7/27/2019 Bridging the Gap Between the Languages of Theory and Research (Blalock H., 1964)

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Bridging The Gap Between The Languages of Theory And Research

Hubert M. Blalock, Jr.1

1 Owing to the inherent nature of the scientific method, there is a gap between the languages of

theory and research. Causal inferences belong on the theoretical level, whereas actual research

can only establish covariations and temporal sequences.2 As a result, we can never actually demonstrate causal laws empirically. This is true even where

experimentation is possible. Causal laws are working assumptions of the scientist, involving

hypothetical statements of the if-then variety.

3 One admits that causal thinking belongs completely on the theoretical level and that causal laws

can never be demonstrated empirically. But this does not mean that it is not helpful to think caus-

ally and to develop causal models that have implications that are indirectly testable. In working

with these models it will be necessary to make use of a whole series of untestable simplifying

assumptions, so that even when a given model yields correct empirical predictions, this does not

mean that its correctness can be demonstrated.

4 Reality, or at least our perception of reality, admittedly consists of ongoing processes. No twoevents are ever exactly repeated, nor does any object or organism remain precisely the same from

one moment to the next.2 And yet, if we are ever to understand the nature of the real world, we

must act and think as though events are repeated and as if objects do have properties that remain

constant for some period of time, however short. Unless we permit ourselves to make such simple

types of assumptions, we shall never be able to generalize beyond the simple and unique event.

5 The point we are emphasizing is that no matter how elaborate the design, certain simplifying

assumptions must always be made. In particular, we must at some point assume that the effects of

confounding factors are negligible. Randomization helps to rule out some of such variables, but

the plausibility of this particular kind of simplifying assumption is always a question of degree.

We wish to underscore this fact in order to stress the underlying similarity between the logic of

making causal inferences on the basis of experimental and nonexperimental designs.

1. Reprinted with permission from Hubert M. Blalock, Jr., Causal Inferences in Non-experimental Research(Chapel Hill: University of North Carolina Press, 1964), 172-73, 6-7, 26.

2. This particular point is emphasized in Karl Pearsons classic, The Grammar of Science (New York: Meridian,

1957), chap. 5.