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I am using a mixed-Anova with one dependent variable measured across two time points (within-subject factor). There are two between-subject factors, between1 & between2, with items equally split across their respective levels.

Results indicate a significant within-subjects effect for the DV * between1, which is comprised of three groups: A, B, C.

Post hoc tests (Tukey's and Bonferroni) indicate significant effect for group A * group B:

enter image description here

However, plots of the estimated marginal means show a greater difference in slope steepness between group C (green) and the others, from time point 1 to time point 2:

enter image description here

Since I am measuring change in DV over time between these three groups, and if the plots are also representing this, then it seems there is a contradiction here.

If the slope of group C is so steep, then the DV has changed to a greater extent relative to groups A and B. If that is the case, given that the groups are all balanced, why would the post-hoc tests find a significant difference between groups A and B instead?

I guess I'm conceptually confused about something here, and it's probably a simple answer. Thanks in advance.

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    $\begingroup$ I see no contradiction in your results so far. "significant within-subjects effect for the DV * between1 [interaction]" corresponds to the steeper slope of one of the lines (the green) on your plot. While the fact that the blue and the red lines are far away from each other corresponds to your post hoc table. $\endgroup$ – ttnphns Nov 6 '20 at 11:41
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    $\begingroup$ No, post-hoc tests are only for levels of a between-subject factor. Not for within-subject factor, nor for interactions. To get means and tests between levels of the within subj factor go to "EM means" button and select Compare main effects for the factor(s) you want. $\endgroup$ – ttnphns Nov 6 '20 at 12:29
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    $\begingroup$ @remnant what you are thinking about with the change in the slope is the interaction effect. Those tables with post-hoc tests are probably for the main effect. $\endgroup$ – Sextus Empiricus Nov 7 '20 at 21:42
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    $\begingroup$ @remnant I am not so good in SPSS so I can't really answer this question fully. I guess that the interaction effect should be in the output, but maybe you did not specify the interaction effect as part of the model? I do not know exactly how this works for SPSS and ANOVA. I find it more intuitive to look at it as a (mixed effects) linear model (with the subject being the random effect). $\endgroup$ – Sextus Empiricus Nov 9 '20 at 8:13
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    $\begingroup$ Btw, looking at the graph, I would not be expecting that the interaction effect is gonna be significant (because the difference that the effect makes looks smaller than the difference due to the contrasts A-C and B-C, which is already not significant). .... This doesn't mean the effect is not present, it means that the effect is smaller than the noise levels of your experiment. $\endgroup$ – Sextus Empiricus Nov 9 '20 at 8:19
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As noted in the comments, POSTHOC results are for main effects for between-subjects factors. That averages over the levels of the within-subjects factor, so to see what in that chart is being compared, look at where the lines for each level are in the middle of the chart (halfway between the two plotted values for each group). A and B are indeed much further apart than either is from C.

If you have an interaction effect, you can explore it using interaction contrasts (using LMATRIX and MMATRIX subcommands), and/or you can look at simple main effects of one or both factor within fixed levels of the other factor, using EMMEANS with COMPARE, again requiring use of syntax in current versions, where you specify the interaction effect in the TABLES part and only one factor in thee COMPARE part. You can use two subcommands, one specifying each factor, to look at groups within time points and time comparisons within groups.

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