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M5 Assignment 1 Discussion
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Assignment 1: SPSS Multiple Regression Analysis
Multiple regression is an extension of bivariate correlation. However, it allows for the consideration of several predictor variables and their interrelationships as they best fit linear models for predicting the one dependent/criterion variable. As you will note, Statistical Package for the Social Sciences (SPSS) allows for several methods of entering predictor variables into the analysis and additional ways to evaluate assumptions (e.g., collinearity), residuals, and the significance of results. Results can be used in applications such as path analyses for causal modeling.
Use the Discussion Area to ask for help in completing the tasks from your classmates and the facilitator; likewise, offer your suggestions to those asking for help. Participating in this community of scholars will help you clarify processes, solve problems, and gain the immediate reinforcement you need to quickly solidify gains that you’re making in working with multiple variables and advanced statistics.
Open the SPSS data file created in M3: Assignment 1. Conduct a multiple regression and prepare a report on the results of the following tasks:
Identify appropriate variables to serve as three predictor (independent) variables (you may create dummy variables from categorical variables, such as Gender, if you wish, and use continuous variables) and one criterion (dependent and continous) variable for a multiple regression analysis.
State a research question (or questions) that could be studied using your selected variables.
Report the results of your prescreening of data regarding the following:
Missing data and outliers: Calculate the Mahalanobis distance.
Evaluation of linearity: You may include supporting scatter plots in your report’s Appendix.
Evaluation of univariate normality: You may include supporting histograms in your report’s Appendix.
Report the results of your multiple regression analysis, one using standard multiple regression and the other using stepwise/hierarchical regression. Include your evaluations of multivariate normality, collinearity diagnostics, and homoscedasticity as part of the regression analysis.
Report descriptive statistics and present each analysis model summary, analysis of variance (ANOVA) table, and coefficients table.
Use charts/plots to evaluate assumptions.
Summarize the results of each multiple regression analysis.
Save the SPSS file as R7034_M5_A1_LastName_FirstInitial.sav.
Create a report using the bulleted format given in Microsoft Word. Add an Appendix for scatter plots and histograms. Name your file R7034_M5_A1_LastName_FirstInitial.doc. Submit your response to the Discussion Area by the due date assigned.
All written assignments and responses should follow APA rules for attributing sources.
Assignment 1 Grading CriteriaMaximum PointsSelected two predictor variables and one criterion variable for accurate multiple regression analysis.4Stated a research question that would fit the type of design being evaluated.8Computed and reported the results of prescreens for missing data, outliers, linearity, and univariate normality.12Conducted both types of multiple regression analyses and evaluated multivariate normality, collinearity diagnostics, and homoscedasticity as part of the regression analysis.24Presented an output for descriptive statistics, model summary, ANOVA table, and coefficients table correctly.16Used charts/plots to evaluate assumptions.4Presented complete, informative narrative summary of the two multiple regression analyses.12Participated actively in the Discussion Area by asking for or providing clarification of a response, addressing gaps, offering suggestions, and asking for help, as needed.8Wrote in a clear, concise, and organized manner; demonstrated ethical scholarship in accurate representation and attribution of sources, displayed accurate spelling, grammar, and punctuation.4Total:92Rubrics 

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