Political scientists use correlation analysis to better understand and measure multidimensional concepts. Consider, for example, why people prefer one political candidate to another. Agreement on issues is one factor, but we also prefer candidates with the right temperament, that we can relate to, and that we can trust. When it comes to measuring a multidimensional concept like this, you want to identify distinct factors and not use measures that are too strongly correlated. Examine the dimensions used to assess candidates in the 1960 Presidential Debate Experiment.

The debate dataset includes four dimensions on which subjects evaluated Kennedy and Nixon: perceived leadership, effectiveness, integrity, empathy, and issue agreement. Are these distinct factors or are they overlapping? To answer this question, apply the correlate C function to the variables named issues, integrity, leadership, and empathy in the debate dataset and complete the below correlation matrix.

	Issues	Integrity	Leadership	Empathy
Issues	1.000
Integrity		1.000
Leadership			1.000
Empathy	0.512			1.000