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There are a few packages that handle objects created by {lme4} commands in R: {emmeans}. pairs() function will do pairwise comparisons and give you test statistics. See vignette at CRAN. {ggpredict} will do this, as well. See Vignette at CRAN. This package has a focus on returning objects that are ready for plotting. {sjPlot} also focuses on plotting, but ...


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The hard thing about these topics in statistics are that they seem very straight forward but when you have questions the answer is always it depends. When correcting for multiple tests this is the same. It all depends on the way you handle your data and interpret your results. When looking at p-values for significance we in general accept the chance of 5% ...


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Is there any adjustment for multiple comparisons built into the p-values for individual regression coefficients? If not, is it prudent to apply an adjustment (Bonferronni, FDR etc) to the coefficient p-values? In short, yes, there can be. I usually use linear regression for everything, including designs that could be estimated with an ANOVA. I use R, so I ...


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I believe I misunderstood your question the first time around. As pointed out by others, there is usually not a recipe for how or when to perform multiple comparison testing. In order to have a better understanding for what might be appropriate in your case, we would need more information about your study design and what constitutes a "test." Suppose you ...


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If the goal is to conserve the family wise error, you should consider each cell a unique test. So, if it makes a p-value it's a test. Though with over 1,000 tests, you ought to consider controlling the false discovery rate. In either case, it's still focused on each unique row and column combination.


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I agree with Christian's answer, but I'd like to add some explanation. I assume you want to find out if one version of the website is better than other, in terms of converting more visitors into customers. So you are actually comparing the fractions of customers among the visitors between the three groups. Chi-squared test on a $2 \times 3$ table will give ...


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its a bit uncelar what you are asing for but maybe you could use a ordinal regression. This is if you want to predict. https://en.wikipedia.org/wiki/Ordinal_regression Or a Chi square. This is if you want to compare proportions of the groups. https://en.wikipedia.org/wiki/Chi-squared_test


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p value adjustment has nothing to do with the test used to derive the p values. It has to do only with the number of hypothesis tests (p values), and what you want to control for (familywise error rate or false discovery rate). With 1000 hypothesis tests, you may not want to adjust p values, but simply accept that you likely have some false positives in ...


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Should you account for multiple testing? I am sure opinions will differ, and it will depend on the goals for the analysis, and the number of separate anova's (you didn't tell.) It is probably better to go for one common model. For that, formulate the anova as a regression model, and choose some technique for handling heteroskedasticity in regression models. ...


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Your results are more credible if you adjust the p-values for multiple dependent variables. But with highly correlated dependent variables, Bonferroni’s correction is extremely conservative. An alternative is to adjust the p-values by permutation or rotation testing. Rotation testing relies on multivariate normal distribution theory and the p-values are ...


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