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From |
Cameron McIntosh <cnm100@hotmail.com> |

To |
STATA LIST <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: comparing coefficients across models |

Date |
Sat, 4 Aug 2012 19:50:25 -0400 |

I would also suggest being wary of non-homogeneity of within-group error variance across the levels of the categorical moderator. That can be dealt with fairly easily, however. Su, H., & Liang, H. (2010). An Empirical Likelihood-Based Method for Comparison of Treatment Effects—Test of Equality of Coefficients in Linear Models. Computational Statistics and Data Analysis, 54(4), 1079–1088. Smithson, M. (2012). A simple statistic for comparing moderation of slopes and correlations. Frontiers in Psychology, 3(231). http://www.frontiersin.org/Quantitative_Psychology_and_Measurement/10.3389/fpsyg.2012.00231/full Weerahandi, S. (1987). Testing Regression Equality with Unequal Variances. Econometrica, 55(5), 1211-1215. http://www.weerahandi.org/ Cam > Date: Sat, Aug 012 3::5::8 -700< > From: ggs_da@yahoo.com > Subject: Fw: st: comparing coefficients across models > To: statalist@hsphsun..harvard.edu > > > David, > > > Thank you so much for helping me think this through. I very much appreciate it. > > Here are the sample sizes for group_dummy (//)) > > group dummy > | Freq. Percent Cum. > ------------+----------------------------------- > | ,,98 1..5 1..5< > | ,,97 8..5 00..0< > ------------+----------------------------------- > Total | ,,95 00..0< > > Your assumptions are correct. Business group dummy is different from group_dummy. Also, the group_dummy in the interaction model is when group== and when group ==.. Also, I am only interested in whether the slope against x differs when group_dummy=//.. I am not intersted in the intercept for the group dummy. From what I understand, I can get at the slope for x by running the regression for the two groups separately, and then comparing the coefficients for x.. Is this correct? Or are you saying something different? > > As you suggested I also looked at the graphs for the relationship between the two variables that are highly correlated (degree centrality and betweenness centrality) for ()) the whole sample, ()) for group_dummy== and ()) for group_dummy==.. The three graphs look extremely similar with a negative, slightly curving slope (I can't seem to figure out how to attach a file, and not get my mail bounced from statalist). Also, I apologize, but I made a mistake in my earlier email, the high correlation is between x degree centrality and x betweenness centrality, and not between x and x.. > > Thanks once again for your help > Dalhia > > ----- Original Message ----- > From: David Hoaglin <dchoaglin@gmail.com> > To: statalist@hsphsun..harvard.edu > Cc: > Sent: Friday, August ,, 012 ::8 PM > Subject: Re: st: comparing coefficients across models > > Dalhia, > > Thanks for the correction. > > I may not understand the relation between the variable group, which > took the values and in your initial example, and group_dummy in > the interaction model. I assume that group_dummy is when group = < > and when group = .. > > I'm also assuming that group_dummy is different from x.. > > From the substantial negative correlation between x and x,, I infer > that the mean of x differs substantially between the two business > groups distinguished by x.. > > I wonder whether the relation between group_dummy and x is part of > the problem. Also, what are the relative sample sizes for the two > values of group_dummy? > > Without x**group_dummy in the model, you would be fitting a slope > against x,, an offset for x,, and a slope against x.. When you > include x**group_dummy, you are fitting an additional slope against x< > for the two groups defined by group_dummy (i.e., if b is the > coefficient of x and b is the coefficient of x**group_dummy, the > slope against x is b when group_dummy = and b + b when > group_dummy = )). > > You aren't including group_dummy itself as a predictor, so I assume > that you don't want different intercepts for those two groups. > > You have few enough variables that you should be able to diagnose the > problem by looking at how x and x are each related to x and > plotting x against x (overall, within the two groups defined by x,, > and within the two groups defined by group_dummy). Also, as I > suggested above, look at a crosstab of x and group_dummy. > > David Hoaglin > > On Fri, Aug ,, 012 at ::6 AM, Dalhia <ggs_da@yahoo.com> wrote: > > David, > > Sorry about the confusion. Typo. > > > > Here is what I should have said: > > > > This is how I ran the interaction model: > > xtreg y x x x x**group_dummy, fe robust > > > > > > where y is log of Tobin's q (a measure of firm performance) > > x is degree centrality (a network measure - continuous) > > x is business group dummy (codes whether or not a firm belongs to a cluster of firms) > > x is betweenness centrality (a network measure - continuous) > > group_dummy is whether or not the firm belongs to a particular component in the network. > > > > x and x are the most highly correlated (-..3)). > > > > Thanks. > > Dalhia > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: comparing coefficients across models***From:*David Hoaglin <dchoaglin@gmail.com>

**References**:**Re: st: Odd behaviour of wordcount function***From:*"Seed, Paul" <paul.seed@kcl.ac.uk>

**st: comparing coefficients across models***From:*Dalhia <ggs_da@yahoo.com>

**Re: st: comparing coefficients across models***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: comparing coefficients across models***From:*Dalhia <ggs_da@yahoo.com>

**Re: st: comparing coefficients across models***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: comparing coefficients across models***From:*Dalhia <ggs_da@yahoo.com>

**Re: st: comparing coefficients across models***From:*David Hoaglin <dchoaglin@gmail.com>

**Fw: st: comparing coefficients across models***From:*Dalhia <ggs_da@yahoo.com>

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