Proportional Reduction Error
Contents |
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
Proportional Reduction In Error Lambda
uncertainty about the intended quantity compared with not having those observations. Proportional the proportionate reduction in error is a measure of the quizlet reduction in error is a more restrictive framework widely used in statistics, in which the general loss function is
Proportionate Reduction In Error Symbol
replaced by 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 proportional reduction calculator 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 also provides a general way of developing measures for the reliability of qualitative data. For example, proportional reduction in error stata 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 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 R
of making observations which are possibly subject to errors of all types. Such measures quantify how much having the
Proportionate Reduction In Error Can Be Symbolized By
observations available has reduced the loss (cost) of the uncertainty about proportional reduction in error spss the intended quantity compared with not having those observations. Proportional reduction in error is a more
Regression To The Mean Occurs Because Extreme Scores Tend To Become:
restrictive framework widely used in statistics, in which the general loss function is replaced by a more direct measure of error such as the mean square https://en.wikipedia.org/wiki/Proportional_reduction_in_loss 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 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 https://en.wikipedia.org/wiki/Proportional_reduction_in_loss 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 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 h
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 https://www.jstor.org/stable/4106125 in to your personal account or through your institution. The Sociological Quarterly Vol. 22, http://www.tankonyvtar.hu/en/tartalom/tamop425/0010_2A_21_Nemeth_Renata-Simon_David_Tarsadalomstatisztika_magyar_es_angol_nyelven_eng/ch08s02.html 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, 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 reduction in Cite This Item Copy Citation Export Citation 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 reduction in error areas of sociological inquiry. Our focus is on publishing the best 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: Journa
PRE, proportional reduction of errorPRE, proportional reduction of error having a mental medical condition financial status relatively bad relatively good total yes 390 (97,5 %) 10 (2,5 %) 400 (100 %) no 40 (6,7 %) 560 (93,3 %) 600 (100 %) total 430 (43 %) 570 (57 %) 1000 (100 %) Using one of the illustrations from the previous lecture (where we considered mental health to be the independent variable and financial status to be the dependent variable), let’s guess the financial status of the individual respondents based on our knowledge of the distribution: 57% have relatively good, 43% have relatively worse financial status.Let’s imagine that the respondents turn up one by one and we have to guess their financial status as accurately as possible. What’s the best way to do that? having a mental medical condition financial status relatively bad relatively good total yes 390 (97,5 %) 10 (2,5 %) 400 (100 %) no 40 (6,7 %) 560 (93,3 %) 600 (100 %) total 430 (43 %) 570 (57 %) 1000 (100 %) Declareing each respondent to have a relatively good financial status is the safest way: thus we are wrong in 430 cases out of 1000.How does the situation change if we already know Table 1 and we can ask each respondent whether or not they have a mental medical condition?In this case we can improve the chances of our guesswork by categorizing everyone with a mental problem as having worse financial status, while those without mental problems as having better financial status. Thus the number of mistakes we make is down to 50.In other words, the guessing error characterizes the relationship of the two variables. Associational indices that work on this principle are called ’proportional reduction of error’ (PRE) indices.Calculating (λ) to get the connection of two nominal variables:8.1. egyenlet - Where:E1 is the number of categorising mistakes made without considering the independent variableE2 is the number of ca