# SPSS Statistics

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## Linear mixed models - interpretation of results

• #### 1.  Linear mixed models - interpretation of results

Posted Mon November 08, 2021 09:59 AM
Hello, I have a mixed model wiith one categorical and one continuous predictor. When I input the continuous one as a fixed effect, both the "tests of fixed effects" and "estimates of fixed effects" table gives significant results for the continuous variable. When I input it as a covariate, suddenly there is a discrepancy between the tables - in "tests of fixed effects" it remains significant, while in "estimates of fixed effects" it becomes insignificant.
This is strange for me. If a continuous variable cannot be a fixed factor (as I understood from reading the web) - why do these results differ in both tables??
Also - there is one more difference between these two analyses - the categorical variable's estimate changes the sign. What statistics should I report to follow APA style?

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Karolina Ziembowicz
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• #### 2.  RE: Linear mixed models - interpretation of results

Posted Mon November 08, 2021 11:25 AM
If you treat a continuous variable as a factor, you are giving it the number of degrees of freedom as the number of levels (ignoring the constant term).  So that allows for a nonlinear relationship between the presumed factor and the dependent variable while as a covariate it would only pick up a linear relationship with one degree of freedom.

In other words, treating it as a factor is equivalent to creating a dummy variable for each level.

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• #### 3.  RE: Linear mixed models - interpretation of results

Posted Mon November 08, 2021 11:50 AM
Ok, I get it. Thanks for the nice clarification. So - are you saying I should not worry that the estimate and t-test are insignificant in the "tests of fixed effects" table? Should I report the value of F and ignore the "estimates of fixed effects" table?

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Karolina Ziembowicz
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• #### 4.  RE: Linear mixed models - interpretation of results

Posted Mon November 08, 2021 04:08 PM
I am not sure which procedure you are running.  Are you backtranslating table names from another language?

But if you get different significance results as a factor from as a covariate, that might suggest a nonlinearity, so you might want to try adding a quadratic term as a covariate or fitting a cubic spline, which you could create with the STATS  SPLINES extension command.

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• #### 5.  RE: Linear mixed models - interpretation of results

Posted Tue November 09, 2021 04:57 AM
Hello, I tried doing as you suggested (added a cubic term as a covariate), it did not help. I am using the linear mixed models procedure and I am not back translating. I attach the output in which I conduct the normal procedure first and then go on with the cubic term. The covariate is indeed not normal, I used LN function which normalized the distribution but it also gives the same combination of significant/insignificant results between tables.
Any other ideas? I'm pretty confused.

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Karolina Ziembowicz
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Attachment(s)

output_SPSS.pdf   34 KB 1 version

• #### 6.  RE: Linear mixed models - interpretation of results

Posted Tue November 09, 2021 09:02 AM
How very strange - I recoded my fixed factor from 01 into 10 (just swapped levels) and suddenly the tables give coherent results.

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Karolina Ziembowicz
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• #### 7.  RE: Linear mixed models - interpretation of results

Posted Tue November 09, 2021 09:11 AM
I see that you are using MIXED, but the message is the same as with other procedures that support factors and covariates.  In your case, this is more  complicated, because you have an interaction term in the model as well as the main effects.  You cannot judge the effect of a variable just from the estimates of individual fixed effects terms.  If you need more help, I would refer you to the Linear Mixed Models Case Study available in the help system.