Proportional Reduction In Error Gamma
Contents |
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 in a cross-tabulation table. proportional reduction in error statistics These statistics are called measures of association. Week Six will introduce you proportional reduction in error lambda to Pearson's correlation coefficient, a measure of association for interval-ratio variables. There are numerous measures of association, each with its proportionate reduction in error symbol 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 Costner points out that we "suffer an proportional reduction calculator 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 of numerous other texts. Those by Fox (2003) and Knoke and
Proportional Reduction In Error Spss
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 about additional measures when you need to. Although we can use percentage point differences to assess the strength of relationships between nomi
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, it will
Proportionate Reduction In Error Can Be Symbolized By
enlarge. Hit your back button to return to the page Proportionate Reduction proportional reduction in error stata of Error (PRE) is the logical foundation of determining measures of association. For example, suppose that you were pre measures range from 0 to ±1. a value of +1 would indicate a: 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 Jewish. How would you https://learn.bu.edu/bbcswebdav/pid-826908-dt-content-rid-2073693_1/courses/13sprgmetcj702_ol/week05/metcj702_W05S03T01_association.html 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 have some errors, but most of http://commons.esc.edu/spss/examining-relationships-among-variables/measures-of-association/ 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 variables. Gamma ranges from -1.00 to 1.00. Again, a Gamma of 0.00 refl
be down. Please try the request again. Your cache administrator is webmaster. Generated Mon, 24 Oct 2016 17:48:30 GMT by s_wx1196 (squid/3.5.20)