Proportionate Reduction In Error Definition
Contents |
PRE
Proportional Reduction In Error Lambda
measures. Proportional Reduction of Error (PRE) The the proportionate reduction in error is a measure of the quizlet concept that underlies the definition and interpretation of several measures
Proportionate Reduction In Error Symbol
of association, PRE measures are derived by comparing the errors made in predicting the dependent while ignoring the proportional reduction calculator independent variable with errors made when making predictions that use information about the independent variable. E1 = errors of prediction made when the independent variable is ignored E2 = errors of prediction made when the prediction proportional reduction in error stata is based on the independent variable "All PRE measures are based on comparing predictive error levels that result from each of two methods of prediction" (Frankfort-Nachmias and Leon-Guerrero 2011:366). Table 12.1 on page 366 of the textbook helps us to understand this. The independent variable is number of children; the dependent variable is support for abortion. Content on this page requires a newer version of Adobe Flash Player. Two of the most commonly used PRE measures of association are lambda (λ) and gamma (γ). Two PRE Measures: Lambda and Gamma Lambda λ Appropriate for: Nominal Variables Gamma γ Appropriate for: Ordinal and Dichotomous Nominal Variables
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 the
Proportionate Reduction In Error Can Be Symbolized By
intended quantity compared with not having those observations. Proportional reduction in error proportional reduction in error spss is a more restrictive framework widely used in statistics, in which the general loss function is replaced by a more
Proportional Reduction In Error Interpretation
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 by https://learn.bu.edu/bbcswebdav/pid-826908-dt-content-rid-2073693_1/courses/13sprgmetcj702_ol/week05/metcj702_W05S03T02_proportional.html 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 that https://en.wikipedia.org/wiki/Proportional_reduction_in_loss 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 WikipediaWikipedia store Intera
Login Username Password Remember me? Forgot your login information? http://methods.sagepub.com/reference/the-sage-encyclopedia-of-social-science-research-methods/n765.xml Reset your password Other Login Options OpenAthens Shibboleth Can't login? Find out how to access the site Search form Advanced Back Browse Browse Content Type BooksLittle Green BooksLittle Blue BooksReferenceJournal ArticlesDatasetsCasesVideo Browse Topic Key concepts in researchPhilosophy of researchResearch ethicsPlanning researchResearch designData collectionData quality reduction in and data managementQualitative data analysisQuantitative data analysisWriting and disseminating research Browse Discipline AnthropologyBusiness and ManagementCriminology and Criminal JusticeCommunication and Media StudiesCounseling and PsychotherapyEconomicsEducationGeographyHealthHistoryMarketingNursingPolitical Science and International RelationsPsychologySocial Policy and Public PolicySocial WorkSociology AnthropologyBusiness and ManagementCriminology and Criminal JusticeCommunication and Media StudiesCounseling and PsychotherapyEconomicsEducationGeographyHealthHistoryMarketingNursingPolitical Science and reduction in error International RelationsPsychologySocial Policy and Public PolicySocial WorkSociology Research Tools Methods Map Reading Lists Proportional Reduction Of Error (PRE) | The SAGE Encyclopedia of Social Science Research Methods Search form Not Found Show page numbers Download PDF Sections Menu Opener Search form icon-arrow-top icon-arrow-top Page Site Advanced 7 of 230 Not Found Opener Sections within this page Sections Proportional Reduction Of Error (PRE) In: The SAGE Encyclopedia of Social Science Research Methods Encyclopedia By: Scott Menard Edited by: Michael S. Lewis-Beck, Alan Bryman & Tim Futing Liao Published: 2004 DOI: http://dx.doi.org/10.4135/9781412950589.n765 +- LessMore information Print ISBN: 9780761923633 | Online ISBN: 9781412950589 Online Publication Date: January 1, 2011 Disciplines: Anthropology, Business and Management, Communication and Media Studies, Criminology and Criminal Justice, Economics, Education, Geography, Health, History, Marketing, Nursing, Political Science and International