How do I calculate effect size for mixed model regression (run using MIXED in SPSS) when there's only one group? So I can report the effect of time?

Does anyone know how to calculate the effect sizes for fixed effects in a linear mixed model that's processed in SPSS (not GLMM)?

Hello,

I am working on a meta-analysis. I have two journal articles I want to include. Unfortunately, they only report F statistics (e.g. F(2, 33) = 4.08). I know how to calculate cohen's d from a one-way ANOVA, but I can't find any information on whether or not it is possible to calculate effect size from just the F statistic and degrees of freedom if it is more than a one-way ANOVA. Is it possible to do this?

Thank you in advance for your help.

Hi all,

I have been advised to run a linear mixed model to analyse my data given the uneven nature of the data but keep receiving the following error message and am unsure why this is.

"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"

In short, I have a within subjects repeated measures design whereby subjects undertake exercise under 3 different dietary conditions. In one condition, I have data for 2 time points and in the other 2 conditions I have data for 3 time points. This was chosen by design to allow us to compare the responses at the same time point during exercise but also at a separate time point in the two other conditions.

If anyone could help or direct me to any relevant resources it would be much appreciated.

More specifically, in my RM ANOVA, both time (pre vs post) and group (intervention vs control) have 2 levels. I am interested in whether the marginal mean of control_pre is significantly different than intervention_post for a particular DV. I know there must be a way to accomplish this within SPSS syntax...

Thank you so much!

Dear all,

After having researched this question for days (ResearchGate, books, youtube, the general WWW....), I'm none the the wiser, even though it's probably an easy question. I'm new to linear mixed models, so my understanding is still very limited.

I'm running a 2-level linear mixed model in SPSS, where participants' search behavior in 2 different decision domains is nested within each individual (you could also imagine it as a repeated measure). I use a random intercept, and random effects for subjects. Other than that, I'm looking at age group (young, old), experimental condition (2 different types of information),health status (healthy, sick), and of course decision domain (health-related, not health-related) as "predictors" / factors. The DV is the amount of information participants have reviewed.

I have run my model but now I don't know how to interpret and report the results. It seems some sources I have consulted look at the Type III Test for Fixed Effects, which offers omnibus tests (F statistic) for main effects and interactions. I assume you can report these in writing, like I would with an ANOVA. However, some other people only report a table with Estimates for Fixed Effects, which offers t-tests. Some of the results in these tables "conflict" (maybe they don't, I just don't understand the difference yet), so I am unsure what to rely on and whether to report both.

To provide an example: My hypothesis was, among other things, that information seeking does not differ between age groups (i.e., no main effect). The F-test supports this, saying there is no overall effect of age group on information seeking. However, the estimates table lists age group as significant at p = .009, with information seeking as DV. The same is true for a couple more effects and interactions. Now I am very confused. There are not a lot of videos or texts covering both. One summary I found that addressed both types of tables did not provide insight into what to do if results differ. (Some people on ResearchGate and elsewhere also seem to refer to this t-test table as pairwise comparisons. If that is the case, does that mean that while age groups were significantly different in their search behavior when compared to each other, search behavior did not rely on age?)

I would be grateful for whatever insight you can share! Thank you.

I performed a multiple linear regression analysis with 1 continuous and 8 dummy variables as predictors. The analysis revealed 2 dummy variables that has a significant relationship with the DV. I need to know the practical significance of these two dummy variables to the DV. That is, I want to know the strength of relationship that existed. I was told that effect size can show this. How can I compute for the effect size, considering that i have both continuous and dummy IVs? Thanks in advance. - Jonas

This study involves a task that measures arousal during a task with three trials of increasing cognitive complexity.  In the last trial, all subjects score the same in terms of performance though demonstrate differing levels of arousal.

1) Because I am a novice when it comes to reporting the results of a linear mixed models analysis, how do I report the fixed effect, including including the estimate, confidence interval, and p-value in addition to the size of the random effects. I am not sure how to report these in writing. For example, how do I report the confidence interval in APA format and how do I report the size of the random effects?

2) How do you determine the significance of the size of the random effects (i.e. how do you determine if the size of the random effects is too large and how do you determine the implications of that size)?

3) Our study consisted of 16 participants, 8 of which were assigned a technology with a privacy setting and 8 of which were not assigned a technology with a privacy setting. Survey data was collected weekly. Our fixed effect was whether or not participants were assigned the technology. Our random effects were week (for the 8-week study) and participant. How do I justify using a linear mixed model for this study design? Is it accurate to say that we used a linear mixed model to account for missing data (i.e. non-response; technology issues) and participant-level effects (i.e. how frequently each participant used the technology; differences in technology experience; high variability in each individual participant's responses to survey questions across the 8-week period). Is this a sufficient justification? 

I am very new to mixed models analyses, and I would appreciate some guidance. 

Hello, I am trying to evaluate the group differences of the change from baseline in an EEG index (fMMN_amplitude), using the Linear Mixed Models process. I entered Cluster (three groups), time (two visits), Cluster × time interaction as fixed effects, baseline MMN_amplitude value as a covariate, and participant as a random effect for the intercept. I used EMMEANS to obtain the change from baseline of MMN_amplitude in each Cluster. Picture 1 is the command lines I used.

However, I could not find a way to compare the change values between groups. Can someone please let me know if there is a way to acquire such group differences and the effect sizes, just like the mean difference versus placebo in the Picture 2 (from DOI: 10.1016/S2215-0366(20)30513-7). It's like computing [(g2t2 - g2t1) - (g1t2 - g1t1)]. SPSS command lines will be best, GUI operations are also fine. Any help will be appreciated!!

Sincerely,

Greatson Wu