Amelia August 2005

amelia@lists.gking.harvard.edu

4 participants
6 discussions

by Gary King

Does anyone have a suggestion? I think this may be a windows problem.. Gary King ---------- Forwarded message ---------- Date: Tue, 30 Aug 2005 22:22:57 -0400 From: Marie Bernard <mariee(a)wow.net> To: king(a)harvard.edu Dear Professor, I was trying to use your Amelia programme but it will not start. The GSRUN part of the programme tells me that "The plication has failed to start because xnmhn4548.dll was not found. Reinstaling the application may fix this problem." I tried reinstalling from your site but it still does not function. I wonder if you can give some advise. Marie-Therese Bernard Student of the University of the West Indies - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia

18 years, 7 months

Amelia hangs up

by Maria

Dear all, I have a problem with imputing missing values in Amelia. I have a dataset of 144 variables and 282 cases (I know it's no good :))); most of the variables are count data which are NOT normally distributed nor could they be transformed into normal. Percent of missingness is rather high (there are very few fully complete cases, average percent - 18 and it varies from 0% to 90%). When I try to use Amelia just to look what I might get, it simply vanishes from the screen without any error messages after I press 'Run' button. I tried to specify most simple options (a couple of indicator variables, a couple of fully complete variables) as well as more complicated (e.g., a ridge prior of 1; I would be grateful if somebody explained me what it exactly means, I haven't found it anywhere). The result was always the same. Thanks to everyone who might give some idea as to what to do with this! With kind regards, Maria Pavlova (a psychologist) Moscow State University Russia mailto:haul@rambler.ru - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia

