CODEWITHSUNDEEP
spssmixed-models
Soeng
Soeng

Reputation: 7

Generalized Linear Mixed Model in SPSS

I'm doing a generalized linear mixed model with SPSS.

Outcome: Wellbeing ("MmDWohlbefinden"), Fixed effects: Intervention (Pre/Post), Symptoms when intervention was applied (depression, apathy, aggression/irritable, restless, nothing) ("BPSD"), intervention*symptoms, time ("Zeit"), Random effects: Individuals (repeated measure)

In SPSS it is possible to choose the order of input categories "ascending" and "descending" to change the reference category.

My question: Why is the intervention effect significant when comparing pre intervention to reference category post, but not significant when comparing it the other way around (post intervention to reference category pre)?

This also occurs with the fixed effect "symptoms". The symptom "depressive" has not significant effect on the wellbeing compared to "nothing", "nothing" on the other hand has a significant effect on the wellbeing compared to "depressive".

These are my codes:

Ascending:

GENLINMIXED
  /FIELDS TARGET=MmDWohlbefinden TRIALS=NONE OFFSET=NONE
  /TARGET_OPTIONS DISTRIBUTION=POISSON LINK=IDENTITY
  /FIXED  EFFECTS=Intervention Zeit BPSD Intervention*BPSD USE_INTERCEPT=TRUE
  /RANDOM EFFECTS=ID USE_INTERCEPT=FALSE COVARIANCE_TYPE=VARIANCE_COMPONENTS SOLUTION=FALSE
  /BUILD_OPTIONS TARGET_CATEGORY_ORDER=ASCENDING INPUTS_CATEGORY_ORDER=ASCENDING MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=RESIDUAL COVB=MODEL PCONVERGE=0.000001(ABSOLUTE) SCORING=0 SINGULAR=0.000000000001
  /EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=LSD.

Descending:

GENLINMIXED
  /FIELDS TARGET=MmDWohlbefinden TRIALS=NONE OFFSET=NONE
  /TARGET_OPTIONS DISTRIBUTION=POISSON LINK=IDENTITY
  /FIXED  EFFECTS=Intervention Zeit BPSD Intervention*BPSD USE_INTERCEPT=TRUE
  /RANDOM EFFECTS=ID USE_INTERCEPT=FALSE COVARIANCE_TYPE=VARIANCE_COMPONENTS SOLUTION=FALSE
  /BUILD_OPTIONS TARGET_CATEGORY_ORDER=ASCENDING INPUTS_CATEGORY_ORDER=DESCENDING MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=RESIDUAL COVB=MODEL PCONVERGE=0.000001(ABSOLUTE) SCORING=0 SINGULAR=0.000000000001
  /EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=LSD.

Thank you!

Upvotes: 0

Views: 1326

Answers (1)

David Nichols
David Nichols

Reputation: 586

When you have a model that involves interaction effects among factors, the parameter estimates for the factors contained in the interactions produce contrasts among the levels of factors nested within the left out categories of the other factors, given the indicator parameterization used in GENLINMIXED and most other more recent SPSS Statistics procedures.

With the INPUTS_CATEGORY_ORDER=ASCENDING default on the BUILD_OPTIONS subcommand, the Intercept gives the predicted value for the (2,4) cell of your 2x4 design (with the covariate set to its mean). The Intervention "main effect" estimate that's not redundant and aliased to 0 gives the first level of Intervention minus the second level, nested at the last level of the BPSD factor, which is the (1,4) cell minus the (2,4) cell. The estimates for the BPSD factor are comparing each level to the last, nested at the second level of Intervention, so they're (2,1) minus (2,4), (2,2) minus (2,4), and (2,3) minus (2,4).

With the INPUTS_CATEGORY_ORDER=DESCENDING option, you change which category of each factor is last, so the de facto reference category is different in this case. The comparisons among cells are among the same cells for the new ordering, but are different in terms of the original ordering, giving results that are different based not just on the left out category of the factor in question, but also on the left out category of the other factor. The Intercept estimate gives the prediction for the original (1,1) cell. The non-redundant Intervention estimate gives (2,1) minus (1,1). The non-redundant estimates for the BPSD factor give (1,4) minus (1,1), (1,3) minus (1,1), and (1,2) minus (1,1), respectively.

Upvotes: 0

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