UT SW 388R - Hierarchical Binary Logistic Regression

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Hierarchical Binary Logistic RegressionSlide 2Output for Hierarchical Binary Logistic Regression after control variables are addedOutput for Hierarchical Binary Logistic Regression after predictor variables are addedThe Problem in BlackboardSlide 6Marking the Statement about Level of MeasurementThe Statement about OutliersRunning the hierarchical binary logistic regressionSelecting the dependent variableSelecting the control independent variablesSelecting the predictor independent variablesDeclare the categorical variables - 1Declare the categorical variables - 2Specifying the method for including variablesAdding the values for outliers to the data set - 1Adding the values for outliers to the data set - 2Requesting the outputDetecting the presence of outliers - 1Detecting the presence of outliers - 2Detecting the presence of outliers - 3Detecting the presence of outliers - 4Detecting the presence of outliers - 5Running the model excluding outliers - 1Running the model excluding outliers - 2Running the model excluding outliers - 3Running the model excluding outliers - 4Running the model excluding outliers - 5Running the model excluding outliers - 6Running the model excluding outliers - 7Running the model excluding outliers - 8Running the model excluding outliers - 9Accuracy rate of the baseline model including all casesAccuracy rate of the revised model excluding outliersMarking the statement for excluding outliersThe statement about multicollinearity and other numerical problemsChecking for multicollinearityMarking the statement about multicollinearity and other numerical problemsThe statement about sample sizeThe output for sample sizeMarking the statement for sample sizeThe hierarchical relationship between the dependent and independent variablesThe output for the hierarchical relationshipMarking the statement for hierarchical relationshipThe statement about the relationship between age and legalization of marijuanaOutput for the relationship between age and legalization of marijuanaMarking the statement for relationship between age and legalization of marijuanaStatement for relationship between general happiness and legalization of marijuanaOutput for relationship between general happiness and legalization of marijuanaMarking the relationship between general happiness and legalization of marijuanaSlide 51Slide 52Slide 53Statement for relationship between religious affiliation and legalization of marijuanaOutput for relationship between religious affiliation and legalization of marijuanaMarking the relationship between religious affiliation and legalization of marijuanaSlide 57Output for the relationship between religious affiliation and legalization of marijuanaSlide 59Slide 60Slide 61Slide 62The statement for the relationship between sex and legalization of marijuanaOutput for the relationship between sex and legalization of marijuanaMarking the statement for the relationship between sex and legalization of marijuanaStatement 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 modelHierarchical Binary Logistic Regression: Level of MeasurementStandard Binary Logistic Regression: Exclude OutliersHierarchical Binary Logistic Regression: Multicollinearity and Sample SizeHierarchical Binary Logistic Regression: Hierarchical RelationshipHierarchical Binary Logistic Regression: Individual RelationshipsHierarchical Binary Logistic Regression: Classification AccuracySlide 1Hierarchical Binary Logistic RegressionSlide 2Hierarchical Binary Logistic RegressionIn hierarchical binary logistic regression, we are testing a hypothesis or research question that some predictor independent variables improve our ability to predict membership in the modeled category of the dependent variable, after taking into account the relationship between some control independent variables and the dependent variable.In multiple regression, we evaluated this question by looking at R2 change, the increase in R2 associated with adding the predictors to the regression analysis. The analog to R2 in logistic regression is the Block Chi-square, which is the increase in Model Chi-square associated with the inclusion of the predictors. In standard binary logistic regression, we interpreted the SPSS output that compared Block 0, a model with no independent variables, to Block 1, the model that included the independent variables.In hierarchical binary logistic regression, the control variables are added SPSS in Block 1, and the predictor variables are added in Block 2, and the interpretation of the overall relationship is based on the change in the relationship from Block 1 to Block 2.Slide 3Output for Hierarchical Binary Logistic Regression after control variables are addedIn this example, the control variables do not have a statistically significant relationship to the dependent variable, but they can still serve their purpose as controls.After the controls are added, the measure of error, -2 Log Likelihood, is 195.412.This output is for the sample problem worked below.Slide 4Output for Hierarchical Binary Logistic Regression after predictor variables are addedAfter the predictors are added, the measure of error, -2 Log Likelihood, is 168.542.The hierarchical relationship is based on the reduction in error associated with the inclusion of the predictor variables.Model Chi-square is the cumulative reduction in -2 log likelihood for the controls and the predictors.The difference between the -2 log likelihood at Block 1 (195.412) and the -2 log likelihood at Block 2 (168.542) is Block Chi-square (26.870) which is significant at p < .001.Slide 5The Problem in BlackboardThe Problem in Blackboard -The problem statement tells us:-the variables included in the analysis -whether each variable should be treated as metric or non-metric-the type of dummy coding and reference category for non-metric variables-the alpha for both the statistical relationships and for diagnostic testsSlide 6The Statement about Level of MeasurementThe first statement in the problem asks about level of measurement. Hierarchical binary logistic regression requires that the dependent variable be dichotomous, the metric independent variables be interval level, and the non-metric independent variables be dummy-coded if they are not dichotomous. SPSS Binary Logistic


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UT SW 388R - Hierarchical Binary Logistic Regression

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