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Gamma Measures The Proportional Reduction In Error When Predicting

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PRE proportional reduction in error statistics measures. Proportional Reduction of Error (PRE) The proportional reduction in error lambda concept that underlies the definition and interpretation of several measures

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of association, PRE measures are derived by comparing the errors made in predicting the dependent while ignoring the

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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 calculator 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

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

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univariate frequency distribution of the values of each variable separately, proportional error physics the researcher is often interested in the joint occurrence and distribution of the values of a simple way to calculate proportionate reduction in error is by the independent and dependent variable together. The joint distribution of two variables is called a bivariate distribution. A contingency table shows the frequency distribution https://learn.bu.edu/bbcswebdav/pid-826908-dt-content-rid-2073693_1/courses/13sprgmetcj702_ol/week05/metcj702_W05S03T02_proportional.html of the values of 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 http://web.csulb.edu/~msaintg/ppa696/696bivar.htm a Contingency Table 1) obtain 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 Lim

 

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