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I am a doctoral student in a statistics course that requires use of SPSS to analyze data and write a

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Linear Regression and Multilevel Modeling Assignment

The concepts for hierarchical regression and multilevel modeling or hierarchical linear modeling and the practice of these applications in SPSS are highlighted for the week. After reading about the concepts for both Hierarchical Linear regression and Multilevel Modeling and completing the practice exercises using SPSS for both techniques.

Download the Data File named Victoria and load it into SPSS.

Download the Data File named Dealership and load it into SPSS as a separate practice set.

Complete the practice exercises in Chapter 8B of the Meyers, Gamst, and Guarino (2013) text using SPSS for the first practice set for hierarchical regression using the Victoria dataset.

Complete the practice exercises in Chapter 9B of the Meyers, Gamst, and Guarino (2013) text using SPSS for the second practice set for multilevel modeling using the Dealership dataset.

Write a 1,050- to 1,400-word paper that includes the following:

o Assess the major concepts and components for hierarchical linear regression and multilevel modeling.

o Replicate the major tables for hierarchical regression and after each of the primary tables explain (from your own understanding) the primary results that are shown in certain columns that lead to the interpretation of test results and the decisions to reject or failure to reject (i.e., retain or accept) the null hypotheses. Evaluate the strengths and weaknesses or limitations of the model.

o Replicate the major tables for multilevel modeling and after each of the primary tables explain (from your own understanding) the primary results that are shown in certain table columns that lead to the interpretation of the test results and the decision to reject or failure to reject (i.e., retain or accept) the null hypotheses. Evaluate the strengths and weaknesses or limitations of the model.

o Draw conclusions about how hierarchical analysis is related to the multilevel analysis and the interaction effects of variables in both the models.

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