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the relationship between two nominal or ordinal variables. This lesson focuses on single number statistics that also indicate the strength and direction of relationships between nominal or ordinal level variables proportional reduction in error statistics in a cross-tabulation table. These statistics are called measures of association. Week

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Six will introduce you to Pearson's correlation coefficient, a measure of association for interval-ratio variables. There are numerous measures proportionate reduction in error symbol of association, each with its own strengths, weaknesses, and idiosyncrasies. It is not uncommon for statistics textbooks to introduce a half dozen or more such measures. In fact, sociologist Herbert

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Costner points out that we "suffer an embarrassment of riches with regard to measures of association" (1965:341). Your textbook discusses three measures of association, lambda for nominal and gamma and Kendall's Tau-b for ordinal variables. I hope you don't feel cheated. If at some point in your career, you need to learn about additional measures of association, you can consult any proportional reduction in error spss of numerous other texts. Those by Fox (2003) and Knoke and Bohrnstedt (1994) are among the best. In his Statistics in Criminal Justice (1999), Walker provides background as well as a solid overview of measures of association. For now you may feel relieved to focus on only three such measures in the textbook. Statisticians have developed many measures of association and most researchers have their personal favorites. Including so few measures is a limitation of this textbook because as you explore the research literature, you will find many other measures cited. However, the authors do a better job in explaining the logic of these measures than do many other texts. In earlier editions, Frankfort-Nachmias and Leon-Guerrero did cover more such measures. However, there are good reasons why it is not really such a problem. Lambda and gamma are the most widely-used measures of association for nominal and ordinal variables, respectively. These are the measures that are most important to know and understand. Kendall's tau-b is a variation on gamma. Learning about these measures provides you with a solid foundation for learning ab

Nominal Measures of Association Ordinal Measures of Association Introducing Control Variables Interpreting Control Tables Contingency Tables After examining the univariate frequency

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distribution of the values of each variable separately, the researcher

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is often interested in the joint occurrence and distribution of the values of the independent and proportional error physics dependent variable together. The joint distribution of two variables is called a bivariate distribution. A contingency table shows the frequency distribution of the values of https://learn.bu.edu/bbcswebdav/pid-826908-dt-content-rid-2073693_1/courses/13sprgmetcj702_ol/week05/metcj702_W05S03T01_association.html the dependent variable, given the occurrence of the values of the independent variable. Both variables must be grouped into a finite number of categories (usually no more than 2 or 3 categories) such as low, medium, or high; positive, neutral, or negative; male or female; etc. Constructing a Contingency Table 1) obtain http://web.csulb.edu/~msaintg/ppa696/696bivar.htm a frequency distribution for the values of the independent variable; if the variable is not divided into categories, decide on how to group the data. 2) obtain a frequency distribution for the values of the dependent variable; if the variable is not divided into categories, decide on how to group the data. 3) obtain the frequency distribution of the values of the dependent variable, given the values of the independent variable (either by tabulating the raw data, or from a computer program) 4) display the results of step 3 in a table Example: Independent Variable: Place of Residence Categories: Inside City Limits=505 Outside City Limits=145 Dependent Variable: Attitude about Consolidation Categories: Favor consolidation=327 No Opinion=168 Against consolidation=155 Joint Distribution: Table 1. Attitudes toward Consolidation by Area of Residence Attitude toward Consolidation Area of Residence Inside City Limits Outside City Limits Against 98 57 No Opinion 134 34 For 273 54 Total 505 14

AnalysisData Analysis PlanIRB / URRQuantitative ResultsQualitative ResultsDiscussion CloseDirectory Of Statistical AnalysesCluster AnalysisConduct and Interpret a Cluster AnalysisCluster Analysis ConsultingGeneralConduct and Interpret http://www.statisticssolutions.com/ordinal-association/ a Profile AnalysisConduct and Interpret a Sequential One-Way Discriminant AnalysisMathematical Expectation[ View All ]Regression AnalysisAssumptions of Linear RegressionTwo-Stage Least Squares (2SLS) Regression AnalysisUsing Logistic Regression in Research[ View All ]CorrelationCorrelation (Pearson, Kendall, Spearman)Correlation RatioMeasures of Association[ View All ](M)ANOVA proportional reduction AnalysisAssumptions of the Factorial ANOVAGLM Repeated MeasureGeneralized Linear Models[ View All ]Factor Analysis & SEMConduct and Interpret a Factor AnalysisExploratory Factor AnalysisConfirmatory Factor Analysis[ View All ]Non-Parametric AnalysisCHAIDWald Wolfowitz Run Test[ View All ] CloseDirectory Of Survey InstrumentsAttitudesEmotional IntelligenceLearning / reduction in error Teaching / SchoolPsychological / PersonalityWomenCareerHealthMilitarySelf EsteemChildLeadershipOrganizational / Social GroupsStress / Anxiety / Depression Close CloseFree ResourcesNext Steps Home | Academic Solutions | Directory of Statistical Analyses | Non-Parametric Analysis | Ordinal Association Ordinal Association Ordinal variables are variables that are categorized in an ordered format, so that the different categories can be ranked from smallest to largest or from less to more on a particular characteristic. Examples of ordinal variables include educational degree earned (e.g., ranging from no high school degree to advanced degree) or employment status (unemployed, employed part-time, employed full-time). Numeric variables that are presented in categories or ranges are also considered ordinal as it is not possible to perform mathematical functions on the grouped num

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