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From |
Nicole Boyle <[email protected]> |

To |
[email protected] |

Subject |
Re: st: "Proper" usage: Univariate, bivariate, multivariate, multivariable |

Date |
Tue, 15 Oct 2013 16:25:09 -0700 |

Thanks so much to Alfonso, Austin, Nick, and Joe for your helpful responses! Per your responses, it seems that these distinctions in terminology, particularly the multivariable vs. multivariate distinction, may only be discussed in the biostatistics/epidemiology contexts, if discussed at all. I'm still perplexed by the usage of some of this terminology (for instance, I've personally heard the same people that distinguish multivariable models from multivariate models by the number of "left-hand side" variables ALSO distinguish univariate models from multivariable models by the number of "right-hand side" variables), but it's very interesting to know that some statistical circles don't even consider the -variate and -variable suffixes as different. For now, I'll follow the Hidalgo (2013) paper cited by Joe and change my sentence from "Univariate and multivariable Cox proportional hazards models..." to "Simple and multivariable Cox proportional hazards models..." Thanks again! Nicole Boyle Hidalgo B, Goodman M. Multivariate or multivariable regression? Am J Public Health. 2013 Jan;103(1):39-40. doi: 10.2105/AJPH.2012.300897. Epub 2012 Nov 15 On Tue, Oct 15, 2013 at 1:42 PM, Alfonso Sanchez-Penalver <[email protected]> wrote: > Hi Nick, > > Yes that's true, but we could make the argument that covariance (and hence correlation) comes from the co-movement of the deviations from the mean of the two variables, which are the errors. > > Alfonso Sánchez-Peñalver > >> On Oct 15, 2013, at 4:36 PM, Nick Cox <[email protected]> wrote: >> >> I'd suggest that "bivariate" also applies to discrete variables and to >> variables just taken two at a time, with no question of an error term. >> Historically, for example, Pearson correlation was just a property of >> a bivariate normal distribution, with no model with error terms >> invoked. >> Nick >> [email protected] >> >> >> On 15 October 2013 21:11, Alfonso Sanchez-Penalver >> <[email protected]> wrote: >>> Hi Nicole, >>> >>> To me a bivariate (and this can be extended to multivariate very straight forwardly) is one where there are two error terms that come from the same distribution, and thus they follow a bivariate distribution. Usually this is because you are trying to estimate the expected value of two dependent variables with two different equations, and the errors are not independent across equations. Anyone understands this differently? >>> >>> Alfonso Sánchez-Peñalver >>> >>>> On Oct 15, 2013, at 3:51 PM, Nicole Boyle <[email protected]> wrote: >>>> >>>> Hello all, >>>> >>>> There are some terms commonly used in the literature that seem (to me) >>>> technically misused. Nick Cox addressed a similar question >>>> previously*, but I'm unfortunately still confused as to the proper >>>> usage of these terms. >>>> >>>> My understanding: >>>> >>>> (1) Multivariable: Model with more than one exposure var and one outcome var. >>>> >>>> (2) Multivariate: Model with one or more exposure vars and multiple >>>> outcome vars. >>>> >>>> (3) Multivariable model != Multivariate model >>>> >>>> (4) Univariate: Not a true model, but just looks at distribution of >>>> one "exposure" var within a group. This method may be repeated across >>>> multiple groups, e.g. demographics table with no test statistics. (In >>>> my humble opinion, this should be instead termed "univariable" to >>>> indicate a single variable, since "univariate" seems to imply a model >>>> with one outcome variable and an undefined number of exposure vars.) >>>> >>>> (5) Bivariate: Model with one exposure var and one outcome var. (In my >>>> very novice opinion, this should instead be termed "bivariable" to >>>> indicate two variables, since "bivariate" seems to imply two outcome >>>> variables with an undefined number of exposure vars.) >>>> >>>> (6) Univariate!=Bivariate >>>> >>>> I've decided to run this by you all while writing what feels like a >>>> strange sentence: "Univariate and multivariable Cox proportional >>>> hazards models..." Perhaps this should be "Bivariate and >>>> multivariable" or even "Bivariable and multivariable"? >>>> >>>> What would be considered proper usage (where "proper usage" might be >>>> defined as technically correct, or might even be defined as >>>> technically incorrect but widely accepted)? >>>> >>>> Thanks so much for your consideration, >>>> Nicole Boyle >>>> >>>> * http://www.stata.com/statalist/archive/2009-02/msg00398.html >>>> * >>>> * 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/ >>> >>> * >>> * 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/ >> >> * >> * 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/ > > * > * 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/ * * 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/

**References**:**st: "Proper" usage: Univariate, bivariate, multivariate, multivariable***From:*Nicole Boyle <[email protected]>

**Re: st: "Proper" usage: Univariate, bivariate, multivariate, multivariable***From:*Alfonso Sanchez-Penalver <[email protected]>

**Re: st: "Proper" usage: Univariate, bivariate, multivariate, multivariable***From:*Nick Cox <[email protected]>

**Re: st: "Proper" usage: Univariate, bivariate, multivariate, multivariable***From:*Alfonso Sanchez-Penalver <[email protected]>

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