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Social
Indicators Research
There was one more sociological journal to
which Hickey had sent his paper, which cannot be
treated as the others discussed above, because the
editor refused to inform Hickey of the scientific
criticisms given as reasons for rejection. This
journal is Social
Indicators Research edited by a Mr. Alex C.
Michalos. Michalos
is identified on the journal's stationery as
Director of the Social Indicators Research Programme
at the University of Geulph in Ontario, Canada, and
the publisher is identified as D. Reidel Publishing
Company, Dordrecht, Holland, and Boston, U.S.A.
Michalos acknowledged his receipt of Hickey's
manuscript in a letter dated 19 January 1982.
In a letter to Hickey dated 4 February 1982
Michalos said that he had received a very
unfavorable review of the manuscript and would not
be able to publish it. He added that usually he has specific comments from a
reviewer to send to authors, but that in this case
the reviewer pretty well threw up his hands.
In response to a letter from Hickey dated 12
February demanding two written referee comments,
Michalos wrote a letter to Hickey dated 22 February
1982 replying that sometimes his reviewers are
brutal, and that when the first reviewer is
exceptionally critical, as in the case of Hickey's
manuscript, he does not go to a second reviewer.
He concluded by saying that he had sent
Hickey all he had.
In Hickey's view no critic is above
criticism, and he wonders what enabled this single
referee to exercise such a controlling influence
over this editor. The most recent edition of the National
Faculty Directory lists Michalos as a faculty
member of the Department of Philosophy at the
University of Guelph.
Having a professional philosopher as editor
of a sociological journal might have been a
singularly fortunate circumstance both for Social
Indicators Research
and for academic sociology.
Instead what Hickey actually encountered is
an editorial practice that resembles the comparably
absurd judicial practice portrayed in Franz Kafka's The Trial, in which the accused is arrested, tried, condemned and
executed without ever having been informed of the
charges brought against him.
Hickey has no idea what Michalos actually
teaches his philosophy students in the Department of
Philosophy at the University of Geulph.
But Hickey believes that both the students in
Michalos’ philosophy classroom and readers of his
journal would be much better served, were Michalos
to accept the decidedly non-Kafkaesque contemporary
Pragmatist philosophy of science, and both teach
contemporary philosophy of science in his classroom
and implement it in his editorial decisions.
Conclusion
Hickey’s submission to the four sociology
journals was not intentionally a Trojan horse.
But for all editors working for journals
serving pseudoscientific professions like American
academic sociology, this author offers a paraphrase
of Homer’s rendering of the advice belatedly
issued by the unfortunate Trojans: “Beware of
philosophers bearing contributions.”
Perhaps some day an indignant and principled
academic sociologist will be inspired to establish a
sociology journal of rejected papers, which would
publish not only the papers rejected by the orthodox
sociological journals but also the shrill and
strident reasons for rejection sent to the rejected
authors together with the author’s replies.
Science after all is inherently public, and
the reasons for rejection written by referees are
attempts at scientific criticism, even if they are
disreputably incompetent attempts.
Such a practice of expose would introduce a
badly needed sense of responsibility into the
published literature of this backwater academic
occupation by making the editors publicly answerable
for the incompetent decisions they would otherwise
be able to hide like incompetent physicians who
believe they can bury their fatal mistakes.
One beneficial outcome would be a high
turnover of the incompetents - firstly the referees
and then eventually the editors who selected these
referees and accepted their opinions.
But the greatest threat to the sociology
journals’ attempted suppression of information is
the Internet. The journals’ gate guards can no
longer protect the careers of ensconced academicians
from new ideas.
The Internet will have the same effect on
sociology’s publication censorship that it has had
on tyrants’ political censorship. Now contributors
can circumvent the obstructionism.
Disingenuous lip service to academic freedom
will be replaced by the Internet’s effective and
unrestricted freedom to distribute and access new
ideas and contributions.
American academic sociology has a long long
long way to travel before it can honestly claim to
have evolved into a modern empirical science, but
the information highway may speed this eventual
development.
The “Last Sociologist”
In
March 2001 Lawrence Summers, formerly Treasury
Secretary under President Clinton and a Harvard
Ph.D. graduate in economics who received tenure at
Harvard at the remarkably young age of twenty-eight
years, was appointed Harvard University’s
twenty-seventh president.