18 years, 8 months

RE: [amelia] Commands for Imputing Dichotomous Variables in AMELIA

by Engstrom, Malitta

Thanks for sending along this additional information, Paul. A central part of my read on the additional articles focuses on the relationship between the proportion of missing data and the relative bias. Although the proportion of missing data is relatively small in dataset with which I'm working (more than half of the variables with missing data have a missing rate of less than 1%; the largest percentage of missing data is 12-13% among a small number of variables), and thus, there could be options for using the normal model of estimation and rounding for binary variables that have the lowest rate of missing data if necessary, I've been persuaded...Some of the dichotomous variables I'm using were formed from continuous variables. I've decided to use the continuous variables in the imputation model, and, if there remains a need for dichotomous values in the analysis models (as I believe there will), I will dichotomize the continuous variables post-imputation. I think this route will help resolve my quandary and reduce the potential for bias in resulting estimates. Thanks, again, for all of the information and input. Malitta Engstrom, Ph.D., L.C.S.W. Assistant Professor University of Chicago -----Original Message----- From: Paul von Hippel [mailto:von-hippel.1@osu.edu] Sent: Wednesday, August 03, 2005 3:02 PM To: Engstrom, Malitta; Gary King Cc: amelia(a)latte.harvard.edu Subject: RE: [amelia] Commands for Imputing Dichotomous Variables in AMELIA My reading of their article is that it's best to avoid rounding imputed values if possible. Here are two more pertinent articles: http://www2.sas.com/proceedings/sugi30/112-30.pdf http://www2.sas.com/proceedings/sugi30/113-30.pdf At 03:34 PM 8/3/2005, Engstrom, Malitta wrote: >Many, many thanks for this information. It sounds like experiments >conducted specifically with AMELIA indicate that using the normal model >underlying AMELIA to impute dichotomous variables (which will be rounded >to 0 or 1) will not create biased estimates. The article by Horton, >Lipsitz, and Parzen suggests that such action should be taken with >caution as there may be a risk of biased estimates in certain situations >when the imputed values are dichotomized. From my read of their >article, it looks like: (1) higher rates of missing data may result in >greater bias; (2) their demonstration of "a simpler model" may not apply >to all situations; and (3) when given an option, it may be better to >select a categorical variable which is not dichotomous for imputation >using the normal model and rounding the variable to reduce possible >bias. > >As you might have guessed, what prompted my question was the imputation >of a dataset with more dichotomous variables than could be accommodated >using the "AMORDS" command in AMELIA; thus it requires the use of the >"ANOMS" command for some of the dichotomous variables. Given the >experiments conducted specifically with AMELIA, it seems that using this >second configuration should be okay. If there is any further information >I should consider, have missed something in my synthesis of the >information, or if there are any specific tests I should conduct to >check for potential bias, I'd be grateful for the input. > >Thank you, again, for your thoughtful (and helpful!) responses. > >Malitta Engstrom, Ph.D., L.C.S.W. >Assistant Professor >University of Chicago > >-----Original Message----- >From: Paul von Hippel [mailto:von-hippel.1@osu.edu] >Sent: Tuesday, August 02, 2005 2:09 PM >To: Gary King; Engstrom, Malitta >Cc: amelia(a)latte.harvard.edu >Subject: Re: [amelia] Commands for Imputing Dichotomous Variables in >AMELIA > >Here's a pertinent article by Horton et al: >http://www.biostat.harvard.edu/~horton/tasround.pdf >It shows that, when a normal model is used to impute a dichotomous >variable, it's best not to dichotomize the imputed values. > >Best -- >Paul > >At 03:04 PM 8/2/2005, Gary King wrote: > > >Experiments seem to indicate that for imputation, you can use the >normal > >model underlying Amelia, even though you wouldn't want to use it for >the > >analysis model. if you choose options appropriately, Amelia will > >dichotomize the resulting simulations for convenience of subsequent > >analysis. But if that's not necessary for your analysis model, then >using > >the continuously imputed variable should work fine. > > > >Gary King > > > >--- > >Gary King Institute for Quantitative Social Science > >Harvard University, 34 Kirkland St, Cambridge, MA 02138 > >http://GKing.Harvard.Edu, King(a)Harvard.Edu > >Direct 617-495-2027, Assistant 495-9271, eFax 812-8581 > > > >On Tue, 2 Aug 2005, Engstrom, Malitta wrote: > > > >>Dear Dr. King, > >> > >>I'm hoping you can provide some information, or direct me to a >resource > >>for gathering information, regarding the imputation of dichotomous > >>variables with AMELIA. Specifically, I'm wondering about how > >>problematic it is to use the "ANOMS" command with the imputation of > >>dichotomous variables. In statistical consultations, I've been >informed > >>that it should not be a problem to use this command; however, since >the > >>program directs one to use the "AMORDS" command with the imputation of > >>dichotomous variables, I wanted to gather additional information about > >>it. > >> > >>I would be most grateful for any information you can share on this > >>topic. > >> > >>Thank you, > >>Malitta Engstrom > >>Assistant Professor > >>University of Chicago > >>- > >>Amelia mailing list served by Harvard-MIT Data Center > >>[Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia > >- > >Amelia mailing list served by Harvard-MIT Data Center > >[Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia > > > >Paul von Hippel >Department of Sociology / Initiative in Population Research >Ohio State University >300 Bricker Hall >190 N. Oval Mall >Columbus OH 43210 >614 688-3768 >Office hours TThF 3-5pm >I read email every weekday at 3. Paul von Hippel Department of Sociology / Initiative in Population Research Ohio State University 300 Bricker Hall 190 N. Oval Mall Columbus OH 43210 614 688-3768 Office hours TThF 3-5pm I read email every weekday at 3. - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia

