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

-----------------------------------------------

Anthony A. Pezzola

apezzola@uc.cl

(02) 354-7823

Profesor de Ciencia Política

Instituto de Ciencia Política

Pontificia Universidad Católica de Chile

Santiago de Chile

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

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

a-sartori@kellogg.northwestern.edu

(847) 491-4017

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

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.

I don't disagree with Larry. Visuals are conveyed quickly; tables can be studied carefully for detail.

Where details are important (hopefully in much scholarly work) tables will be essential, even if they are

relegated to appendices. If you wanted to know life expectancies for example, a table is perfect,

whereas a visual portrayal won't have the detailed information.

I was simply arguing (perhaps too obliquely) that relying on contemporary standards in state of the art

journals may not be the best approach to presenting your own work. Someone had to be the first to

present a density plot, for example, in the APSR.

My own opinion is that the APSR standard for regression tables consumes far too many column inches.

Every year I give a quiz to my graduate students to tell me the value of a single coefficient they

remember reading during the previous quarter. So far my record is "perfect".

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

Hi all,

In an article published in *Perspectives on Politics* in 2007, Eduardo Leoni

and I argue that in most cases, graphs of regression results can more

effectively present all the information contained in a standard regression

table, and can do so using a similar amount of space. We illustrate this by

converting a few published tables into regression graphs.

The paper is available at

http://www.columbia.edu/~jpk2004/graphs.pdf

there is also a web site accompanying the paper at

http://tables2graphs.com/doku.php that contains replication code.

Best,

John Kastellec