His has not been a caretaker administration;
in his first year his changes occasioned no little
controversy. In
“Roiling His Faculty, New Harvard President
Reroutes Tenure Track” the Wall
Street Journal (11 Jan. 2002) reported that
Summers has attempted to make tenure accessible to
younger faculty members and to avoid extinct
volcanoes, those graybeard professors who receive
tenure due to past accomplishments, but whose
productive years are behind them.
The threatening implications of Summers’
administrative changes for Harvard’s social
science departments including sociology have not
been overlooked.
One critical faculty member is quoted by the Wall
Street Journal as saying that a prejudice for
younger over older candidates amounts to a prejudice
for mathematical and statistical approaches - such
as those reflected by Summers’ own area of
economics - over historical or philosophical
approaches. Thus
it appears that American academic social scientists
are finally - in the twenty-first century - being
dragged out of their murky misty Romanticism albeit
kicking and screaming, but not without rear-guard
resistance.
Another
example of such resistance is a New
York Times OP-ED-page article (19 May 2002)
titled “The Last Sociologist” by Harvard
sociology professor Orlando Patterson.
Essentially Patterson’s article is a
defense of the Romantic dualism between the natural
and social sciences with its doctrine that sociology
is the interpretative understanding of culture.
He complains that in their anxiety to achieve
the status of economics, contemporary sociologists
have adopted a style of scholarship that mimics the
methodology and language of the natural sciences,
which he describes as a style that focuses on
building models, formulating laws, and testing
hypotheses based on data generated by measurement.
He claims that for most areas of social life
- especially those areas in which the general public
is interested - the methods of natural science are
not only inappropriate but are also distorting.
Patterson illustrates the kind of scholarship
he approves for sociology by referencing such books
as The Lonely
Crowd by David Riesman, Patterson’s mentor
whom he describes as discarded and forgotten by his
discipline of sociology, and The Sociology of Everyday Life by Erving Goffman, a Reisman
contemporary. Patterson
writes that these authors followed in an earlier
tradition, and that their style of sociology was
driven firstly by the significance of the subject
and secondly by an epistemological emphasis on
understanding the nature and meaning of social
behavior. He
says that this understanding is of a type that can
only emerge from the interplay of the author’s own
views with those of the people being studied.
Patterson laments that today sociologists
eschew any explanation of human values, meanings,
and beliefs due to ambiguities and judgment.
He says that sociologists writing today about
culture disdain as reactionary any attempt to
demonstrate how culture explains behavior, while
their models emphasize the organizational aspects of
culture, with the result that little or nothing is
learned from sociology about literature, art, music,
or religion even by those who purport to study these
areas.
Patterson’s
complaints notwithstanding sociology is becoming an
empirical social science capable of making
predictions with quantitative models like the
econometric models of empirical economics.
Society needs and wants an empirical science
of society that enables forecasting and policy, and
this achievement requires subordinating the Romantic
mentalistic criteria to the Pragmatic empirical
criteria. American
academic sociology might finally graduate to the
status of an empirical science were Patterson
actually the last Romantic sociologist.
But Patterson’s OP-ED comments
notwithstanding he is not the “last sociologist”
meaning the last Romantic sociologist of culture.
American academic sociology has a long, long,
long road ahead of it before it graduates to the
status of a modern empirical science.
Examination of recently published articles in
the four journals, to which Hickey had sent his
macro-sociometric model twenty-five years ago,
reveals that editors and referees are still
Romantics. There
now appear a few statistical models, but the authors
are still required to supplement their statistical
models with descriptions of motivations that supply
the required understanding, so they “make
sense.”
Nonetheless
changes at Harvard are in progress thanks in no
small part to inexorable attrition.
The Wall Street Journal article reported that Summers’ hiring policies
have the support of Harvard’s governing board, and
that hiring is an area that could prove to be his
most enduring legacy.
And given that Harvard is the cradle of both
the classical and contemporary variations of
Pragmatism, Summers’ influence augers well for
academic sociology at Harvard.
