Posted by
Maguin, Eugene on
May 10, 2013; 1:01pm
URL: http://spssx-discussion.165.s1.nabble.com/Multi-Level-Modeling-Imputation-Concerns-tp5720153p5720164.html
Bruce, thanks. I didn’t know that existed and I see that it is reachable from the spss community link in the help drop-down.
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of Bruce Weaver
Sent: Thursday, May 09, 2013 9:47 PM
To:
[hidden email]
Subject: Re: Multi-Level Modeling/Imputation Concerns
From
http://publib.boulder.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Fmi_analysis.htm:Procedures That Support Pooling
The following procedures support MI datasets, at the levels of pooling specified for each piece of output.
Frequencies
• The Statistics table supports Means at Univariate pooling (if S.E. mean is also requested) and Valid N and Missing N at Naïve pooling.
• The Frequencies table supports Frequency at Naïve pooling.
Descriptives
• The Descriptive Statistics table supports Means at Univariate pooling (if S.E. mean is also requested) and N at Naïve pooling.
Crosstabs
• The Crosstabulation table supports Count at Naïve pooling.
Means
• The Report table supports Mean at Univariate pooling (if S.E. mean is also
requested) and N at Naïve pooling.
One-Sample T Test
• The Statistics table supports Mean at Univariate pooling and N at Naïve pooling.
• The Test table supports Mean Difference at Univariate pooling.
Independent-Samples T Test
• The Group Statistics table supports Means at Univariate pooling and N at Naïve pooling.
• The Test table supports Mean Difference at Univariate pooling.
Paired-Samples T Test
• The Statistics table supports Means at Univariate pooling and N at Naïve pooling.
• The Correlations table supports Correlations and N at Naïve pooling.
• The Test table supports Mean at Univariate pooling.
One-Way ANOVA
• The Descriptive Statistics table supports Mean at Univariate pooling and N at Naïve pooling.
• The Contrast Tests table supports Value of Contrast at Univariate pooling.
Linear Mixed Models
• The Descriptive Statistics table supports Mean and N at Naïve pooling.
• The Estimates of Fixed Effects table supports Estimate at Univariate pooling.
• The Estimates of Covariance Parameters table supports Estimate at Univariate pooling.
• The Estimated Marginal Means: Estimates table supports Mean at Univariate pooling.
• The Estimated Marginal Means: Pairwise Comparisons table supports Mean Difference at Univariate pooling.
Generalized Linear Models and Generalized Estimating Equations. These procedures support pooled PMML.
• The Categorical Variable Information table supports N and Percents at Naïve pooling.
• The Continuous Variable Information table supports N and Mean at Naïve pooling.
• The Parameter Estimates table supports the coefficient, B, at Univariate pooling.
• The Estimated Marginal Means: Estimation Coefficients table supports Mean at Naïve pooling.
• The Estimated Marginal Means: Estimates table supports Mean at Univariate pooling.
• The Estimated Marginal Means: Pairwise Comparisons table supports Mean Difference at Univariate pooling.
Bivariate Correlations
• The Descriptive Statistics table supports Mean and N at Naïve pooling.
• The Correlations table supports Correlations and N at Univariate pooling.
Note that correlations are transformed using Fisher's z transformation before pooling, and then backtransformed after pooling.
Partial Correlations
• The Descriptive Statistics table supports Mean and N at Naïve pooling.
• The Correlations table supports Correlations at Naïve pooling.
Linear Regression. This procedure supports pooled PMML.
• The Descriptive Statistics table supports Mean and N at Naïve pooling.
• The Correlations table supports Correlations and N at Naïve pooling.
• The Coefficients table supports B at Univariate pooling and Correlations at Naïve pooling.
• The Correlation Coefficients table supports Correlations at Naïve pooling.
• The Residuals Statistics table supports Mean and N at Naïve pooling.
Binary Logistic Regression. This procedure supports pooled PMML.
• The Variables in the Equation table supports B at Univariate pooling.
Multinomial Logistic Regression. This procedure supports pooled PMML.
• The Parameter Estimates table supports the coefficient, B, at Univariate pooling.
Ordinal Regression
• The Parameter Estimates table supports the coefficient, B, at Univariate pooling.
Discriminant Analysis. This procedure supports pooled model XML.
• The Group Statistics table supports Mean and Valid N at Naïve pooling.
• The Pooled Within-Groups Matrices table supports Correlations at Naïve pooling.
• The Canonical Discriminant Function Coefficients table supports Unstandardized Coefficients at Naïve pooling.
• The Functions at Group Centroids table supports Unstandardized Coefficients at Naïve pooling.
• The Classification Function Coefficients table supports Coefficients at Naïve pooling.
Chi-Square Test
• The Descriptives table supports Mean and N at Naïve pooling.
• The Frequencies table supports Observed N at Naïve pooling.
Binomial Test
• The Descriptives table supports Means and N at Naïve pooling.
• The Test table supports N, Observed Proportion, and Test Proportion at Naïve pooling.
Runs Test
• The Descriptives table supports Means and N at Naïve pooling.
One-Sample Kolmogorov-Smirnov Test
• The Descriptives table supports Means and N at Naïve pooling.
Two-Independent-Samples Tests
• The Ranks table supports Mean Rank and N at Naïve pooling.
• The Frequencies table supports N at Naïve pooling.
Tests for Several Independent Samples
• The Ranks table supports Mean Rank and N at Naïve pooling.
• The Frequencies table supports Counts at Naïve pooling.
Two-Related-Samples Tests
• The Ranks table supports Mean Rank and N at Naïve pooling.
• The Frequencies table supports N at Naïve pooling.
Tests for Several Related Samples
• The Ranks table supports Mean Rank at Naïve pooling.
Cox Regression. This procedure supports pooled PMML.
• The Variables in the Equation table supports B at Univariate pooling.
• The Covariate Means table supports Mean at Naïve pooling.
Maguin, Eugene wrote
> Thank you for telling us that information. But you said you imputed
> values for several variables. I missed seeing that you said in your
> original message how many imputations you did. I've never used spss's
> imputation facility because I have mplus. You've got to be running
> genlinmixed. That said, I wonder if genlinmixed will work with imputed
> datasets. I'd guess that somebody on the list has run genlinmixed with imputed datasets.
> Perhaps they will respond. I'd expect that you could run a one level
> multiple regression model using your dataset. You could also run a one
> level ordinal regression model with genlinmixed. But, I wonder if you
> could run a model with mixed. Be interesting to know if the problem is
> with multilevel analyses, in general, or with genlinmixed, specifically.
> Gene Maguin
>
>
>
>
> -----Original Message-----
> From: SPSSX(r) Discussion [mailto:
> SPSSX-L@.UGA
> ] On Behalf Of jlukewood
> Sent: Thursday, May 09, 2013 7:55 PM
> To:
> SPSSX-L@.UGA
> Subject: Re: Multi-Level Modeling/Imputation Concerns
>
> The dataset has 9,000+ cases nested in 240(ish) institutions. The
> model is complex, four primary scales and numerous controls.
> Missingness was only a problem in one scale. I had five variables
> (part of the problematic scale), missingness on these variables ranged
> from 10.7% to 25.0%. I did imputation.
>
>
>
> --
> View this message in context:
>
http://spssx-discussion.1045642.n5.nabble.com/Multi-Level-Modeling-Imp> utation-Concerns-tp5720153p5720156.html
> Sent from the SPSSX Discussion mailing list archive at Nabble.com.
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--
Bruce Weaver
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View this message in context:
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=====================
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