Proportionate Reduction In Error Equation
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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 proportionate reduction in error can be symbolized by the loss (cost) of the uncertainty about the intended quantity compared proportional reduction in error spss with not having those observations. Proportional reduction in error is a more restrictive framework widely used in
Regression To The Mean Occurs Because Extreme Scores Tend To Become:
statistics, in which the general loss function is replaced by a more direct measure of error such as the mean square error. Examples are the coefficient of determination https://learn.bu.edu/bbcswebdav/pid-826908-dt-content-rid-2073693_1/courses/13sprgmetcj702_ol/week05/metcj702_W05S03T02_proportional.html and Goodman and Kruskal's lambda.[1] The concept of proportional reduction in loss was 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 https://en.wikipedia.org/wiki/Proportional_reduction_in_loss also provides a general way of developing measures for the reliability of qualitative data. For example, this framework provides several 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
Login Username Password Remember me? Forgot your login information? Reset your password http://methods.sagepub.com/reference/the-sage-encyclopedia-of-social-science-research-methods/n765.xml Other Login Options OpenAthens Shibboleth Can't login? Find out http://www.d.umn.edu/~schilton/2700/LectureNotes/PREsynopsis.html 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 and data managementQualitative data analysisQuantitative reduction in 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 International RelationsPsychologySocial Policy and Public PolicySocial WorkSociology reduction in error 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 Relations, Psychology, Social Policy and Public Policy, Social Work, Sociology Buy in print Entry Reader
one another? We need a summary measure; we can't just reproduce the table in our articles and reports. General principle of PRE measures: does knowing the value of a case on one variable help you to predict its value on the other, that is, help you as compared to not knowing its value? General PRE Formula: (error before - error after) / (error before) So: each specific PRE formula has three elements: How shall we measure error in prediction for each case, or what will count as an error? How shall we predict the dependent variable before knowing the independent variable? In general, we use the prediction method which minimizes our total error (subject perhaps to side constraints). How shall we predict the dependent variable after knowing the independent variable? Notice that this measure always varies between 0 and 1. 0 occurs when error before = error after, in other words, when knowing the independent variable doesn't help us predict. In other words, 0 means no association. 1 occurs when error after = 0, i.e., when knowing the independent variable enables us to make a perfect prediction of the dependent variable. In other words, 1 means perfect association. Can there ever be a negative measure? No, because you can't predict worse than by not knowing anything. Can there ever be a measure greater than 100%? No, because that would mean errors after would have to be negative, and there's no such thing as a negative error. We're going to study three measures: Lambda for nominal, Pearson's r-squared for interval, and gamma for ordinal. LAMBDA: A PRE MEASURE FOR NOMINAL VARIABLES For the specific example of nominal variables, the elements of this formula come out as follows: How shall we measure error in prediction, or what will count as an error? Answer: Having our prediction wrong counts as one error. Having it right counts as no errors. For nominal variables, that's the only possible definition of an error. How shall we predict the dependent variable before knowing the independent variable? Answer: We use the mode, which is the prediction method which minimizes the error. How shall we predict the dependent variable after knowing the independent variable? Answer: We use the mode for each category of the independent variable. This measure is called lambda. There are other (and better) measures of association for nominal variables, but this is the simplest. Let's apply this to the table I showed last time: Parents lean: Democrat Republican Total Children lean Democrat 11 (79%) 7 (26%) 18 (44%) Republican 3 (21% 20 (74%) 23 (56%) Total 14