Stepwise Binary Logistic RegressionStepwise Binary Logistic Regression - 1Stepwise Binary Logistic Regression - 2Pros and Cons of Stepwise Logistic Regression75/25% Cross-validationThe Problem in BlackboardThe Statement about Level of MeasurementMarking the Statement about Level of MeasurementThe statement about multicollinearity and other numerical problemsRunning the Stepwise binary logistic regressionSelecting the dependent variableSelecting the independent variablesDeclare the categorical variables - 1Declare the categorical variables - 2Declare the categorical variables - 3Specifying the method for including variablesRequesting the outputChecking for multicollinearityMarking the statement about multicollinearity and other numerical problemsThe statement about sample sizeThe output for sample sizeMarking the statement for sample sizeThe stepwise relationship between the dependent and independent variablesThe output for the stepwise relationshipMarking the statement for stepwise relationshipThe statement about the relationship between education and abortion for any reasonOutput for the relationship between education and abortion for any reasonMarking the statement for relationship between education and abortion for any reasonStatement for relationship between fundamentalism and abortion for any reasonOutput for relationship between fundamentalism and abortion for any reasonMarking the relationship between fundamentalism and abortion for any reasonSlide 32Slide 33Slide 34Statement for relationship between socioeconomic index and abortion for any reasonOutput for relationship between socioeconomic index and abortion for any reasonMarking the relationship between socioeconomic index and abortion for any reasonStatement about the usefulness of the model based on classification accuracyComputing proportional by-chance accuracy rateOutput for the usefulness of the model based on classification accuracyMarking the statement for usefulness of the modelStatement about Cross-validationCreating the Training Sample and the Validation Sample - 1Creating the Training Sample and the Validation Sample - 2Creating the Training Sample and the Validation Sample - 3Creating the Training Sample and the Validation Sample - 4Creating the Training Sample and the Validation Sample - 5Creating the Training Sample and the Validation Sample - 6An Additional Task before Running the Stepwise Logistic Regression on Training SampleSelecting Cases with Valid Data for All Variables in the Analysis - 1Selecting Cases with Valid Data for All Variables in the Analysis - 2Selecting Cases with Valid Data for All Variables in the Analysis - 3Selecting Cases with Valid Data for All Variables in the Analysis - 4Selecting Cases with Valid Data for All Variables in the Analysis - 5Selecting Cases with Valid Data for All Variables in the Analysis - 6Run the Stepwise Logistic Regression on the Training Sample - 1Run the Stepwise Logistic Regression on the Training Sample - 2Run the Stepwise Logistic Regression on the Training Sample - 3Run the Stepwise Logistic Regression on the Training Sample - 4Run the Stepwise Logistic Regression on the Training Sample - 5Run the Stepwise Logistic Regression on the Training Sample - 6Run the Stepwise Logistic Regression on the Training Sample - 7Slide 63Run the Stepwise Logistic Regression on the Training Sample - 8Validating the Model - 1Validating the Model - 2Validating the Model - 3Marking the Check Box for the Cross-validation StatementStepwise Binary Logistic Regression: Level of MeasurementStepwise Binary Logistic Regression: Multicollinearity and Sample SizeLogic Diagram for Solving Homework Problems: Stepwise RelationshipStepwise Binary Logistic Regression: Individual RelationshipsStepwise Binary Logistic Regression: Classification AccuracyStepwise Binary Logistic Regression: Cross-validationSlide 1Stepwise Binary Logistic RegressionSlide 2Stepwise Binary Logistic Regression - 1Stepwise binary logistic regression is very similar to stepwise multiple regression in terms of its advantages and disadvantages.Stepwise logistic regression is designed to find the most parsimonious set of predictors that are most effective in predicting the dependent variable. Variables are added to the logistic regression equation one at a time, using the statistical criterion of reducing the -2 Log Likelihood error for the included variables. After each variable is entered, each of the included variables are tested to see if the model would be better off the variable were excluded. This does not happen often.The process of adding more variables stops when all of the available variables have been included or when it is not possible to make a statistically significant reduction in -2 Log Likelihood using any of the variables not yet included. Nonmetric variables are added to the logistic regression as a group. It is possible, and often likely, that not all of the individual dummy-coded variables will have a statistically significant individual relationship with the dependent variable. We limit our interpretation to the dummy-coded variables that do have a statistically significant individual relationship.Slide 3Stepwise Binary Logistic Regression - 2SPSS provides a table of variables included in the analysis and a table of variables excluded from the analysis. It is possible that none of the variables will be included. It is possible that all of the variables will be included. The order of entry of the variables can be used as a measure of relative importance. Once a variable is included, its interpretation in stepwise logistic regression is the same as it would be using other methods for including variables. The number of cases required for stepwise logistics regression is greater than the number for the other forms. We will use the norm of 20 cases for each independent variable, double the recommendation of Hosmer and Lemeshow.Slide 4Pros and Cons of Stepwise Logistic RegressionStepwise logistic regression can be used when the goal is to produce a predictive model that is parsimonious and accurate because it excludes variables that do not contribute to explaining differences in the dependent variable.Stepwise logistic regression is less useful for testing hypotheses about statistical relationships. It is widely regarded as atheoretical and its usage is not recommended. Stepwise logistic regression can be useful in finding relationships that have not been