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Russel-Research-Method-in-Anthropology

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Multivariate Analysis 675<br />

BTU and OVER65 show effects on suicide rates that are <strong>in</strong>dependent of the<br />

effects of INCOME.<br />

But look what happens when we enter all three <strong>in</strong>dependent variables <strong>in</strong>to<br />

the regression. Table 21.21 shows the result: INCOME rema<strong>in</strong>s statistically<br />

significant, but neither BTU nor OVER65 are.<br />

TABLE 21.21<br />

Regression of All Three Independent Variables and SUICIDE<br />

Dep Var: SUICIDE N: 51 Multiple R: 0.630 Squared multiple R: 0.397<br />

Adjusted squared multiple R: 0.358 Standard error of estimate: 2.889<br />

Effect Coefficient Std Error Std Coef Tolerance t P (2 Tail)<br />

CONSTANT 26.898 4.806 0.000 . 5.597 0.000<br />

INCOME 0.000 0.000 0.434 0.889 3.613 0.001<br />

BTU 0.004 0.003 0.183 0.714 1.362 0.180<br />

OVER65 0.439 0.228 0.245 0.793 1.927 0.060<br />

If BTU rema<strong>in</strong>ed statistically significant while OVER65 did not, we could<br />

conclude that OVER65 had no effect on suicide <strong>in</strong>dependently of INCOME<br />

and BTU. Likewise, if OVER65 rema<strong>in</strong>ed statistically significant while BTU<br />

did not, we could conclude that BTU had no effect on suicide <strong>in</strong>dependently<br />

of INCOME and OVER65. In this <strong>in</strong>stance, however, we can only conclude<br />

that we have a multicoll<strong>in</strong>earity problem that obscures our f<strong>in</strong>d<strong>in</strong>gs. We can’t<br />

tell if either BTU or OVER65 has effects on suicide <strong>in</strong>dependently of<br />

INCOME. (In fact, the condition <strong>in</strong>dex for this regression is 27, which <strong>in</strong>dicates<br />

strong to severe multicoll<strong>in</strong>earity.)<br />

Factor Analysis<br />

Factor analysis is based on the simple and compell<strong>in</strong>g idea that if th<strong>in</strong>gs we<br />

observe are correlated with each other, they must have some underly<strong>in</strong>g variable<br />

<strong>in</strong> common. Factor analysis refers to a set of techniques for identify<strong>in</strong>g<br />

and <strong>in</strong>terpret<strong>in</strong>g those underly<strong>in</strong>g variables.<br />

For example, people <strong>in</strong> the United States who are <strong>in</strong> favor of gun control<br />

are likely (but not guaranteed) to be <strong>in</strong> favor of: (1) a woman’s right to an<br />

abortion; (2) participation by the U.S. military <strong>in</strong> overseas peacekeep<strong>in</strong>g missions<br />

of the United Nations; and (3) affirmative action <strong>in</strong> college admissions.<br />

People who are aga<strong>in</strong>st gun control are likely to favor: (1) restrictions on abortion;<br />

(2) less <strong>in</strong>volvement of the United States <strong>in</strong> UN peacekeep<strong>in</strong>g missions;<br />

and (3) curbs on affirmative action.

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