Sounds like you are using genlinmixed. I haven't used this for a while but what a beast! I think the first message, which is a warning but not an error, is because your DV has missing data. I think that if those cases are excluded that warning will disappear. I also think the second message is related to missing data as well.

Gene Maguin

-----Original Message-----

From: SPSSX(r) Discussion [mailto:

[hidden email]] On Behalf Of Elisabeth

Sent: Monday, July 27, 2020 10:06 AM

To:

[hidden email]
Subject: Warnings concerning a multilevel analysis with dichotomous outcomes

Hello :)

Actually I'm doing my Master thesis and therefor I'm running a multilevel analysis with clustered data, but actually I get some warnings.

I have 103 participants and each of them answered up to 21 questionnaires (3 times daily). I have some missing data, but I ordered the data in the vertical format, so I'm loosing around 10% of the measurements.

If I run the null model for a binary outcome (nominal scale; 0,1 format), to check what proportion of the variance is caused by clustering of the data, the model is running but I get two warnings, and I'm not sure if I can ignore them.

I get the following warnings:

glmm: One or more records are not used in the analysis because they have one or more fields with invalid or missing values.

glmm: Valid values for events (target) and trials variables are non-negative and positive integers respectively, and the number of trials cannot be less than the number of events.

If I rerun the model after adding predictors the same warnings are displayed but the analysis is nevertheless running..

I use the following syntax for my model:

GENLINMIXED

/DATA_STRUCTURE SUBJECTS=Person REPEATED_MEASURES=MP_timec

COVARIANCE_TYPE=AR1

/FIELDS TARGET=ND_Fortschritt_GMc_Cutoff TRIALS=NONE OFFSET=NONE

/TARGET_OPTIONS DISTRIBUTION=BINOMIAL LINK=LOGIT

/FIXED USE_INTERCEPT=TRUE

/RANDOM USE_INTERCEPT=TRUE SUBJECTS=Person COVARIANCE_TYPE=VARIANCE_COMPONENTS SOLUTION=FALSE

/BUILD_OPTIONS TARGET_CATEGORY_ORDER=DESCENDING INPUTS_CATEGORY_ORDER=DESCENDING

MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=RESIDUAL COVB=ROBUST

PCONVERGE=0.000001(ABSOLUTE)

SCORING=0 SINGULAR=0.000000000001

/EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=LSD.

I would be very grateful if somebody could help me with my question.

Thanks a lot!

Elisabeth

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