Then eventually the professors and
practitioners of sociology, the science of
conformism, will follow Harvard’s lead, just as
they did when Parsons was the Pied Piper from
Harvard.
American academic sociology is still a
philosophically retrograde prescientific academic
profession. But
happily not every American academic sociologist is a
philosophical simian that drags his knuckles as he
walks. Immediately
below the reader will find a description of a
computerized artificial-intelligence discovery
system developed by an atypically avant
garde sociologist, John A. Sonquist, who not
surprisingly has never had any paper appear in any
academic sociology journal. Read on.
Sonquist
on Simulating the Research Analyst with AID
John A. Sonquist (1931-
) received a doctorate in sociology from the
University of Chicago, and is at this writing a
professor of sociology and the Director of the
Sociology Computing Facility at the University of
California at Santa Barbara, California.
He was previously on the faculty at the
University of Michigan at Ann Arbor, and was
Research Associate and Head of the Computer Services
Facility for the University's Institute for Social
Research. He is also a past chairman of the Association for Computing
Machinery. For
his Ph.D. dissertation written in 1963 at the
University of Chicago he developed a computerized
discovery system called the AID
system. "AID"
is an acronym for "Automated Interaction
Detector" system.
Today description of the AID
system can be found in many marketing research
textbooks in chapters discussing data analysis
techniques for hypothesis development.
The system is also used extensively by
lending institutions for risk analysis.
The AID
system performs a type of statistical analysis often
called “segmentation modeling”, and in
Sonquist’s system, which is described in his Multivariate
Model Building (1970) and elsewhere, the
analysis uses a well known statistical segmentation
method called “one-way analysis of variance.”
A variation on AID
has been developed by Jay Magidson of Statistical
Innovations, Inc., Belmont, MA, which is based on
the equally well known segmentation method called
“chi-squared analysis.”
The system is called CHAID
(Chi-squared Automatic Interaction Detector), and is
now commercially available in the SPSS
computer statistical software package.
And a version also exists in the SAS system
called SY-CHAID.
In the "Preface" to his 1970 book
Sonquist says that his interest in such a system
started with a conversation with Professor James
Morgan, in which the question was asked whether a
computer could ever replace the research analyst
himself, as well as replacing many of his
statistical clerks.
He writes that they discarded as irrelevant
the issue of whether or not a computer can
"think", and instead explored the question
of whether or not the computer might simply be
programmed to make some of the decisions ordinarily
made by the scientist in the course of handling a
typical analysis problem, as well as doing the
computations. Developing
such a computer program required firstly examining
the research analyst's decision points, his
alternative courses of action, and his logic for
choosing one rather than the other course, and then
secondly formalizing the decision-making procedure
and programming it but with the capacity to handle
many variables instead of only a few.
An early statement of this idea was published
in Sonquist’s "Simulating the Research
Analyst" in Social
Science Information (1967).
In this earlier work Sonquist distinguishes
three kinds of computer applications in social
science: data processing, simulation, and
information retrieval.
He observes that data processing systems and
many information retrieval systems are nothing but
an extension of the analyst's pencil and lack really
complex logical capabilities.
But he adds that there also exist information
retrieval systems which are much more sophisticated,
because simulating the human being retrieving
information is one of the objectives of the system
designer. These
sophisticated retrieval applications combine both a
considerable data processing capability and a logic
for problem solving, such that the whole system is
oriented toward the solution of a specific class of
problems without human intervention in a long chain
of decisions.
Sonquist then argues that such a combination
of capabilities need not be limited to information
retrieval, and that major benefits can be gained
from the construction of a new type of simulation
program, one in which the phenomenon simulated is
the research analyst attempting to "make
sense" out of his data.
The phrase "make sense", which is a
characteristic locution of the verstehen
Romantics, is placed in quotation marks by Sonquist,
and there is no evidence that he is advocating the verstehen
philosophy of scientific criticism.
In fact on the verstehen
view a computer cannot "make sense" of
social data, because it is not human and therefore
cannot empathize with the human social actors.
He says instead that an important function of
the research analyst in the social sciences is the
construction of models which fit the observed data
at least reasonably well, and that this approach to
the analysis of data can be likened to curve fitting
rather than to the testing of clearly stated
hypotheses deduced from precise mathematical
formulations. He
offers his own AID
system as an example of a system that simulates
the research analyst.
