From: "Wendy K. Olsen" <W.K.Olsen-AT-Bradford.ac.uk>
Subject: Re: BHA: Regression paper; problem with example
Date: Thu, 24 Sep 1998 14:11:25 +0100 (GMT Daylight Time)


Dear Everyone,
This is my first message to the list although I've been reading the 
list selectively for months.  I couldn't attend the conference - too 
bad for me!

Doug Porpora, you said that the regression equation would tell you 
whether race was appearing to have an effect in addition to (or 
controlling for) economic factors.  I enclose at the bottom your 
statement as QUOTE.  But you ignore the crucial problem that the simple
regression cannot discern between the concurrent effect of 'race' and 
an 'economic' factor when and if they rise and fall together.  (In 
other words, if many persons are both of one race and have low values 
for the measured economic variable, and many persons of another 'race' 
have high values, regression will say 'race adds nothing to the 
explanation by economic factors'.) This is the problem highlighted in 
Lawson's book. 

There are two constructive responses to this problem.  One is to get 
some new data that hits more directly the possible racism of bank 
employees -- opinion data, person-wise loan approval data, or 
something. 

Another is to use the data in a way that have multiple dependent 
variables.  Although imperfect, path analysis (using two, three, or 
more regressions) may help utilise evidence in a more complex way than 
single regressions ever can do.  (I am assuming that we have 
cross-sectional, not time-series data, since I too have just about 
given up hope on econometrics -- which is almost defined as the 
statistical analysis of time series data.)    

Path analysis is described in a working paper I've been developing; it 
has its defects.  But one thing it allows you to do is distinguish 
operationalised causal mechanisms working at three or four units of 
analysis, as they affect a final outcome which is likely to be 
noticeably influenced by some of the underlying factors.  

E.g. path analysis could have variables at several levels, like:

INDIVIDUals

a)in bank		
				BANK e.g. policies
b) in household
e.g. gender, age, earnings

		HOUSEHOLDS	

		e.g. Hh Income

				REGION

				e.g. neighbourhood
				facilities 
				atmosphere
				no. of bankers' visiting etc.
				----->		LOAN REQUEST
						e.g.
						application details
						proposal risk analysis
						etc.
						--->	LOAN DECISION.


I've shown these objects moving to the right on my screen, as we move 
toward the final outcome; intermediate factors may be very important to 
the final outcome.  A major one in your example is that 'race', or 
perceptions of racism, may affect the odds of a person putting in a 
loan application. Self-selection then gives the bankers a biased 
sample.  This affects the likelihood (and we need to study the bias as 
an empirical question) of a particular 'race'(!)'s loan application 
getting approved.  

Path analysis is promising, and if anyone on the list wants to explore 
the use of CR ideas to hone path analysis for heuristic scientific 
research, get in touch.


QUOTE FROM DP:
In my talk, I offered some examples where multiple regression analyses
applied to current history have politically important implications.  
One example concerned whether American banks were red-lining -- 
refusing loans to neighborhoods on the basis of race.  The banks 
claimed they heeded only economic variables . . . We can compare the 
importance of neighborhood racial characteristics with neighborhood 
economic characteristics, each controlling for the other. If we see 
that in the  recent past, race made an important difference 
even controlling for economic factors, that undermines the banks' 
claims that they were not redlining.

Does the regression equation applied to this situation represent a
universal law?  No. Does it tell us anything about what will be in 
the future?  Hopefully not.  If the regression equation had its 
intended political effect, then a subsequent regression equation ought 
to yield very different results.
On Thu, 24 Sep 1998 00:17:19 -0400 Doug Porpora 
<porporad-AT-duvm.ocs.drexel.edu> wrote:

> Hi Everyone,
> 
> Tony, I've given some thought to the question you posed to me.  
First, you > asked me to clarify which of the following positions i am 
defending: > 
> >1) regression analysis does not require a closure for the 
"parameter > >estimates", etc., to be meaningful/interpretable.
> > > >2) regression analysis only requires a closure within the sample
> >period or domain. > 
> You further ask, > 
> >If 1) I would be interested in how this conclusion is reached; if > 
>2) I would be interested in wether you think this will get us far in > 
>social world. > 
> These questions are wonderful because they are far more difficult 
than they > appear.  I probably need more time to think them through, 
but since I only > asked for a day, here is what I have come up with. I 
think in a way I want > to defend both (1) and (2).  I think that in 
some important disputes, we > have the closure you describe in (2) and 
that this is all we need.  But I > also think that closure is not even 
necessary for regression to be > applicable -- the claim you label (1).
> > Let me deal with (2) first.  I think regression is often one piece 
of > evidence that can be adduced to resolve debate about a given
> sociohistorical context.  I stopped myself from saying something in 
my talk > that I am not sure I am ready to stand by, but let me float 
it here: > 
> In one sense, the past is a closed system: Those mechanisms that 
operated > operated and no others did or ever will in the past.  (I am 
assuming that a > closure can encompass more than one mechanism as long 
as the operative > mechanisms are isolated from all others.)
> > If the past is a closed system, then if we want to know which 
mechanisms > actually operated in a given sociohistorical context and 
if we want to know > their relative importance, then regression -- and 

particularly multiple > regression -- offers one valid piece of 
evidence. > 
> In my talk, I offered some examples where multiple regression 
analyses > applied to current history have politically important 
implications.  One > example concerned whether American banks were 
red-lining -- refusing loans > to neighborhoods on the basis of race.  
The banks claimed they heeded only > economic variables such as a 
neighborhood's median income or housing value. > Well, if we look at 
the past, what's done has been done, and a multiple > regression 
equation can tell us the relative importance of the different > factors 
involved. > 
> We can compare the importance of neighborhood racial characteristics 
with > neighborhood economic characteristics, each controlling for the 
other. If > we see that in the  recent past, race made an important 
difference even > controlling for economic factors, that undermines the 
banks' claims that > they were not redlining.
> > Does the regression equation applied to this situation represent a
> universal law?  No. Does it tell us anything about what will be in 
the > future?  Hopefully not.  If the regression equation had its 
intended > political effect, then a subsequent regression equation 
ought to yield very > different results.
> > Another example I cited was from the work of Christopher Chase-Dunn 
and > Denny Braun, who both use multiple regression on the recent past 
to show > that multinational penetration of third world countries has 
deleterious > effects on development and inequality, controlling for 
rival explanations. > Again, the effort here is to determine what has 
been the case so far in a > very specific historical context.  The 
regression equations are not > themselves the explanation for what 
happened but only evidence for an > explanation.  They are certainly 
not meant to be taken as universal laws, > applicable anywhere, 
anytime. > 
> Arguably, if the past can be treated as a closed system, then 
regression > equations applied to the past are premised on closed 
systems. I think that > the use of regression in cases like this does 
address some politically > important issues and in that sense does get 
us as far as anything will. > 
> Do systems have to be completely closed, however, for regression 
analysis > to be valid?  No. As I understand regression, the error term 
can accomodate > a porous system where the extraneous factors at least 
approximate > randomness in their effect. So I do not think that 
regression requires a > perfectly closed system.  Of course, more 
closure is necessary to make more > of regression than an evidentiary 
tool, to make it into the basis for > nomothetic explanation.  I don't 
think that obtains. > 
> The above is my general line at the moment, but I confess I am not 
entirely > satisfied with what I have said.  I confess Tony's questions 
require more > than the day I requested to think this through.  Let me 
hasten to add that > I was an early critic of the use to which 
sociologists typically put > regression analysis -- to search for 
timeless, contextless laws for > nomothetic explanation.  I don't 
support that.  I also don't think it makes > much sense to attempt to 
fix with any precision the exact beta weights > associated with 
economic variables as if they will remain stable. And much > of what 
goes on in econometrics sounds even worse than what has tended to > go 
on in sociology.  But as with postmodernism, I think there is 
something > of a baby there that should not be tossed out with the 
volume of bathwater. > 
> Well, it's 11:30 p.m. and my mind has just suddenly stopped. I 
welcome any > and all feedback from anyone.  Thanks, Tony, for asking 
some questions that > are much more difficult than they seem.  I will 
keep thinking about it. > 
> doug > 
> p.s. Tony, the private copy I just sent you was inadvertant.  I meant 
to > send to the list.
> > doug porpora
> dept of psych and sociology > drexel university
> phila pa 19104 > USA
> > porporad-AT-duvm.ocs.drexel.edu
> > 
> > 
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----------------------
Wendy K. Olsen
w.k.olsen-AT-bradford.ac.uk




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