Myths of Murder and Multiple Regression
Rutgers University, Camden NJ 08102
Published in The Skeptical Inquirer, Volume 26, No 1, January/February 2002, pp. 19-23.
If you would like a longer, more technical version of this paper, in Word format,
Do you believe that every time a prisoner is executed in the United States, eight future murders are deterred?
Do you believe that a 1% increase in the number of citizens licensed to carry concealed weapons causes a
in the state's murder rate? Do you believe that 10 to 20% of the decline in crime in the 1990s
was caused by an increase in abortions in the 1970s? Or that the murder rate would have increased by 250%
since 1974 if the United States had not built so many new prisons?
If you were misled by any of these studies, you may have fallen for a pernicious form of junk science: the use
of mathematical models with no demonstrated predictive capability to draw policy conclusions. These studies
are superficially impressive. Written by reputable social scientists from prestigious institutions, they often
appear in peer reviewed scientific journals. Filled with complex statistical calculations, they give precise
numerical "facts" that can be used as debaters’ points in policy arguments. But these "facts" are will o' the
wisps. Before the ink is dry on one study, another appears with completely different "facts." Despite their
scientific appearance, these models do not meet the fundamental criterion for a useful mathematical model:
the ability to make predictions that are better than random chance.
Although economists are the leading practitioners of this arcane art, sociologists, criminologists and other
social scientists have versions of it as well. It is known by various names, including "econometric modeling,"
"structural equation modeling," and "path analysis." All of these are ways of using the correlations between
variables to make causal inferences. The problem with this, as anyone who has had a course in statistics
knows, is that correlation is not causation. Correlations between two variables are often "spurious" because
they are caused by some third variable. Econometric modelers try to overcome this problem by including all
the relevant variables in their analyses, using a statistical technique called "multiple regression." If one had
perfect measures of all the causal variables, this would work. But the data are never good enough. Repeated
efforts to use multiple regression to achieve definitive answers to public policy questions have failed.
But many social scientists are reluctant to admit failure. They have devoted years to learning and teaching
regression modeling, and they continue to use regression to make causal arguments that are not justified by
their data. I call these arguments the myths of multiple regression, and I would like to use four studies of
murder rates as examples.
Myth One: More Guns, Less Crime.