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Making It Count: The Improvement of Social Research and Theory

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Title: Making It Count: The Improvement of Social Research and Theory
by Stanley Lieberson
ISBN: 0-520-06037-7
Publisher: University of California Press
Pub. Date: June, 1987
Format: Paperback
Volumes: 1
List Price(USD): $21.95
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Average Customer Rating: 3 (1 review)

Customer Reviews

Rating: 3
Summary: dense but interesting
Comment: Lieberson has some great things to say, but he will lose many readers because of his turgid phrasing.

Lieberson's main thesis is that quantitative social research (e.g. sociological studies of racial differences) makes unwarrented conclusions from multiple regression of observed social data. Lieberson criticizes the current researchers because they ignore factors like selectivity, assymetrical causal relationships (if you remove a bullet from someone's chest, it doesn't bring them back to life!) and time lags in social responses.* Lieberson does a good job of showing the validity of his points and of showing how they are often ignored by social scientists who are overly wedded to their statistical computer programs. His comments would be a little more strongly stated if he went on and showed some example of policy missteps based on flaws that he documents. (But I can understand his not wanting to step into a political firefight.)

The book is written in a very academic manner; he refers to himself in third person, overuses nominalizations, etc. I struggled to keep my brain engaged as I read through some of his examples and followed the complicated logic. He could have improved here by using simpler words, using illustrative diagrams, by clearly identifying sidebar discussions and by using summary syntheses. (The Bell Curve is a model of how to do this well--granted Lieberson is writing for a more academic audience, but still some sympathy for the reader might induce Stan to reduce the overly dry "academic tone".)

A couple of methodological questions/issues:

1. When Lieberson describes the dangers of controlling for variables when selectivity is present, he ought to note that this "control danger" exists any time there is a significant unknown variable skewing the data (selectivity or just an "X3" factor. Agreeably selectivity is more likely to be prevelant in social research (and ignored), but in his long, torturous math explanation (show it on a number line, Stan!), he should cite the similar problem of a hidden X3 or a hidden selectivity.

2. Lieberson never clearly addresses what natural scientists or engineers (those who use regression and DOE and read books like Box, Hunter, Hunter) would call an interaction effect:

For instance, Y is a possible function of A, B, C... After testing for each factor individually, you will want to test for the effect of A*B. (above and beyond individual A, B effects. Many of the difficult results that Lieberson describes in social research do not need an "X28" to explain them but can be explained by interaction effects.

3. I wonder if sociologists like Lieberson could learn something from looking at more applied forms of social research (election predictions, actuarial research). Actuarial work for insurance work is a great example to prove Leiberson's point about causal factors acting with a set of random results. And the economic incentive to be "right" may serve to overcome some of the pitfalls that Lieberson documents. I also wonder what business researchers and other practical creatures should "take away" from Lieberson.

* A nice physical analogy for assymetricity is magnetic hysterisis. Technical folks will often talk of a "hysterisis effect" when describing social issues that have assymetic response.

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