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From | "JVerkuilen (Gmail)" <jvverkuilen@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: reliability with -icc- and -estat icc- |
Date | Tue, 26 Feb 2013 20:40:49 -0500 |
On Tue, Feb 26, 2013 at 8:31 PM, Lenny Lesser <lenny3200@gmail.com> wrote: > Yes. I want to know how consistent the raters are in their scoring > and/or ranking. > The Applications are Fixed Effects. The raters are Random Effects. > > Any help would be appreciated. > > I have a colleague who works in SAS and did proc corr alpha. I'm not > sure if that is the correct way to do it, and I'm not sure that method > is possible in STATA. It's absolutely possible. I just ran the following model, which I believe (but am not 100% sure) is what you want. This has a random intercept for Rator and fixed effects for application. The ICC is massively inflated by rater 4, who is clearly anchored very differently and has a massively lower response variance. HUGE outlier. If you'd be willing I'd love to use it as an example for my Bayesian ICC estimator paper. . xtmixed Score i.Application if Rator != 4, || Rator:, covariance(independent) difficult Mixed-effects ML regression Number of obs = 33 Group variable: Rator Number of groups = 3 Obs per group: min = 11 avg = 11.0 max = 11 Wald chi2(10) = 95.51 Log likelihood = -77.750467 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ Score | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- Application | 2 | -7.333333 1.954361 -3.75 0.000 -11.16381 -3.502855 3 | -2 1.954361 -1.02 0.306 -5.830478 1.830478 4 | -2 1.954361 -1.02 0.306 -5.830478 1.830478 5 | -12 1.954361 -6.14 0.000 -15.83048 -8.169522 6 | -9.333333 1.954361 -4.78 0.000 -13.16381 -5.502855 7 | -9.333333 1.954361 -4.78 0.000 -13.16381 -5.502855 8 | -4 1.954361 -2.05 0.041 -7.830478 -.1695221 9 | -6 1.954361 -3.07 0.002 -9.830478 -2.169522 10 | 1 1.954361 0.51 0.609 -2.830478 4.830478 11 | -8.333333 1.954361 -4.26 0.000 -12.16381 -4.502855 | _cons | 14 1.565624 8.94 0.000 10.93143 17.06857 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ Rator: Identity | sd(_cons) | 1.274458 .6891609 .4416111 3.677996 -----------------------------+------------------------------------------------ sd(Residual) | 2.393594 .3090117 1.858493 3.082763 ------------------------------------------------------------------------------ LR test vs. linear regression: chibar2(01) = 3.99 Prob >= chibar2 = 0.0229 . estat icc Residual intraclass correlation ------------------------------------------------------------------------------ Level | ICC Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ Rator | .2208793 .1946277 .0299686 .7223361 ------------------------------------------------------------------------------ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/