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From |
Lisa Marie Yarnell <lisa.yarnell@utexas.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Re: three-level gllamm - variable as a nesting variable or a predictor? |

Date |
Sun, 6 Oct 2013 17:54:36 -0700 |

Thanks, Joseph. The two segments of the study are ascending and descending halves of a drinking experience; we are studying alcoholism. A person in the ascent (Segment A) has different physiological and mental symptoms than in the descent (Segment B). So perhaps this is like repeated measures and suggests a two-level model, as you said? But it is not a cross-over study per se, with two different treatments. It is just the two halves of a drinking experience. In the Stata output, when I constructed the model (again with 305 observations placed into either Segment A or Segment B, nested within 31 individuals), the Stata output showed: number of level 1 units = 305 number of level 2 units = 62 number of level 3 units = 31 I was confused at first because there are only really 2 levels of Level 2 (Segment A and Segment B). I wondered why the output shows 62 units at Level 2. But my thought was that Stata recognizes the three-level structure that I specified, and knowing that the Level 2 units are nested in 31 Level 1 persons, it indicates 62 units at Level 2? Can you explain why the Stata output would show 62 units at Level 2? Frankly, the way I had specified this model, I am not sure how I would depict this graphically--it seems mixed-up in some place. I will think about the alternative model that you suggested: > generate byte cons = 1 > eq idc: cons > eq ids: segment > gllamm drivesf gender aldh2cent braccent segment, /// > i(id) nrf(2) eqs(idc ids) /// > family(binomial) link(logit) adapt On Sun, Oct 6, 2013 at 1:33 AM, Joseph Coveney <stajc2@gmail.com> wrote: > Lisa Marie Yarnell wrote: > > Is it possible (or meaningful) to have a variable both as a nesting > variable AND a predictor in a three-level model? > > I have a three-level model with 305 observations nested within > segments of the study (segment A and segment B), which are nested in > 31 persons. id is the person id. > > So the form of the model is something like: > gllamm drivesf gender aldh2cent braccent, i(segment id) family(binom) > link(logit) eform > > But can I also put segment as a variable in the model to see its > direct effect on the dependent variable, contrasting segment B against > segment A? > > I want to put it both as a nesting variable AND a predictor in the > model. Is that viable? > > -------------------------------------------------------------------------------- > > From your description, it seems like a cross-over study. If that's the case, > then it seems that your model is misspecified. More to the point, I wouldn't > call your model a three-level model, but rather two-level. Each person > undergoes the same basic Study Segment A and Study Segment B when participating > in the study (repeated measures). In the model's random portion, segment should > be a random coefficient / random slope, and not a nesting factor / separate > level in a hierarchy--thus my comment that the model comes across as improperly > specified. > > If you look at the Stata documentation for the official -melogit- command, > you'll see the difference: its example of a three-level model has a *unique* > subject nested under a family. Even with cross-classified random effects, each > of the two factors has a property of uniqueness (for example, in the classic > example of students nested in schools who transfer to other schools, both > students and schools are unique). > > So, from your description of the circumstances, the model for -gllamm- would > look something like: > > generate byte cons = 1 > eq idc: cons > eq ids: segment > gllamm drivesf gender aldh2cent braccent segment, /// > i(id) nrf(2) eqs(idc ids) /// > family(binomial) link(logit) adapt > > You can find an analogous model (two-level logistic with a random slope) in the > examples for the study of contraceptive use in the documentation for the > official -melogit-; take a look at how the variable "urban" is handled, for > example: > > melogit c_use urban age child* || district: urban, cov(unstruct) > > You're probably aware that a sample size of 31 persons might be considered a > little lightweight for these kinds of methods; you might even have difficulty > with convergence. I don't know what drivesf is, but if it's discretized, then > maybe you can leave it as continuous and use a fixed-effects linear model > (-xtreg , fe-) and take advantage of the small-sample test statistics that you > get with it for the difference between study segments. (Time-invariant > variables, such as sex, will drop out of the fixed-effects model, but will > remain in the complementary -xtreg , be-.) > > Joseph Coveney > > * > * 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/ -- Lisa M. Yarnell Department of Psychology University of Southern California 3620 McClintock Ave. Mailroom SGM 501, Office 826B Los Angeles, CA 90089-1061 Mobile, preferred: (617) 548-2893 Office: x0-0850 or (213) 740-0850 Fax: (213) 746-9082 * * 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/

**Follow-Ups**:**Re: st: Re: three-level gllamm - variable as a nesting variable or a predictor?***From:*"Joseph Coveney" <stajc2@gmail.com>

**References**:**st: three-level gllamm - variable as a nesting variable or a predictor?***From:*Lisa Marie Yarnell <lisa.yarnell@utexas.edu>

**st: Re: three-level gllamm - variable as a nesting variable or a predictor?***From:*"Joseph Coveney" <stajc2@gmail.com>

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