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 |
Mosi Ifatunji <ifatunji@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: st: Using MVN for Multiple Missing Ordinal Variables |

Date |
Sun, 8 May 2011 11:29:31 -0400 |

Daniel, I also found this information useful. Thanks, Mosi On May 8, 2011, at 10:32 AM, daniel klein wrote: First of all, I am all but an expert on multiple imputation -- however interested in it. So I hope there will also be answers from more experienced people. As far as I know, MVN is designed for continuos variables. However Allison (2002:38-40) describes an ad-hoc solution to impute categorical variables. I have an .ado file assigning "final values" to such imputed variables (-findit mi mvncat-). I can think of other possibilities however. If your variables are not "strictly" categorical (such as race in the NLSW88.dta -sysuse nlsw88-), you might want to consider treating them as if they were continous. For example a variable with levels 1. "strongly disagree" to 7. "strongly agree" migth well be treated continous (as you woud do using them in a regression) in which case -mi impute mvn- is fine without any afterward "ad-hoc" corrections. You might also want to use Royston's (e.g. 2005) -ice- (findit -ice-, also see -help mi ice-) to impute missing values using chaind-equations. This allows you to impute missing values using ordinal regression modells. From what I understand, chaind-equations perform pretty well in simulations, yet are not theoretically established. I did not fully understand what kind of descriptive statistics you want to calculate and report. Concerning "descriptive" statistics in general, as far as I know, there is no need to adjust the variance. If you are only interessted in point estimates (not their standard errors and therefore not in statistical inference) you just average the respective point estimates. This is what Yulia Marchenko's -mibeta- (-findit mibeta-) does, reporting R-squared messures for -mi estimate regress- (see http://www.stata.com/support/faqs/stat/mi_combine.html). I have an .ado reporting summary statistics for the dataset cobining results from -summarize- (-findit misum-), where I do not adjust the (sample) variance. Best Daniel References Allison, Paul D. (2002) Missing Data. Thousand Oaks, CA: Sage Publications. Royston, P. (2005) Multiple imputation of missing values: update. Stata Journal 5 (2), 188-201. * * 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/

**References**:**Re: st: st: Using MVN for Multiple Missing Ordinal Variables***From:*daniel klein <klein.daniel.81@googlemail.com>

- Prev by Date:
**st: Saving matrices with -simulate-** - Next by Date:
**RE: st: shifting existing values to subsequent variables in a row** - Previous by thread:
**Re: st: st: Using MVN for Multiple Missing Ordinal Variables** - Next by thread:
**st: Saving matrices with -simulate-** - Index(es):