Proportional Reduction Of Error
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 proportional reduction in error spss intended quantity compared with not having those observations. Proportional reduction in error is proportionate reduction in error can be symbolized by a more restrictive framework widely used in statistics, in which the general loss function is replaced by a more direct regression to the mean occurs because extreme scores tend to become: 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 Bruce Cooil https://learn.bu.edu/bbcswebdav/pid-826908-dt-content-rid-2073693_1/courses/13sprgmetcj702_ol/week05/metcj702_W05S03T02_proportional.html 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 are applicable https://en.wikipedia.org/wiki/Proportional_reduction_in_loss 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 Interaction HelpAbout WikipediaCommunity portalRecent chan
Login Help Contact Us About Access You are not currently logged in. Access your personal account or get JSTOR access through your library or other institution: login Log in to your personal https://www.jstor.org/stable/4106125 account or through your institution. The Sociological Quarterly Vol. 22, No. 3, Summer, 1981 Interpreting Proport... Interpreting Proportional Reduction in Error Measures as Percentage of Variation Explained Frederick J. Kviz The Sociological Quarterly Vol. 22, http://www.d.umn.edu/~schilton/2700/LectureNotes/PREsynopsis.html No. 3 (Summer, 1981), pp. 413-420 Published by: Wiley on behalf of the Midwest Sociological Society Stable URL: http://www.jstor.org/stable/4106125 Page Count: 8 Download ($14.00) Subscribe ($19.50) Cite this Item Cite This Item Copy Citation Export Citation reduction in Export to RefWorks Export a RIS file (For EndNote, ProCite, Reference Manager, Zotero…) Export a Text file (For BibTex) Note: Always review your references and make any necessary corrections before using. Pay attention to names, capitalization, and dates. × Close Overlay Journal Info The Sociological Quarterly Description: The Sociological Quarterly is devoted to publishing cutting-edge research and theory in all areas of sociological inquiry. Our focus is on publishing the best reduction in error in sociological research and writing to advance the discipline and reach the widest possible audience. Since 1960, the contributors and readers of The Sociological Quarterly have made it one of the leading generalist journals in the field. Each issue is designed for efficient browsing and reading and the articles are helpful for teaching and classroom use. Coverage: 1960-2010 (Vol. 1, No. 1 - Vol. 51, No. 4) Moving Wall Moving Wall: 5 years (What is the moving wall?) Moving Wall The "moving wall" represents the time period between the last issue available in JSTOR and the most recently published issue of a journal. Moving walls are generally represented in years. In rare instances, a publisher has elected to have a "zero" moving wall, so their current issues are available in JSTOR shortly after publication. Note: In calculating the moving wall, the current year is not counted. For example, if the current year is 2008 and a journal has a 5 year moving wall, articles from the year 2002 are available. Terms Related to the Moving Wall Fixed walls: Journals with no new volumes being added to the archive. Absorbed: Journals that are combined with another title. Complete: Journals that are no longer published or that have been combined with another
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 sha