Amelia seems to work fine for imputing ordinal variables. Amelia II now
has special features for time series cross-sectional data and so if you
your hierarchies fit that framework, and likely it does, then it should
work fine for you. there are no stata clustering type options, but that
shouldn't matter for imputation so long as you include the variables
that form the sampling strata in the imputation procedure.
Gary
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http://gking.harvard.edu
Myers, Nicholas wrote:
Hello.
A colleague has suggested that Amelia can be used to address the missing data problem
described below. I just subscribed to Amelia list – so please let me know if there is a
protocol in to posting that I am unaware of.
I am hoping that folks on the list are willing to
1. Let me know if they believe Amelia can be used to address this problem
2. Point me in the right direction as to where I can read about Amelia’s approach to this
problem
I write looking for recommended resources/references on imputing for missing values of
ordinal data at Level 1 (taking clustering into account). Responses on or off list will be
appreciated.
The relevant data are nested (two levels). There are no missing data at Level 2 (number
of groups = 32). The missing data at Level 1 are from ordinal variables (i.e., 32 Likert
scale variables with 5 ordered categories). Within said variables there is not much
missingness (i.e., no more than 5%). A multidimensional measurement model that accounts
for the ordinal nature of the data (probit) will be imposed across the imputed datasets
(all 32 variables).
Thank you.
nick myers
-----------------------------------------------------------------------------------------------------------
Nicholas D. Myers, Ph.D.
University of Miami
School of Education
Department of Educational and Psychological Studies
Research, Measurement and Evaluation program
Merrick Building 311E
P.O. Box 248065
Coral Gables, FL 33124-2040
Tel: 305.284.9803
Fax: 305.284.3003
E-mail: nmyers(a)miami.edu
http://www.education.miami.edu/facultyStaff/Faculty_Bio.asp?ID=143
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