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StatisticsGraphsExamining Relationships Among VariablesCrosstabulationsMeasures of Association and CorrelationChi-Square Test of IndependenceExamining Differences Between GroupsANOVAT- tests Search for: Measures of Association and Correlation Note: if you click on an image,

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it will enlarge. Hit your back button to return to the proportional reduction in error stata page Proportionate Reduction of Error (PRE) is the logical foundation of determining measures of association.  For example, proportional reduction in error statistics suppose that you were told that there were a 100 people in a room and each person would leave individually.  You are asked to guess whether the person is

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Jewish.  How would you make your decision?  Logically, you would think about the proportion of Jews to the population of people in the community.  If you know that Jews are a minority subgroup in the community, what would make the best guess for each and every person leaving that room?  You would probably choose “not Jewish”.  You will

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have some errors, but most of the time you would be correct.  Now, is there additional information that would help you improve your prediction?  What would happen if you knew that the room was in the temple?  Would you change your prediction?  If this information improves your predictions (and correspondingly reduces your mistakes, or “proportionately reduces your error”), that is information that you want to know.  This is the logic behind the measurements of association. How do you measure association? Lambda is a measure of association for nominal variables.  Lambda ranges from 0.00 to 1.00.  A lambda of 0.00 reflects no association between variables (perhaps you wondered if there is a relationship between a respondent having a dog as a child and his/her grade point average).  A Lambda of 1.00 is a perfect association (perhaps you questioned the relationship between gender and pregnancy).  Lambda does not give you a direction of association:  it simply suggests an association between two variables and its strength. Gamma is a measure of association for ordinal

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Search form Advanced Back Browse Browse Content Type BooksLittle Green BooksLittle Blue BooksReferenceJournal ArticlesDatasetsCasesVideo Browse interpreting lambda in spss Topic Key concepts in researchPhilosophy of researchResearch ethicsPlanning researchResearch designData collectionData quality and data managementQualitative data analysisQuantitative data analysisWriting and disseminating research Browse Discipline AnthropologyBusiness http://commons.esc.edu/spss/examining-relationships-among-variables/measures-of-association/ 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 Research Tools Methods Map Reading Lists Proportional Reduction Of Error (PRE) | http://methods.sagepub.com/reference/the-sage-encyclopedia-of-social-science-research-methods/n765.xml 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’s Guide Entries A-Z Subject Index Search form Not Found Download PDF Show page numbers Looks like you do not have access to this c

if you know the right questions to ask. At this point we will focus on describing how strong the relationship is in table. Measures of association In this topic, we will deal with measures of association http://homes.chass.utoronto.ca/~josephf/pol242/5bMeasuringAssociation.htm between two variables calculated in conjunction with a contingency table. Elsewhere, we discuss measures of association used when comparing means and in doing regression analysis. As already noted, statistical significance means only that we can confidently infer that there is some degree of relationship between our variables in the population from which the sample was drawn. We would also like to know how strong the relationship is in the sample. With population data we do proportional reduction not need to worry about statistical significance (since we are dealing directly with the entire population, we dont need to worry about inferring something about the population based on a sample), but we would still like to know how strong the relationship is. Assessing the strength of a relationship is where measures of association come in. The best way to determine whether a relationship in a table is strong or weak is to examine the proportional reduction in table itself. If the percentage differences among categories of the independent variable seem important, they probably are. Just how big the differences need to be to be considered important will vary with the research questions you are asking. A difference of 10 percentage points is not very dramatic, but in a two-candidate political campaign it could spell the difference between a comfortable 55 to 45 percent win and a decidedly uncomfortable defeat by the same margin. Still, measures of association between variables are a useful way to summarize the strength of a relationship. This is especially true if you are running a large number of crosstabulations and need a convenient way of sorting out the results to determine which relationships are most important. In general, measures of association range in value from 0 (indicating no relationship), to 1 (indicating a perfect relationship). Some measures appropriate for use with nominal data range from 0 to a number approaching, but never reaching, 1. Measures appropriate with nominal data are always positive (since direction has no meaning with nominal data), while those appropriate for use with ordinal data or higher may be either positive or negative. The strength of the relationship is indicated by the absolute value of the measure, not its sign. An association of -.7 is much stronger than one of +.2. Moreover, the sign

 

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