Making It Count: The Improvement of Social Research and Theory. by Stanley LiebersonReview by: Richard T. CampbellSocial Forces, Vol. 65, No. 3 (Mar., 1987), pp. 905-906Published by: Oxford University PressStable URL: http://www.jstor.org/stable/2578548 .

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Book Reviews / 905

ences. Thus he argues that many of those who do not fit the norms-marginals- bring to social organization, not problems, but a sense of transcendence, an ability to serve as catalysts for positive social change. In the final analysis, in toto, the works that make up Race, Ethnicity and Socioeconomic Status provide a unique in- sight into the contributions nondominant "norm-breakers" have made to this society.

Making It Count: The Improvement of Social Research and Theory. By Stanley Lieberson. University of California Press, 1985. 271 pp. $24.95.

Review: RICHARD T. CAMPBELL, University of Illinois at Chicago

The publisher's blurb for this book promises "a landmark study that will pro- foundly affect our way of thinking about empirical social research." One is tempted to ignore that kind of hyperbole, but when it refers to a book by someone with Lieberson's track record for incisive analysis, attention must be paid. So what do we have? Certainly less than promised (who could deliver?), but there are some useful and provocative ideas, examples, and arguments. Unfortffnately, the book is not as coherent as it might be.

Lieberson argues that sociologists have gotten into deep trouble by blindly following the experimental model of the physical sciences. This theme is meant to serve as the organizing principle for the book, but it doesn't work. Lieberson never defines what he means by "the analogy with natural science experiments" that has betrayed us. It is probably a good thing, too, because if he did he would find (as he later suggests) that there is no such thing as the natural science model. By failing to provide a coherent basis for its underlying theme, the book becomes simply a series of relatively unrelated chapters.

Lieberson deals with three broad classes of issues, two clearly stated and a third presented somewhat less precisely. The first and most effectively argued position is that most cross-sectional analysis is of questionable value. Granted, but that is hardly fresh criticism. Lieberson devotes a great deal of space to dis- tinguishing between "symmetrical" and "asymmetrical" causation, by which he means effects that are reversible versus those that are not. An asymmetric effect occurs when increases in X lead to increases in Y, but Y does not decrease when X decreases. This kind of "ratchet effect" (to use Tuma and Hannon's term) may well be common, but the idea is somewhat less than revolutionary.

A second broad theme has to do with model specification (although Lieber- son never uses that phrase). The most important topic under this rubric is selec- tion bias, of which some useful examples are given, although without statistical details. A second topic, discussed at great length, is the role of "control variables." What is a "control?" It is no accident that the term never appears in formal econometric discussions of model specification because it is meaningless: a vari- able either belongs in a causal model or it does not. Lieberson takes a much looser view of what constitutes a model and pays the price. For example, he develops an elaborate example of the effects of first regressing school performance on school type and then controlling for family background (i.e., a two-equation model). But why do that? The equations represent a causal model which makes no sense.

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906 / Social Forces Volume 65:3, March 1987

Family background is not affected by school type, although the converse may be true, and in any case Lieberson's real concern is with specification (omitted vari- able) bias.

The third, most interesting, and least clearly articulated theme has to do with level of theoretical generality. Distinguishing between "basic" and "superfi- cial" causes, Lieberson argues that some apparently distinct causal effects may in fact be alternate manifestations of more general principles. To construct my own example: Why does family background affect income attainment? To show that background affects education, which in turn affects income, does not answer the question "why"; it tells us "how." And the way in which background works may vary over time and place, reflecting a more general principle that families will seek to pass on income advantages and will use a variety of social mechanisms to ac- complish it. Again, this argument basically has to do with model specification and again we are back to finding problems with cross-sectional or ahistorical data.

I believe that what Lieberson sees as a deficiency in the linkage of theory and method is in fact a deficiency of data. The issue is not that we slavishly imi- tate the "hard sciences," but rather that the data we have been able to collect ar- ticulate only loosely with the (extremely vague) traditional theoretical concerns of the discipline. Cross-sectional-even single-cohort single-society longitudinal- research provides essentially descriptive data (however expressed-in tables, path diagrams, or some other way) of a process. The process needs to be modeled appropriately, but the interesting question is how the parameters of that process respond (perhaps reciprocally) to social structure and historical change. To answer that question requires a far more ambitious data collection program than we have ever imagined. To a degree, as in the work of Tilly, we can reconstruct the quanti- tative past, but to a large extent we shall have to wait for history to happen. Lieberson's own highly praised A Piece of the Pie is an example of what can be done with the right kind of question and the right kind of data. Data have immense heuristic value and as multilevel, multiperiod, multisociety data accumulate (as they will) our fixation with the cross-sectional will vanish.

So who is this book for? I don't believe it can be used as a textbook in graduate methods courses, at least not without a lot of work by the instructor. It assumes too much on the part of the reader. Lieberson is basically arguing with his peers. But most of them will already know the technical points he makes, and in any case those points are made more effectively elsewhere. There are some useful things in the book, but they are scattered. A discussion of variance explained as a criterion for theory testing, for instance, argues that attempts to maximize R2 by introducing scores of variables into cross-sectional models cannot possibly lead to theoretical precision. Lieberson sees this as resulting from our blind worship of real science. I think the problem is real, but fault lies elsewhere.

This book is provocative and worth reading, but it is by no means the basis for a scientific revolution.

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