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Hi there, actually, OLS regression is the method for estimating the parameters in a linear regression model, minimizing the sum of the squared differences between observed and predicted values. Cheers, Björn.

Glad you liked it and found it helpful. Many more videos on this channel that might help you. ;-) Cheers, Björn.

Hey there, it is the same calculation as for the one-way ANOVA, since the goal is the same and power differences are often negligible between the two, except in rare cases. Some researchers will add 15% to the sample size to be safe. Cheers, Björn.

@@statorialsOh okay, thanks for the clarification! Do you by any chance know where I can find the actual calculations G*Power uses to ultimately reach these sample sizes? I have been running power analysis in Stata, Python, & R using the same inputs but I get different Ns!

I would have pointed to the GPower manual, but that is not revealing the formulas used. Cohen (1988) is always a good start to search, but I could not see anyhting either while taking a quick glance.

@@statorials Oh, thank you !

Hey there, it sounds like you have no repeated measures when you have only between subject factors, hence, I would recommend going with the two-factorial ANOVA and not a mixed ANOVA. df %>% anova_test(dv ~ iv1*iv2) Post-hoc testing depends on the interaction effect. If you have none, you will use something like that: df %>% pairwise_t_test(dv ~ iv1, p.adjust.method = "bonferroni", detailed = TRUE) with iv1 being your group variable. Cheers, Björn.

Thanks

Hi there, if you have specified a between-subjects factor, SPSS automatically uses it within products in EM Means. The only thing I can think of, is that you specified a custom model under the Model button that consits only of the respective "main" effects. Maybe check on that. Cheers, Björn.

@@statorials Yes, actually, under EM Means Button, there's no Compare Simple Main Effect's Box to tick, like what's shown in your video. Only Compare Main Effect's Box. I still don't know how i could specified it under the Model Button. Will there be any problems if I try to manually use independent t test to determine the mean difference for each measurement as a post-hoc analysis? without Bonferroni correction. Thank you very much

Hmm, I suppose SPSS 27 could have EM Means not included (yet). You can of course do the t-tests yourself. You can use ruclips.net/video/PW4OlfCLrM8/видео.html or ruclips.net/video/INg9oHbmsJY/видео.html as orientation. You only have to take care when doing independent or paired t-tests when you observe between-subjects effects and within-subjects effects or even when doing the interaction effect. Cheers, Björn.

@@statorials thank you very much. Very helpful...

Hallo Lisa, danke für das Lob! Ja, das ist möglich. Auf dem deutschen Kanal ist das erste von zwei Videos schon online: ruclips.net/video/f6JRhnCmePc/видео.html Die Effektstärke für die Haupteffekte kommt in genau einer Woche. Viele Grüße, Björn.

@@statorials Danke für deine schnelle Antwort - das hilft mir so sehr! 🙏 Tatsächlich hätte ich noch eine Frage: Bei mir kam ein signifikanter Interaktionseffekt raus und die Post-Hoc-Tests haben gezeigt, dass sich die Gruppen zum Prä- und zum Post-Messzeitpunkt signifikant unterscheiden. Ist es sehr problematisch, dass sie sich auch zum Prä-Messzeitpunkt unterscheiden? Denn das würde man ja eigentlich nicht erwarten. Die Werte der EG steigen vom Prä- zum Post-Messzeitpunkt auch signifikant an. Die der KG nicht. Das ist also erwartungsgemäß. Ich danke dir jetzt schon vielmals!

Hallo Lisa, gerne! Zu deiner Frage: Wie stark ist der Gruppen-Unterschied in der Prä-Messung mit Cohen's d denn? Wenn es ein noch kleiner Unterschied ist, wäre das sicher verkraftbar und könnte auch problemlos als vertretbar eingeordnet werden. Die Tatsache, dass sich nur die EG steigert, unterstützt entsprechend die Hypothese, dass die Intervention hilfreich war. Viele Grüße, Björn.

@@statorials Hallo Björn, es handelt sich um einen kleinen Unterschied (d = .441). Wie würdest du hier dann argumentieren? Ich bin dir so dankbar!!

@@la_lisamarie Hallo Lisa, zwar ist der Effekt schon tendenziell mittel, dennoch würde man den hier relativieren können, wenn man gleichzeitig die Steigerung nur bei der EG hat. Ist die EG oder KG höher in der Prä-Messung höher? Also überholt die EG die KG oder wird der Unterschied zwischen EG und KG größer, wenn die EG auch bei der Prä-Messung höher lag? Viele Grüße, Björn.

