Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
"JVerkuilen (Gmail)" <jvverkuilen@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Nonparametric Methods for Longitudinal Data |

Date |
Mon, 11 Feb 2013 11:30:48 -0500 |

On Mon, Feb 11, 2013 at 7:16 AM, Thomas Herold <thomasherold@gmx.net> wrote: > The problem is that it has recently been discovered that the depression > score we are working with can only be interpreted as ordinal data. What´s > more, the resulting variable is far from being normally distributed - the > data is just not suitable for parametric analysis. > These are two separate issues. Parametric analysis based on the assumption of the predictor being metric may be a very good approximation even if, strictly speaking, the measure doesn't satisfy the necessary assumptions of interval measurement. Also the crucial assumption is that errors are Gaussian, not that the dependent variable itself is; it can be markedly non-normal. Thus a model that assumes Gaussian errors and a linear regression may be pretty reasonable despite the issues you mention. However, screening data tend to be highly non-normal, often to the point that normal-theory based models such as linear mixed models break down. There are a number of ways to solve this problem but without knowing more about your data it's impossible to know. Questions you need to address: -Are there missing observations? If so, what is the nature of the missingness by variable? Do you have only time-varying missingness or are the time-constant variables missing? If the latter, the analysis has just become WAY more complex. -What kind of analysis do you want, conditional on subjects or some kind of population-averaged? If they are as I would suspect, you may benefit from a model such as a gamma Generalized Estimating Equation (GEE), or you might well be able to trick xtmepoisson into doing the analysis even if the data aren't integer-valued. * * 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: Nonparametric Methods for Longitudinal Data***From:*Thomas Herold <thomasherold@gmx.net>

- Prev by Date:
**Re: st: How to drop missing values by period?** - Next by Date:
**Re: st: Why do `test' and a`test' make a difference?** - Previous by thread:
**Re: st: Nonparametric Methods for Longitudinal Data** - Next by thread:
**re: Re: st: Nonparametric Methods for Longitudinal Data** - Index(es):