikipedia.org/w/index.php?title=Proportional_reduction_in_loss&oldid=735653331" Categories: Comparison of assessments Navigation menu Personal tools Not logged inTalkContributionsCreate accountLog in Namespaces Article Talk Variants Views Read Edit View history More Search Navigation Main pageContentsFeatured contentC
Not registered? Sign up.What's NewAboutGuided TourLibrarian ServicesContact UsHelpThe Oxford Index Underbar oi AllAllTitleAuthorKeywordAllTitleAuthorKeywordHomeBrowseLanguageSelect language English Spanish My Content(1)Recently viewed (1)proportional reduction...My Searches(0)PrintSaveEmailShareText size: AAOverviewproportional reduction in errorFind a librarySelect a Library...No libraries found near you.Search...Related Overviewsassociation variable coefficient of determination More Like ThisShow all results sharing this subject:Probability and StatisticsGOFeedback »Quick Reference(PRE)A criterion underlying some measures of association. The measures attempt to quantify the extent to which knowledge about one variable helps with the prediction of another variable. Examples include R2 (see coefficient of determination) and Goodman and Kruskal's lambda (see association).From: proportional reduction in error in A Dictionary of Statistics »Subjects: Probability and Statistics. Related content in Oxford IndexSee all related items in Oxford Index »Reference entriesproportional reduction in error in A Dictionary of Statistics Reference EntrySearch for the text `proportional reduction in error' anywhere in Oxford Index »Oxford University PressCopyright © 2016. All rights reserved.Privacy policy and legal noticeCreditsSite indexPowered by: Safari Books Online[165.231.164.213|165.231.164.213]165.231.164.213