Does anyone know how to calculate the effect sizes for fixed effects in a linear mixed model that's processed in SPSS (not GLMM)?
For the fixed part - I would simply use the estimated fixed part terms - they are in the metric of the response variable. I typically draw graphs with predicted response on the vertical axis (same scale for all) and a separate graph for each predictor variable over the range of the predictor (deciles for continuous; 0 and 1 for dummies) and also plot (say) the 95% confidence interval around the partial line which I think is a helpful visualization about the comparative size of effect.
You can see the debate around this and appreciate why I do not like standardized regression coefficients
https://www.researchgate.net/post/How_to_calculate_the_effect_size_in_multiple_linear_regression_analysis
and
https://www.researchgate.net/post/How_can_I_calculate_an_effect_size_cohens_d_preferably_from_a_linear_random_effects_model_beta
Here is a resource with multiple effect size metrics for LMMs: http://web.pdx.edu/~newsomj/mlrclass/ho_r2.pdf
Cort
the attachment is about pseudo R-squared measures for continuous outcome multilevel models - is that what you intended?
Maybe can this help you ? http://www.spss.ch/upload/1126184451_Linear%20Mixed%20Effects%20Modeling%20in%20SPSS.pdf
Thanks all for the kind suggestions, I've decided to go with Prof Jones's advise.
I've got another question regarding reporting of F statistics and contrasts statistics.
Hypothetically in a typical ANOVA, we'll write F(X,Y) = 11, where X is d.f of the effect and Y is d.f of the error.
In a mixed model, what is value Y if I'm going to report on fixed effect B in text - F(2,Y) = 11.355, p < 0.001. Also if interaction of A*B is significant, how do I report the statistics for its subsequent contrasts as well?
The model dimension of my data (from SPSS) is listed below.
Number of levels
Fixed Effects Intercept 1
A 2
B 2
C 6
A*B 4
Random Effects Intercept 1
Repeated Effects B 2
Total 18
The effect sizes are estimated based on the Estimates of Covariance Parameters in the SPSS output. Variances between old/new models should be compared in the intercepts and here is the basic formula:
Variance in the old model - Variance in the new model / Variance in the old model.
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