RE: st: Standardized coefficients using nestreg: svy

[Cameron McIntosh <cnm100@hotmail.com>]

I think that standardized coefficients are always a bad idea, but opinions vary:
Brillinger, D.R. (2001a). John Tukey and the correlation coefficient. Computing Science and Statistics, 33, 204–218.http://www.galaxy.gmu.edu/interface/I01/I2001Proceedings/DBrillinger/DBrillinger.pdf
Brillinger, D.R. (2001b). Does Anyone Know When the Correlation Coefficient Is Useful? A Study of the Times of Extreme River Flows. Technometrics, 43(3), 266-273.
Baguley, T. (2010). When correlations go bad. The Psychologist, 23(2), 122-123.http://www.thepsychologist.org.uk/archive/archive_home.cfm/volumeID_23-editionID_185-ArticleID_1633-getfile_getPDF/thepsychologist/0210bagu.pdf
King, G. (1986). How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science. American Journal of Political Science, 30(3), 666-687.http://gking.harvard.edu/files/mist.pdf>http://gking.harvard.edu/files/mist.pdf
Richards, J.M., Jr. (1982). Standardized versus Unstandardized Regression Weights. Applied Psychological Measurement, 6(2), 201-212. Greenland, S., Schlessman, J.J., & Criqui, M.H. (1986). The fallacy of employing standardized regression coefficients and correlations as measures of effect. American Journal of Epidemiology, 123, 203–208.
Greenland, S., Maclure, M., Schlessman, J.J., Poole, C., & Morgenstern, H. (1991). Standardized Regression Coefficients: A Further Critique and Review of Some Alternatives. Epidemiology, 2(5). 387-392. 
Criqui, M.H. (1991). On the Use of Standardized Regression Coefficients. Epidemiology, 2(5), 393.  Newman, T.B., & Browner W.S. (1991). In defense of standardized regression coefficients. Epidemiology, 2(5), 383-386.
Luskin, R.C. (1991). Abusus Non Tollit Usum: Standardized Coefficients, Correlations, and R2s. American Journal of Political Science, 35(4), 1032-1046.  Bring, J. (1994). How to standardize regression coefficients. American Statistician, 48, 209–213. Hofler, M. (2008). Translocation relative to range: A standardized index for effect intensity. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 4(3), 132-138.
Hargens, L.L. (1976). A Note On Standardized Coefficients as Structural Parameters. Sociological Methods & Research, 5(2), 247-256
Kim, J.O., & Feree, G. D. (1981). Standardization in causal analysis. Sociological Methods and. Research, 10(2), 187–210.
Gelman, A. (2008). Scaling regression inputs by dividing by two standard deviations. Statistics in Medicine, 27(15), 2865-2873.http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf
Cam

> Date: Sun, 6 Nov 2011 20:14:28 -0500
> To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu
> From: richardwilliams.ndu@gmail.com
> Subject: Re: st: Standardized coefficients using nestreg: svy
> 
> At 06:17 PM 11/6/2011, Patricia Logan-Greene wrote:
> >Hello All,
> >
> >We are hoping to get standardized betas when using nested 
> >regressions and the svy commands. The usual command (,beta) doesn't 
> >work with the svy commands. Adding listcoef after the nestreg 
> >command, unfortunately, only provides the betas for the last step. 
> >One can repeat each step of the nested regression separately, adding 
> >listcoef after each step, however the sample n in earlier steps does 
> >not always match the n included in the nested regression, as cases 
> >with missing values on later steps are excluded from all steps in nestreg.
> >
> >Is there another way to get the betas? I suppose one could limit the 
> >sample to those with full data on all variables, OR could 
> >standardize all the variables before running the regression. Would 
> >love to hear if there's another option.
> 
> It isn't that hard to use the same sample throughout. Do something like
> 
> gen touse = !missing(y, x1, x2, x3, x4, x5, x6)
> svy, subpop(touse): reg y x1 x2
> listcoef
> svy, subpop(touse): reg y x1 x2 x3 x4
> listcoef
> 
> Whether you should do this is another matter. The fact that the 
> -beta- option doesn't work is one warning sign. I could swear there 
> were threads explaining why standardized coefficients with svy: were 
> a bad idea, but I can't find them, so maybe I am just imagining things.
> 
> 
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
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> EMAIL:  Richard.A.Williams.5@ND.Edu
> WWW:    http://www.nd.edu/~rwilliam
> 
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