Well, tastes differ.  I, for one, don't think it is so obvious that
graphs are underutilized and tables overutilized in political science
journals.  Obviously, it is often most helpful to have both.  As
electronic publishing becomes the norm, that will presumably become
increasingly common.  In the meantime, however, when space constraints
necessitate a choice my preference will usually be for the table.  Not
always, of course -- a scatterplot with lowess regression superimposed
has no satisfactory tabular equivalent.  But if the point is simply to
report regression coefficients, graphs are likely to take more space and
convey the relevant information much less precisely.  For making a point
in an undergraduate textbook or an op-ed piece that may be fine, but for
a scholarly journal article accuracy and efficiency should often take
precedence over "present[ing] your work in the most compelling visual
manner," as Mike puts it. 

Having said that, it should be obvious where I stand on Anthony's
original question about reporting point estimates.  Yes, by all means --
but with the associated standard errors as well, so that readers can
appropriately gauge the relevant uncertainty.  (Do most readers "really
understand" what these mean?  That, too, is largely a matter of context.
For an undergraduate textbook, probably not.  For a scientific journal,
I'd hope so.)  Simply reporting p-values will only be informative to
readers who happen to be interested in the null hypothesis that the
relevant parameter value is zero, which I almost never am.  The point of
doing _quantitative_ analysis, as I see it, is to learn something about
the _magnitudes_ of empirical relationships, and that is what the point
estimates (and standard errors) convey.

Larry


-----Original Message-----
From: Political Methodology Society [mailto:[log in to unmask]] On
Behalf Of Antonio P. Ramos
Sent: Friday, March 27, 2009 5:55 PM
To: [log in to unmask]
Subject: Re: [POLMETH] FW: [POLMETH] Displaying regression coefficients
and standardized partial regression coefficients

Hi all,

The issue about the Journal editors in political science is a serious
one:
why would we learn how to program detailed graphical displays if, at the
end, what they want us to show are difficult to interpret tables. After
all, these graphs need careful thinking and some programming experience,
while just say coefficient beta one is significant does not require much
effort....

How can we persuade people - including graduate students like me - to
learn these smart and "conscious" ways to present data and results if
referees don't allow us to use them?

All the best,

Antonio.

On Fri, Mar 27, 2009 at 1:21 PM, mike <[log in to unmask]> wrote:

> Anthony (regards from Seattle!)
>
> All of this is good advice, but don't forget that journal editors can 
> also learn new tricks. So don't stop trying to present your work in 
> the most compelling visual manner, even when they might want yet 
> another regression table...
>
>
> Michael D. Ward, Professor of Political Science University of 
> Washington, Seattle, WA, 98195-3530, USA
>
>  direct: 206.616.3583 (email is better) messages: 206.543.2780; fax:
> 206.685.2146 web site: faculty.washington.edu/mdw
>
>
> On Fri, 27 Mar 2009, Anne Sartori wrote:
>
>  Anthony,
>>
>> On the problem with standardized betas, see
>>
>> King, Gary, "How Not to Lie with Statistics: Avoiding Common Mistakes

>> in Quantitative Political Science," American Journal of Political 
>> Science 30:3 (August, 1986) 666-687.
>>
>> Best, Anne
>>
>> Anne E. Sartori
>> Associate Professor of Political Science and (by courtesy) of 
>> Managerial Economics and Decision Sciences Northwestern University 
>> [log in to unmask]
>> (847) 491-4017
>>
>>
>>
>> -----Original Message-----
>> From: Political Methodology Society [mailto:[log in to unmask]]

>> On Behalf Of Jay Ulfelder
>> Sent: Friday, March 27, 2009 5:13 AM
>> To: [log in to unmask]
>> Subject: Re: [POLMETH] Displaying regression coefficients and 
>> standardized partial regression coefficients
>>
>> Anthony,
>>
>> I suspect that journals' editorial practices play an important role 
>> in compelling authors to present results in the conventional form.
>>
>> I had one experience where I submitted an article that presented 
>> results from a logistic regression model in graphical form, showing 
>> the 95% CIs associated with the estimated odds ratios for a set of 
>> binary variables (membership in a variety of organizations). This was

>> about as simple a case as you could get, because the original 
>> estimates were all directly comparable to one another, and I still 
>> included a table showing the results in the industry-standard way in 
>> an appendix. Nevertheless, the one significant change the editors 
>> asked me to make before publication was to dump the chart and replace

>> it with the table. They didn't explicitly say why.
>>
>> -Jay
>>
>> On Thu, Mar 26, 2009 at 5:40 PM, Anthony A. Pezzola <[log in to unmask]>
>> wrote:
>>
>>> It may be that I have misinterpreted a few things, but here is my 
>>> question in two parts.  I would greatly appreciate any thoughts, 
>>> references, ideas, or insights.
>>>
>>>
>>>
>>> First, given that p-values in a regression only allow us to reject 
>>> the null hypothesis that the parameter is less-than or equal to 
>>> (greater-than or equal to) zero, why do we display the regression 
>>> coefficients (point estimates), especially since the actual 
>>> parameter may be very different that the point estimate arrived at 
>>> by the regression.  Yes, the point estimate is our "best guess" as 
>>> the influence of the parameter, but doesn't displaying the 
>>> coefficient suggest a degree of accuracy that we do not have?
>>>  Wouldn't
>>> it be better to simply display a +(-) and the p-value?  Or perhaps 
>>> the 95% confidence interval of the point estimate?  Removing the 
>>> point estimates of the explanatory variables from our tables would 
>>> highlight the degree of uncertainty that exists about the "true" 
>>> influence of the explanatory variables being studied.  I know that 
>>> reported the regression coefficient is noting more than a point 
>>> estimate, but do most readers really understand the degree of 
>>> uncertainty behind them?
>>>
>>>
>>>
>>> Second, if we are going to display regression coefficients, why 
>>> isn't standard practice to (also) display standardized regression 
>>> coefficients (standardized partial regression coefficients)?  
>>> Standardized regression coefficients are harder to interpret in 
>>> terms of, say, "X increase in GPD decrease child mortality by Y", or

>>> "X increase in a country's polity score increases foreign direct 
>>> investment by Y," but they often allow a direct and clear comparison

>>> of the influence of the different causal variable being utilized in 
>>> a model.  If we display regression coefficients because we are 
>>> interested in the degree that variable influences the outcome being 
>>> studied, doesn't make more sense to let the reader know the interval

>>> of influence we would normally expect to see by standardizing the 
>>> coefficients?  With out knowing the standard deviation of the 
>>> response variable it is difficult to know the degree of influence 
>>> that the explanatory variables have.  When standardized coeffiecents

>>> are presented the reader is much less likely to confuse a large 
>>> coefficient with a strong effect and a small coefficient with a big 
>>> effect.  Of course, standardized regression coefficentes should be 
>>> used with care, especially when sampling error or multicollinearity 
>>> may inflate standad errors oe when the variables being compared have

>>> nonnormal distributions.  I have read Fox's two pages on the 
>>> subject, but was left unconvinced that using standardized partial 
>>> regression coefficients is not a generally better approach to 
>>> conveying information.  Hence, any insights or references on this 
>>> subject would be greatly appreciated.
>>>
>>>
>>>
>>> Finally, is there any particular difficulty in interpreting the 
>>> standardized partial regression coefficients variables included in 
>>> an interaction term?
>>> Is it possible to simply generate 3D graphics just like you would 
>>> for the unstandardized coefficients?
>>>
>>>
>>>
>>> Cheers and thanks in advance,
>>>
>>> Anthony
>>>

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