H0 imputation with Bayes estimator PreviousNext
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 Ewan Carr posted on Friday, September 13, 2013 - 6:52 am
I'm estimating a two-level random slope model with the Bayes estimator. I have missing data on one independent variable.

I'm trying to impute this variable using the H0 approach.

1) Am I correct in understanding that, if I use the H0 approach, I can't use the BAYES estimator in the subsequent analysis model?


quote:

ESTIMATOR=BAYES is not available with TYPE=IMPUTATION in the DATA command.




I want/need to use Bayesian estimation (due to small level-2 sample size; 22). But I also need to impute the missing values.

2) Is this impossible in Mplus?

3) Are there any alternative approaches to imputing the data that would allow Bayesian estimation? (e.g. using plausible values)

Many thanks,

Ewan
--
 Bengt O. Muthen posted on Friday, September 13, 2013 - 5:43 pm
You should include your independent variable in the model by mentioning its mean or variance parameter. Then Bayes can be used.
 Ewan Carr posted on Monday, September 16, 2013 - 2:33 am
Thanks for your reply. However, I'm not sure I understand.

My imputation model looks like:


quote:

TITLE:
DATA: file = ess5.dat;
VARIABLE:
[...]
usevariables = satlife sec1 income;
cluster = cntry2;
within = income sec1;

ANALYSIS:
type = twolevel random;
estimator = bayes;
processors = 2;

MODEL:

%WITHIN%
s | satlife ON sec1;
[income];

%BETWEEN%
satlife;
s;

DATA IMPUTATION:
impute = income;
ndatasets = 5;
save = H0_impute*.dat;




And the analysis model looks like:


quote:

TITLE:
DATA:
file = H0_imputelist.dat;
type = IMPUTATION;
VARIABLE:
names = SEC1 SATLIFE INCOME CNTRY2;
missing = *;
usevariables = all;
cluster = cntry2;
within = income sec1;

ANALYSIS:
type = twolevel random;
estimator = bayes;

MODEL:

%WITHIN%
s | satlife ON sec1;
satlife ON income;

%BETWEEN%
satlife;
s;




I still get the error: "BAYES not available with TYPE=IMPUTATION".

Any idea where I'm going wrong?

Many thanks.
 Linda K. Muthen posted on Monday, September 16, 2013 - 11:21 am
Following is what Bengt suggested. You cannot do multiple imputation and use Bayes. Bayes is essentially doing multiple imputation behind the scene.


TITLE:
DATA:
DATA: file = ess5.dat;
VARIABLE:
names = SEC1 SATLIFE INCOME CNTRY2;
missing = *;
usevariables = all;
cluster = cntry2;
within = income sec1;

ANALYSIS:
type = twolevel random;
estimator = bayes;

MODEL:

%WITHIN%
s | satlife ON sec1;
satlife ON income;

%BETWEEN%
satlife;
s;
 Linda K. Muthen posted on Monday, September 16, 2013 - 11:23 am
Also, add the variance of income to the MODEL command to avoid losing cases with missing on income, for example,

%WITHIN%
s | satlife ON sec1;
satlife ON income;
income;
 Ewan Carr posted on Tuesday, September 17, 2013 - 2:35 am
Thank you Linda and Bengt that worked perfectly.

One final question: what should I call this method? I'm responding to reviewer comments who have recommended multiple imputation. I'm wondering how I should explain the above technique.

If Bayes is doing "multiple imputation behind the scenes" then is this multiple imputation?

Many thanks for your help with this,

Ewan
--
 Linda K. Muthen posted on Tuesday, September 17, 2013 - 12:27 pm
I don't think this has a name.

No, Bayes takes an approach similar to FIML.
 Ewan Carr posted on Thursday, September 19, 2013 - 9:33 am
OK great, that's very helpful.

So when explaining what I've done about missing data I'll refer readers to the FIML literature.

Many thanks,

Ewan.
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