Hallo Lisa, das partielle Eta² erhältst du, wenn du effect.size="pes" in anova_test() einfügst. Viele Grüße, Björn.

@@statorials Danke!!!

Hi there, I would recommend doing a mixed ANOVA approach that covers between and within effects: ruclips.net/video/5AflJkhaln0/видео.html The estimated marginal means (posthoc-testing) you mentioned, cannot be directly considered in the sample size calculation though, which is not a deal breaker. Cheers, Björn.

Hi Adam, glad you like it. The way I read you post, it appears to me that you have 8 measurements per 272 individuals that will be split into 16 groups. Do you have an additional between-subject factor with the pots, meaning your subjects will be put into 16 groups and within each group you have another experiment? What is the distinction between the groups, if not? Cheers, Björn.

@@statorials Hi Björn, thanks for the reply. So the experiment is divided into 4 variables: presence of plants (yes or no), presence of contaminant (yes or no), type of soil (type 1 or 2), aeration (yes or no), which gives 16 combinations. There would be 8 samplings every week. We would like to used repeated measures ANOVA afterwards and we're interested if 3 replicates per each group would be enough. All the groups would be affected in the same way by changing temperature. Thanks. Adam

Hey there, the term "mixed ANOVA" is used because it includes both within-subjects (repeated measures) and between-subjects factors. in the traditional ANOVA model world. While the term "mixed" in mixed ANOVA may be misleading in regard to linear mixed models it is, as you pointed out, a different class of models. Depending on your research field, the terms may vary a bit, like split-plot ANOVA or something with mixed, like mixed-design ANOVA or mixed-model ANOVA. Cheers, Björn.

Thanks, mate! Cheers, Björn.

Thanks, appreciate the feedback!

Glad you like it!

I need some more information. How many time points do you have and how many show a low p-value for Levene's test? Cheers, Björn.

@@statorials i have 3 levels for my within subjects factor and two show a low p value for the levenes test. However, my sample sizes for the between groups only have a difference of 11 participants and the standard deviations for each group do not vary much. Is there another rule of thumb I can use to evaluate this assumption ?

Thanks for the feedback! Cheers, Björn.

Freut mich, wenn es hilft. 🙂

a question, do you know how I can ‘correlate’ an ordinal variable with a nominal variable (with categories)?

Hey there, I dug into that a bit and Spearman is an approximation (at best). I'll swap out the video in the oncoming weeks when I had time to record a new video regarding that. One can use the effect size calculation of the two-sample Wilcoxon test, meaning Glass rank biserial correlation coefficient or Cliff's delta. For your question, you can "correlate" an ordinal an a nominal variable using Chi² test of independence and use the effect size Cohen's ω to quantify the association. Cheers, Björn.

Hi there, you are almost correct in your proceedings. 1) I would be careful though when it comes to the Shapiro-Wilk test and its sensitivity in larger samples, rejecting the Null Hypothesis because of negligible differences. Consider a Q-Q-plot for that task. 2) If that looks not approximately normally distributed, go with the Wilcoxon paired test aka. Wilcoxon-signed rank test (ruclips.net/video/9sX62Qagrs0/видео.html), NOT the Mann-Whitney-Wilcoxon test. Cheers, Björn.

@@statorials I really appreciate your help and time Thank you so much <3

@@odaimaihoub1222 you're welcome! Best of luck for your analysis!

Hi there, you migt be able to solve this by adding na.rm=TRUE, which will listwise exclude cases with missing values. Best Regards, Björn.

You're welcome. Glad the video about Pearson helps. :-) Cheers, Björn.

Hello Sara, my G*Power does not show error variance under the Determine option for the mixed ANOVA. Which version of G*power are you using? You could also use the sum of squares, if provided by the studies, to calculate the Eta-squared and then transform it to f. See here, how to do it: ruclips.net/video/YPO0hBKCUQc/видео.html . By the way, it should be sressed that the effect siize for the mixed ANOVA is based on the interaction effect. Cheers, Björn.

@@statorials Thanks for the reply, Björn. I have the same version as you. The error variance will appear under the Determine option if choosing "as in Cohen (1988) - recommended" under Options.

You're welcome! Cheers, Björn.

Yes, because the order of the groups plays a role. Report only absolute effect sizes. Cheers, Björn.