Hierarchical Multiple RegressionDifferences between standard and hierarchical multiple regressionDifferences in statistical resultsVariations in hierarchical regression - 1Variations in hierarchical regression - 2Differences in solving hierarchical regression problemsA hierarchical regression problemLevel of measurement - answerSample size - questionThe baseline regression - 1The baseline regression - 2The baseline regression - 3The baseline regression - 4The baseline regression - 5R² for the baseline modelSample size – evidence and answerAssumption of normality for the dependent variable - questionRun the script to test normalityNormality of the dependent variable: spouse’s highest degreeNormality of the transformed dependent variable: spouse’s highest degreeNormality of the control variable: ageNormality of the control variable: ageNormality of the predictor variable: highest academic degreeNormality of the predictor variable: respondent’s highest academic degreeAssumption of linearity for spouse’s degree and respondent’s degree - questionRun the script to test linearityLinearity test: spouse’s highest degree and respondent’s highest academic degreeLinearity test: spouse’s highest degree and respondent’s ageAssumption of homogeneity of variance - questionRun the script to test homogeneity of varianceAssumption of homogeneity of variance – evidence and answerIncluding the transformed variable in the data set - 1Including the transformed variable in the data set - 2Including the transformed variable in the data set - 3Including the transformed variable in the list of variables in the script - 1Including the transformed variable in the list of variables in the script - 2Detection of outliers - questionThe revised regression using transformationsThe revised regression: substituting transformed variablesThe revised regression: selecting statisticsThe revised regression: saving standardized residualsThe revised regression: obtaining outputOutliers in the analysisSelecting the model to interpret - questionSelecting the model to interpret – evidence and answerRe-running the baseline regression - 1Re-running the baseline regression - 2Revised regression using transformations and omitting outliers - 3Re-running the baseline regression - 4Assumption of independence of errors - questionAssumption of independence of errors: evidence and answerMulticollinearity - questionMulticollinearity – evidence and answerOverall relationship between dependent variable and independent variables - questionOverall relationship between dependent variable and independent variables – evidence and answerSlide 56Slide 57Relationship of the predictor variable and the dependent variable - questionRelationship of the predictor variable and the dependent variable – evidence and answerSlide 60Validation analysis - questionValidation analysis: set the random number seedSet the random number seedValidation analysis: compute the split variableThe formula for the split variableThe split variable in the data editorRepeat the regression for the validationUsing "split" as the selection variableSetting the value of split to select casesCompleting the value selectionRequesting output for the validation analysisValidation analysis - 1Validation analysis - 2Validation analysis - 3Validation analysis - 4Steps in complete hierarchical regression analysisComplete Hierarchical multiple regression analysis: level of measurementComplete Hierarchical multiple regression analysis: sample sizeComplete Hierarchical multiple regression analysis: assumption of normalityComplete Hierarchical multiple regression analysis: assumption of linearityComplete Hierarchical multiple regression analysis: assumption of homogeneity of varianceComplete Hierarchical multiple regression analysis: detecting outliersComplete Hierarchical multiple regression analysis: picking regression model for interpretationComplete Hierarchical multiple regression analysis: assumption of independence of errorsComplete Hierarchical multiple regression analysis: multicollinearityComplete Hierarchical multiple regression analysis: overall relationshipComplete Hierarchical multiple regression analysis: individual relationshipsSlide 88Complete Hierarchical multiple regression analysis: validation analysis - 1Complete Hierarchical multiple regression analysis: validation analysis - 2Homework Problems Multiple Regression – Hierarchical Problems - 1Homework Problems Multiple Regression – Hierarchical Problems - 2Homework Problems Multiple Regression – Hierarchical Problems - 3Homework Problems Multiple Regression – Hierarchical Problems - 4Homework Problems Multiple Regression – Hierarchical Problems - 5Homework Problems Multiple Regression – Hierarchical Problems - 6Homework Problems Multiple Regression – Hierarchical Problems - 7Homework Problems Multiple Regression – Hierarchical Problems - 8Homework Problems Multiple Regression – Hierarchical Problems - 9Slide 100Homework Problems Multiple Regression – Hierarchical Problems - 10Homework Problems Multiple Regression – Hierarchical Problems - 11Homework Problems Multiple Regression – Hierarchical Problems - 12SW388R7Data Analysis & Computers IISlide 1Hierarchical Multiple RegressionDifferences between hierarchical and standard multiple regressionSample problemSteps in hierarchical multiple regressionHomework ProblemsSW388R7Data Analysis & Computers IISlide 2Differences between standard and hierarchical multiple regressionStandard multiple regression is used to evaluate the relationship between a set of independent variables and a dependent variable.Hierarchical regression is used to evaluate the relationship between a set of independent variables and the dependent variable, controlling for or taking into account the impact of a different set of independent variables on the dependent variable.For example, a research hypothesis might state that there are differences between the average salary for male employees and female employees, even after we take into account differences between education levels and prior work experience.In hierarchical regression, the independent variables are entered into the analysis in a sequence of blocks, or groups that may contain one or more variables. In the example above, education and work experience would be entered in the first block and sex would be entered in the second block.SW388R7Data Analysis & Computers IISlide