Multivariate Regression with Controls (HW4)
Due Date: Pol 101 Exam slot (Wednesday 21 March 4-7pm). Please note that this assignment must be submitted on Canvas no later that 7pm on Wednessday March 21st.
In submitting your assignment please clearly indicate which of the following options you have selected in completing HW4:
1. Extend HW3 in line with HW4 based on lectures regarding Regression Models;
2. Revise HW3 in line with feedback received;
3. Indicate that you wish to have your HW3 grade also count for HW4.
Please note that irrespective of which option above is chosen the grade assigned for HW4 will not be lower than that for HW3. Of course, in the absence of a submission for HW4 a grade of zero will be assigned.
In this assignment, you are to explore one (or more) aspect(s) of the elaboration model using a hierarchical approach to regression.
One aspect of this assignment essentially recapitulates what you have already done in HW3. Thus, you will again conduct a simple multiple regression analysis including three (or more) independent variables and a dependent variable which is preferably an index. For this aspect of the assignment you may repeat all or part of your work from previous assignments or you may select new variables.
This assignment also requires, however, that you extend your work by adding at least one but preferably two (or more) control variables using a hierarchical approach to regression. As always, you should have a clearly formulated theoretical basis for this analysis, stated in this case as a series of hypotheses. Your results for this assignment should be presented in multi-model regression tables supplemented, where appropriate, with arrow or line diagrams.
An essential aspect of this assignment is to identify a control variable (CV) from the same data set for inclusion into your analysis. In searching for this variable, please review the elements of the elaboration paradigm discussed in class. Your goal is to find a variable that you reasonably think will explain, interpret or specify the relationship between your dependent variable (DV) and one or more of your independent variables (IVs).
In planning your analysis, it may be useful to identify a ‘focal’ relationship (between a DV and a single IV) into which the CV will be introduced. This CV may, but need not, be one of the independent variables used in your previous assignments. In any case, provide the item number and full question wording. Please note that items that are anticipated to lead to replication are not appropriate for this assignment.
Your goal should be to describe some meaningful results using interpretation, explanation, specification (interaction) or suppression through regression analysis. A clear hypothesis should be the basis for whichever type of elaboration you pursue. Nevertheless, a finding of replication is acceptable.
At least one group of variables included in your analysis should consist of dummy variables derived from a variable measured at the nominal level which has three or more categories or values.
In preparing this work, recall that with replication your independent variable will remain a substantial predictor of your dependent variable after controlling for one or more theoretically relevant control variables. In contrast, with explanation or interpretation you will find that by controlling for a theoretically relevant variable (or combination of variables) your original independent variable is no longer as substantial a predictor of your dependent variable as it was originally. In such instances, be sure to consider carefully the logical order of your variables so as to distinguish between explanation and interpretation. Hypotheses regarding specification should be approached via an interaction approach. This entails adding a carefully constructed multiplicative (interaction) term into the regression equation, as well as its constituent parts.
Irrespective of your theoretical model, the results should be presented in properly formatted, presentation quality tables, including titles and labels. Also be sure to include your syntax.
As in all assignments, do not use examples provided in the lectures or labs.
Generally speaking, comments will be made only on those assignments attempting a hierarchical regression model. Otherwise, a grade will be assigned with only minimal written comments.