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Re: st: Calculating and interpreting effect size when DV is a proportion


From   Michelle Dynes <dynes.michelle@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Calculating and interpreting effect size when DV is a proportion
Date   Mon, 14 Jan 2013 11:55:39 -0500

Thank you for providing your example Jeffrey! Is there a reasonable
way to decide between generating Fractional Proportion Ratios with
-eform- versus generating marginal effects using -margins- ? This
seems like an important question given that I have done both and the
two methods generate different results in terms of interpretation. The
general direction and strength of the associations are the same for
both strategies, but the percent change in my outcome variable is not
the same. Insight?
Thank you again to all who have contributed! I truly appreciate your
time and consideration.
Michelle

On Mon, Jan 14, 2013 at 11:26 AM, Jeffrey Wooldridge
<jmwooldridge60@gmail.com> wrote:
> Here is an example I generated from data that comes with my MIT Press book:
>
> . glm prate mrate c.mrate#c.mrate age c.age#c.age ltotemp i.sole,
> fam(bin) link(logit) robust
> note: prate has noninteger values
>
> Iteration 0:   log pseudolikelihood = -1315.4966
> Iteration 1:   log pseudolikelihood = -1288.1302
> Iteration 2:   log pseudolikelihood = -1287.6149
> Iteration 3:   log pseudolikelihood = -1287.6145
> Iteration 4:   log pseudolikelihood = -1287.6145
>
> Generalized linear models                          No. of obs      =      4075
> Optimization     : ML                              Residual df     =      4068
>                                                    Scale parameter =         1
> Deviance         =  882.4410467                    (1/df) Deviance =  .2169226
> Pearson          =  858.6841333                    (1/df) Pearson  =  .2110826
>
> Variance function: V(u) = u*(1-u/1)                [Binomial]
> Link function    : g(u) = ln(u/(1-u))              [Logit]
>
>                                                    AIC             =  .6353936
> Log pseudolikelihood = -1287.614502                BIC             = -32933.32
>
> ---------------------------------------------------------------------------------
>                 |               Robust
>           prate |      Coef.   Std. Err.      z    P>|z|     [95%
> Conf. Interval]
> ----------------+----------------------------------------------------------------
>           mrate |   1.377793   .1671457     8.24   0.000     1.050194
>   1.705393
>                 |
> c.mrate#c.mrate |  -.1943269   .1282904    -1.51   0.130    -.4457715
>   .0571177
>                 |
>             age |   .0474067    .006151     7.71   0.000      .035351
>   .0594625
>                 |
>     c.age#c.age |  -.0004339   .0001756    -2.47   0.013     -.000778
>  -.0000898
>                 |
>         ltotemp |  -.2087835   .0141589   -14.75   0.000    -.2365345
>  -.1810325
>          1.sole |   .1675674   .0507829     3.30   0.001     .0680348
>      .2671
>           _cons |   2.330817   .1089061    21.40   0.000     2.117365
>   2.544269
> ---------------------------------------------------------------------------------
>
> . margins, dydx(*)
>
> Average marginal effects                          Number of obs   =       4075
> Model VCE    : Robust
>
> Expression   : Predicted mean prate, predict()
> dy/dx w.r.t. : mrate age ltotemp 1.sole
>
> ------------------------------------------------------------------------------
>              |            Delta-method
>              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
> -------------+----------------------------------------------------------------
>        mrate |   .1586229   .0125717    12.62   0.000     .1339829    .1832629
>          age |   .0053308   .0005544     9.61   0.000     .0042441    .0064174
>      ltotemp |  -.0265256    .001827   -14.52   0.000    -.0301065   -.0229447
>       1.sole |     .02093   .0062078     3.37   0.001     .0087628    .0330971
> ------------------------------------------------------------------------------
> Note: dy/dx for factor levels is the discrete change from the base level.
>
> The AME for mrate means that if mrate (the match rate) increases by
> .10 (ten cents on the dollar) then, on average, the prate
> (participation rate) increases by about .016, or 1.6 percentage
> points.
>
>
>
>
>
>
>
> On Mon, Jan 14, 2013 at 11:06 AM, Michelle Dynes
> <dynes.michelle@gmail.com> wrote:
>> Thank you Maarten and Jeffrey for your prompt replies! I have gone
>> ahead and followed the example Maarten provided by centering my
>> continuous variables in the fractional logit model along with the
>> -eform- command. Maarten, for further clarification, is it ok to refer
>> to the ORs, produced using the -eform- command per your example, as
>> Relative Proportion Ratios even though Stata reports them as ORs? This
>> makes sense to me given the outcome variable is a proportion, but I
>> thought I would double check. Many thanks!
>> *
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> *
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