18 years, 8 months

RE: [amelia] Commands for Imputing Dichotomous Variables in AMELIA

by Paul von Hippel

My reading of their article is that it's best to avoid rounding imputed values if possible. Here are two more pertinent articles: http://www2.sas.com/proceedings/sugi30/112-30.pdf http://www2.sas.com/proceedings/sugi30/113-30.pdf At 03:34 PM 8/3/2005, Engstrom, Malitta wrote: >Many, many thanks for this information. It sounds like experiments >conducted specifically with AMELIA indicate that using the normal model >underlying AMELIA to impute dichotomous variables (which will be rounded >to 0 or 1) will not create biased estimates. The article by Horton, >Lipsitz, and Parzen suggests that such action should be taken with >caution as there may be a risk of biased estimates in certain situations >when the imputed values are dichotomized. From my read of their >article, it looks like: (1) higher rates of missing data may result in >greater bias; (2) their demonstration of "a simpler model" may not apply >to all situations; and (3) when given an option, it may be better to >select a categorical variable which is not dichotomous for imputation >using the normal model and rounding the variable to reduce possible >bias. > >As you might have guessed, what prompted my question was the imputation >of a dataset with more dichotomous variables than could be accommodated >using the "AMORDS" command in AMELIA; thus it requires the use of the >"ANOMS" command for some of the dichotomous variables. Given the >experiments conducted specifically with AMELIA, it seems that using this >second configuration should be okay. If there is any further information >I should consider, have missed something in my synthesis of the >information, or if there are any specific tests I should conduct to >check for potential bias, I'd be grateful for the input. > >Thank you, again, for your thoughtful (and helpful!) responses. > >Malitta Engstrom, Ph.D., L.C.S.W. >Assistant Professor >University of Chicago > >-----Original Message----- >From: Paul von Hippel [mailto:von-hippel.1@osu.edu] >Sent: Tuesday, August 02, 2005 2:09 PM >To: Gary King; Engstrom, Malitta >Cc: amelia(a)latte.harvard.edu >Subject: Re: [amelia] Commands for Imputing Dichotomous Variables in >AMELIA > >Here's a pertinent article by Horton et al: >http://www.biostat.harvard.edu/~horton/tasround.pdf >It shows that, when a normal model is used to impute a dichotomous >variable, it's best not to dichotomize the imputed values. > >Best -- >Paul > >At 03:04 PM 8/2/2005, Gary King wrote: > > >Experiments seem to indicate that for imputation, you can use the >normal > >model underlying Amelia, even though you wouldn't want to use it for >the > >analysis model. if you choose options appropriately, Amelia will > >dichotomize the resulting simulations for convenience of subsequent > >analysis. But if that's not necessary for your analysis model, then >using > >the continuously imputed variable should work fine. > > > >Gary King > > > >--- > >Gary King Institute for Quantitative Social Science > >Harvard University, 34 Kirkland St, Cambridge, MA 02138 > >http://GKing.Harvard.Edu, King(a)Harvard.Edu > >Direct 617-495-2027, Assistant 495-9271, eFax 812-8581 > > > >On Tue, 2 Aug 2005, Engstrom, Malitta wrote: > > > >>Dear Dr. King, > >> > >>I'm hoping you can provide some information, or direct me to a >resource > >>for gathering information, regarding the imputation of dichotomous > >>variables with AMELIA. Specifically, I'm wondering about how > >>problematic it is to use the "ANOMS" command with the imputation of > >>dichotomous variables. In statistical consultations, I've been >informed > >>that it should not be a problem to use this command; however, since >the > >>program directs one to use the "AMORDS" command with the imputation of > >>dichotomous variables, I wanted to gather additional information about > >>it. > >> > >>I would be most grateful for any information you can share on this > >>topic. > >> > >>Thank you, > >>Malitta Engstrom > >>Assistant Professor > >>University of Chicago > >>- > >>Amelia mailing list served by Harvard-MIT Data Center > >>[Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia > >- > >Amelia mailing list served by Harvard-MIT Data Center > >[Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia > > > >Paul von Hippel >Department of Sociology / Initiative in Population Research >Ohio State University >300 Bricker Hall >190 N. Oval Mall >Columbus OH 43210 >614 688-3768 >Office hours TThF 3-5pm >I read email every weekday at 3. Paul von Hippel Department of Sociology / Initiative in Population Research Ohio State University 300 Bricker Hall 190 N. Oval Mall Columbus OH 43210 614 688-3768 Office hours TThF 3-5pm I read email every weekday at 3. - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia

18 years, 8 months

RE: [amelia] Commands for Imputing Dichotomous Variables in AMELIA

by Engstrom, Malitta

Many, many thanks for this information. It sounds like experiments conducted specifically with AMELIA indicate that using the normal model underlying AMELIA to impute dichotomous variables (which will be rounded to 0 or 1) will not create biased estimates. The article by Horton, Lipsitz, and Parzen suggests that such action should be taken with caution as there may be a risk of biased estimates in certain situations when the imputed values are dichotomized. From my read of their article, it looks like: (1) higher rates of missing data may result in greater bias; (2) their demonstration of "a simpler model" may not apply to all situations; and (3) when given an option, it may be better to select a categorical variable which is not dichotomous for imputation using the normal model and rounding the variable to reduce possible bias. As you might have guessed, what prompted my question was the imputation of a dataset with more dichotomous variables than could be accommodated using the "AMORDS" command in AMELIA; thus it requires the use of the "ANOMS" command for some of the dichotomous variables. Given the experiments conducted specifically with AMELIA, it seems that using this second configuration should be okay. If there is any further information I should consider, have missed something in my synthesis of the information, or if there are any specific tests I should conduct to check for potential bias, I'd be grateful for the input. Thank you, again, for your thoughtful (and helpful!) responses. Malitta Engstrom, Ph.D., L.C.S.W. Assistant Professor University of Chicago -----Original Message----- From: Paul von Hippel [mailto:von-hippel.1@osu.edu] Sent: Tuesday, August 02, 2005 2:09 PM To: Gary King; Engstrom, Malitta Cc: amelia(a)latte.harvard.edu Subject: Re: [amelia] Commands for Imputing Dichotomous Variables in AMELIA Here's a pertinent article by Horton et al: http://www.biostat.harvard.edu/~horton/tasround.pdf It shows that, when a normal model is used to impute a dichotomous variable, it's best not to dichotomize the imputed values. Best -- Paul At 03:04 PM 8/2/2005, Gary King wrote: >Experiments seem to indicate that for imputation, you can use the normal >model underlying Amelia, even though you wouldn't want to use it for the >analysis model. if you choose options appropriately, Amelia will >dichotomize the resulting simulations for convenience of subsequent >analysis. But if that's not necessary for your analysis model, then using >the continuously imputed variable should work fine. > >Gary King > >--- >Gary King Institute for Quantitative Social Science >Harvard University, 34 Kirkland St, Cambridge, MA 02138 >http://GKing.Harvard.Edu, King(a)Harvard.Edu >Direct 617-495-2027, Assistant 495-9271, eFax 812-8581 > >On Tue, 2 Aug 2005, Engstrom, Malitta wrote: > >>Dear Dr. King, >> >>I'm hoping you can provide some information, or direct me to a resource >>for gathering information, regarding the imputation of dichotomous >>variables with AMELIA. Specifically, I'm wondering about how >>problematic it is to use the "ANOMS" command with the imputation of >>dichotomous variables. In statistical consultations, I've been informed >>that it should not be a problem to use this command; however, since the >>program directs one to use the "AMORDS" command with the imputation of >>dichotomous variables, I wanted to gather additional information about >>it. >> >>I would be most grateful for any information you can share on this >>topic. >> >>Thank you, >>Malitta Engstrom >>Assistant Professor >>University of Chicago >>- >>Amelia mailing list served by Harvard-MIT Data Center >>[Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia >- >Amelia mailing list served by Harvard-MIT Data Center >[Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia > Paul von Hippel Department of Sociology / Initiative in Population Research Ohio State University 300 Bricker Hall 190 N. Oval Mall Columbus OH 43210 614 688-3768 Office hours TThF 3-5pm I read email every weekday at 3. - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia

18 years, 8 months

Commands for Imputing Dichotomous Variables in AMELIA

by Engstrom, Malitta

Dear Dr. King, I'm hoping you can provide some information, or direct me to a resource for gathering information, regarding the imputation of dichotomous variables with AMELIA. Specifically, I'm wondering about how problematic it is to use the "ANOMS" command with the imputation of dichotomous variables. In statistical consultations, I've been informed that it should not be a problem to use this command; however, since the program directs one to use the "AMORDS" command with the imputation of dichotomous variables, I wanted to gather additional information about it. I would be most grateful for any information you can share on this topic. Thank you, Malitta Engstrom Assistant Professor University of Chicago - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.hmdc.harvard.edu/?info=amelia

18 years, 8 months

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Amelia August 2005