Stepwise Multiple RegressionLogistic regressionWhat logistic regression predictsLevel of measurement requirementsAssumptionsSample size requirementsMethods for including variablesComputational methodOverall test of relationshipBeginning logistic regression modelEnding logistic regression modelRelationship of Individual Independent Variables and Dependent VariableNumerical problemsStrength of logistic regression relationshipEvaluating usefulness for logistic modelsComparing accuracy ratesComputing by chance accuracyOutliersStudentized residualsStrategy for OutliersSlide 21The Problem in BlackboardThe Statement about Level of MeasurementMarking the Statement about Level of MeasurementSatisfying the Assumptions of Multiple RegressionUsing the Script to Evaluate AssumptionsOpening the ScriptSelect the VariablesSelect the Reference Category for SexSelect the Reference Category for RaceRequest the Tests of Assumptions for Multiple RegressionThe Variables Included in the AnalysisEvaluating the Assumption of Independence of VariablesEvaluating the Assumption of LinearityEvaluating the Assumption of HomoscedasticityEvaluating the Assumption of NormalityEvaluating the Assumption of Independence of ErrorsExcluding Extreme Outliers to Try to Achieve NormalityFeedback on Extreme OutliersOutliers and Extreme Outliers in SPSS OutputTesting Normality for Variables Treated as Metric: empathy7Test of Normality in SPSS OutputAdding the Transformation of Empathy7 to Data SetTesting Normality for Variables Treated as Metric: polviewsSlide 45Testing Normality for Variables Treated as Metric: fundSlide 47The Hierarchical Regression with Transformed VariablesSlide 49Slide 50Evaluating the Assumption of HomoscedasticitySlide 52Slide 53Excluding Extreme OutliersFeedback on Extreme Outliers in the ScriptSlide 56Marking the Check Boxes for Regression AssumptionsRemoving the Transformed VariableRetaining the Dummy-coded Variables for the Hierarchical RegressionThe Dummy-coded Variables in the Data EditorRunning the Hierarchical Regression Model in SPSSIncluding the Dependent and Control Variables in the AnalysisAdding the Predictor Independent Variables to the AnalysisRequesting Additional Statistical OutputAdditional Statistical OutputCompleting the Request for the Hierarchical RegressionThe Statement for Sample SizeSatisfying the Sample Size RequirementMarking the Check Box for Sample Size RequirementStatement about Predictors and Controls as Independent VariablesSignificance of the Predictor VariablesStrength of the Relationship of the Predictor VariablesMarking the Statement for Predictors and Controls as Independent VariablesStatements about Individual RelationshipsSlide 75Output Before and After Predictors are Included.Religious Fundamentalism and Empathy - 1Religious Fundamentalism and Empathy - 2Marking the Statement on Religious Fundamentalism and EmpathyStatement on Political Conservatism and EmpathyPolitical Conservatism and EmpathyMarking the Check Box for Political Conservatism and EmpathyStatement on the Relationship between Sex and EmpathySex and Empathy - 1Sex and Empathy - 2Marking the Statement on the Relationship between Sex and Empathy7Statements on the Relationship between Race and EmpathyRace and Empathy - 1Race and Empathy - 2Marking Statements on the Relationship between Race and EmpathyThe Problem Graded in BlackBoardRemoving the Variables Created by the ScriptSlide 93Logic Diagram for Solving Homework Problems: Level of MeasurementRegression Assumptions for Model 1: Original Variables, All CasesRegression Assumptions for Model 2: Original Variables, Excluding Extreme OutliersRelationships with Individual Controls and PredictorsSlide 98Slide 1Stepwise Multiple RegressionSlide 2Logistic regressionLogistic regression is used to analyze relationships between a dichotomous dependent variable and metric or dichotomous independent variables. (SPSS now supports Multinomial Logistic Regression that can be used with more than two groups, but our focus now is on binary logistic regression for two groups.)Logistic regression combines the independent variables to estimate the probability that a particular event will occur, i.e. a subject will be a member of one of the groups defined by the dichotomous dependent variable. In SPSS, the model is always constructed to predict the group with higher numeric code. If responses are coded 1 for Yes and 2 for No, SPSS will predict membership in the No category. If responses are coded 1 for No and 2 for Yes, SPSS will predict membership in the Yes category. We will refer to the predicted event for a particular analysis as the modeled event.Predicting the “No” event create some awkward wording in our problems. Our only option for changing this is to recode the variable.Slide 3What logistic regression predictsThe variate or value produced by logistic regression is a probability value between 0.0 and 1.0.If the probability for group membership in the modeled category is above some cut point (the default is 0.50), the subject is predicted to be a member of the modeled group. If the probability is below the cut point, the subject is predicted to be a member of the other group.For any given case, logistic regression computes the probability that a case with a particular set of values for the independent variable is a member of the modeled category.Slide 4Level of measurement requirementsLogistic regression analysis requires that the dependent variable be dichotomous.Logistic regression analysis requires that the independent variables be metric or dichotomous. If an independent variable is nominal level and not dichotomous, the logistic regression procedure in SPSS has a option to dummy code the variable for you.If an independent variable is ordinal, we will attach the usual caution.Slide 5AssumptionsLogistic regression does not make any assumptions of normality, linearity, and homogeneity of variance for the independent variables.When the variables satisfy the assumptions of normality, linearity, and homogeneity of variance, discriminant analysis is generally cited as the more effective statistical procedure for evaluating relationships with a non-metric dependent variable.When the variables do not satisfy the assumptions of normality, linearity, and homogeneity of variance, logistic regression is the statistic of choice since it does not make these assumptions.Slide 6Sample size requirementsThe minimum number