Sonquist and Morgan initially published their
idea in their "Problems in the Analysis of
Survey Data, and a Proposal" in Journal
of the American Statistical Association (June,
1963). The authors examine a number of problems in survey research
analysis of the joint effects of explanatory factors
on a dependent variable, and they maintain that
reasonably adequate techniques have been developed
for handling most of them except the problem of
interaction. Interaction
is the existence of an intercorrelating influence
among two or more variables that explain a dependent
variable, such that the effects on the dependent
variable are not independent and additive.
This is contrary to the situation that is
assumed by the use of other multivariate techniques,
such as multiple classification analysis and
multiple linear regression.
In multiple regression each variable
associated with an estimated coefficient is assumed
to be independent, so that the effects of each
variable on the dependent variable can be treated as
additive. In
"Finding Variables That Work" in Public
Opinion Quarterly (Spring, 1969) Sonquist notes
that it is possible to represent interaction among
explanatory variables in a regression, if the
interacting variables are combined multiplicatively
prior to statistical estimation.
But there still remains the prior problem of
discovering the interacting variables.
A triangular correlation matrix of a factor
analysis can do this.
Another is the AID discovery system, which may be used in conjunction with such
techniques as regression or multiple classification,
in order to detect and identify interaction effects
and to assist equation specification for regression.
The AID system also resembles an earlier statistical technique called
“cluster analysis”, because it too combines and
segments the observations into groups.
But the AID system differs in that it is a segmentation analysis procedure
that uses a dependent variable as a criterion for
forming the segments, and therefore the segments are
derived to predict a dependent variable.
Furthermore clusters generally are not
defined as explicit functions of the predictors, and
so cannot easily be used to classify a new sample
into clusters.
In The
Detection of Interaction Effects: A Report on a
Computer Program for the Optimal Combinations of
Explanatory Variables (1964, 1970) and in Searching
for Structure: An Approach to Analysis of
Substantial Bodies of MicroData and Documentation
for a Computer Program (1971, 1973) Sonquist and
Morgan describe their algorithm, as it is
implemented in the AID
computer program used at the University of
Michigan, Survey Research Center. The program answers the question: what dichotomous split on
which single predictor variable will render the
maximum improvement in the ability to predict values
of the dependent variable.
The program divides a sample of at least one
thousand observations through a series of binary
splits into a mutually exclusive series of
subgroups. Each
observation is a member of exactly one of these
subgroups. The
subgroups are chosen so that at each step in the
procedure, the arithmetic means of each subgroup
account for more of the total sum of squares (i.e.
reduce the predictive error more) than the means of
any other pair of subgroups.
This is achieved by maximizing a statistic
called the “between-group sum of squares.”
The procedure is iterative and terminates
when further splitting into subgroups is
unproductive.
The authors illustrate the algorithm with a
tree diagram displaying the binary splits for an
analysis of income using data categories
representing age, race, education, occupation, and
length in present job.
When the total sample is examined, the
minimum reduction in the unexplained sum of squares
is obtained by splitting the sample into two new
groups: persons under sixty-five years of age and
persons aged sixty-five and over. Both of these groups may contain some nonwhites and varying
degrees of education and occupation groups.
The group that is sixty-five and over is not
further divided, because control parameters in the
system detect that the number of members in the
group is too small.
It is therefore a final grouping.
The other group is further subdivided by race
into white and nonwhite persons. The nonwhite group is not further subdivided, because it is
too small, but the system further subdivides the
white group into persons with college education and
persons without college education.
Each of these latter is further subdivided.
The college-educated group is split by age
into those under forty-five years and those between
forty-six and sixty-five.
Neither of these subgroups is further
subdivided. Those
with no college are further subdivided into laborers
and nonlaborers, and the latter are still further
split by age into those under thirty five and those
between thirty six and sixty five.
The variable representing length of time in
current job is not selected, because at each step
there existed another variable which was more useful
in explaining the variance remaining in that
particular group. The predicted value of an individual's income is the mean
value of the income of the final group of which the
individual is a member.
Such in overview is the AID discovery system.
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