Stats Apps Tutorials: 20. Repeated measures ANOVA, 2-way ANOVA, and mixed models in JASP
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- Опубликовано: 15 июл 2024
- Links to video sections and data files are in the description below. In this tutorial video we go through the steps to perform repeated measures analysis of variance (ANOVA), 2-way factorial ANOVA, and Mixed ANOVA using JASP.
Data files used in this video:
CoreTemp_rm_wide.csv:
www.mediafire.com/file/wyj6d7...
BeerGoggles.csv
www.mediafire.com/file/fmbt1y...
CoreTemp_mixed_wide.csv
www.mediafire.com/file/6ldmdo...
Sections:
00:00 Introduction
00:15 Repeated Measures ANOVA in JASP
02:22 Assumption of Sphericity and corrections
03:23 Plotting the data and post-hoc tests
05:03 Two-way ANOVA
08:01 Plotting two-way ANOVA and post-hoc tests
10:55 Mixed ANOVA in JASP
12:53 Plotting mixed ANOVA and post-hoc tests
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Oh my god I have been looking at how to find an interaction effect with the two-way ANOVA for quite some time. Thank you so much.
Thank you thank you thank you! You just saved my weekend! I have been tearing my hair out to get my assessment submitted. I just couldn't grasp the mixed model ANOVA.
Hello Tony, thanks very much for your explanation. I would like to know the effect size for Facetype-Alcohol. If you clik in effect size it does not show datas. Thank you
Hello Tony, thank you so much for this explanation! I'm working with a loth of biostatistical data, and in general, I use this RM mixed model, but, in according to the pasting experience of my research group (that is based in SPSS), when you did the post hoc test, we do differently: we open each "point of time" (each day of analyze, for example) and make a one-way anova + bonferroni post hoc. Do you think this is wrong? cause I think the way you did, considering the influence of time, is correct, but you know right?!
Hi Aline, Yes - that is certainly one way of following up a significant interaction effect. What you are referring to is called "simple main effects" analysis. It is typically used if you have a specific a-priori hypothesis regarding one of the treatment levels. You would then follow up the (Bonferroni or otherwise corrected) simple ANOVA with pairwise post-hoc tests. Depending on how it is done, this can be a *less* conservative method, as in the end, the follow-up post-hoc tests would be correcting for fewer comparisons. For example if you had 2 groups (one control and one intervention) and measured them at four times and found an interaction, you could then do two 1-way ANOVAs on each of the group using a corrected p-value of .025 (assuming that your intervention predicts that one group would change and the control group would not). After that you could follow up the significant ANOVA with corrected pairwise comparisons among the four times, needing a Bonferroni corrected p-value for 4 means (or 6 comparisons) of .0083 . Note, that if you then also follow up the between group differences at each time with uncorrected independent t-tests, then you are inflating your type I error rate. The way I've outlined the follow up tests is if you want to determine post-hoc where any differences lie. This is arguably a more conservative method and requires fewer steps. It corrects for *all* possible pairwise comparisons at once (in this case it would be 8 means, or 28 potential pairwise comparisons). Because a Bonferroni correction can be too conservative with more than ~6 comparisons, we then often choose a less conservative post-hoc correction procedure such as Tukey's HSD or Holm-Bonferroni. In the end the analysis method you choose depends on your hypothesis(es), and you just need to justify it appropriately.
@@tonycarlsen1627 Thank you so much!!
In stata program please