The data file contains 6 variables. The dependent variable is reading comprehension (reading), the independent variables are phoneme awareness (phoneme), visual perception (visual), morpheme awareness (morpheme), gender (1-female, 2-male) and LS (Learning Style:1-visual, 2-auditory, 3-kinesthetic). Use SPSS for this project:
1. Create dummy codes for categorical predictor variables. This will involve creating two new dichotomous variables from the LS variable: 1)LS_visual (where "1" indicates a visual learning style and "0" indicates not a visual learning style), and 2)LS_auditory (where "1" indicates an auditory learning style and "0" indicates not an auditory learning style). Provide a frequency table for the dummy coded variables.
2. Check the assumptions of normality, homoscedasticity, and collinearity. Describe and provide support regarding whether the assumptions were met (Include supporting tables/graphs).
3. Run multiple regression using three different methods (forced entry, stepwise, and hierarchical analysis). For the hierarchical analysis, consider the following variables first: gender, LS_visual and LS_auditory. Describe, compare, and interpret the results (Include supporting tables). Which method would you pick for this analysis? Why?© BrainMass Inc. brainmass.com October 25, 2018, 8:36 am ad1c9bdddf
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This solution is comprised of a detailed explanation of the use SPSS to create new variables and modifying existing variables in the dataset. The multiple regression model is fitted after modifying the variables. A detailed explanation is provided to check the assumptions of normality, homoscedasticity, and collinearity.
Computing regression analysis in SPSS
In a study with three treatment groups, A1, A2 and A3, and a control group, C, dummy vectors were constructed as follows: subjects in A1 were identified in D1, those in A2 were identified in D2, and those in A3 were identified in D3. A multiple regression analysis was done in which the dependent variable was regressed on the three coded vectors. The following regression equation was obtained:
y = 8 + 6D1 + 5D2 -2D3
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