CODEWITHSUNDEEP
warningsspssmixed-models
user12837970
user12837970

Reputation: 1

Warning appears in mixed effect model using spss

I am using spss to conduct mixed effect model of the following project:  The participant is being asked some open ended questions and their answers are recorded.

For example, if the participant's answer is related to equality, the variable "equality" is coded as "1". Otherwise, it is coded as "0". Therefore, dependent variable is the variable "equality".

Fixed effects: 
- participant's country (Asians vs. Westerners)
- gender (Male vs Female)
- age group (younger age group vs. older age group)
- condition (control group vs. intervention group)

Random effect: Subject ID (participants)

Sample size: over 600 participants

My syntax in spss: 

MIXED  Equality BY Country Gender AgeGroup Condition 
/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)
/FIXED= Country Gender AgeGroup Condition  | SSTYPE(3)
/METHOD=ML
/PRINT=SOLUTION TESTCOV
/RANDOM=INTERCEPT | SUBJECT(SubID_R) COVTYPE(VC).

When running this analysis in spss, the following warning appears: 

Iteration was terminated but convergence has not been achieved.

The MIXED procedure continues despite this warning. Subsequent results produced are based on the last iteration. Validity of the model fit is uncertain.    

I try to increase the number of "MXSTEP" from 10 to 10000 in syntax, but another warning appear: 

The final Hessian matrix is not positive definite although all convergence criteria are satisfied.

The MIXED procedure continues despite this warning. Validity of subsequent results cannot be ascertained.  

I also try to increase the number of  "MXITER" but the warning remains. May I ask how to deal with this problem to get rid of the warning? 

Upvotes: 0

Views: 2832

Answers (1)

David Nichols
David Nichols

Reputation: 586

Aside from what you've already tried, in some cases increasing the number of Fisher scoring steps can be helpful, but it may be the case that your random intercept variance is truly redundant and you won't be able to resolve this problem with those data and that model.

Also, typically you would not use a linear model for a binary response variable, but would use something like a logistic model (this can be done in GENLINMIXED, under Analyze>Mixed Models>Generalized Linear in the menus).

Upvotes: 0

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