New answers tagged interaction
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Which coefficient is correct for assessing effect size of a regression interaction, standardized or unstandardized?
The choice of coefficient should depend on business needs and your desired interpretation, no? In your example, a unit increase in disp*hp is associated with a 0....
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Cox survival PH violated variable is part of an interaction
First, as Power seems to be a continuous predictor, see if modeling it more flexibly, for example with a regression spline, fixes your proportional hazards (PH) ...
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I have a 3 way mixed anova. The 3 way interaction is not significant
You do paired tests when the data are actually paired. For example, if the same individuals were evaluated at both values of time, then a paired test of some sort ...
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Reporting the results of a continuous moderating variable on a categorical independent variable
Since you have paired pre-music & post-music test scores for each participant in the study, you can analyze the data by regressing the post-music test scores (the outcome) on the pre-music scores, ...
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Repeated measures ANCOVA interpretation – significant covariate/test day interaction
Instead of getting caught up in ANCOVA terminology, remember that it's just a particular form of a linear regression model. With ANCOVA, you have a continuous outcome (here, ...
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How to account for moderator variables in propensity score matching or exact matching?
In order to perform moderation, you need to be able to validly estimate subgroups effects, which means confounding needs to be removed within subgroups of the moderating variable. In the context of ...
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Reporting the results of a continuous moderating variable on a categorical independent variable
Each person has been tested twice, once before listening to music, and a second time after listening to music. I think you want to presume that the first test does not have an influence on the second ...
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When predicting case status, what is the appropriate way to examine differences in proteomics by gender (/the interaction between protein and gender)?
Approach 3: instead of modeling case/control against proteins and gender, model the set of protein expression levels against case/control and gender. That's been a standard approach dating back to the ...
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Interpreting Significant Interaction Term Odds/Hazard Ratio with Binary Variables
The correct answer (assuming that you are using treatment or dummy coding for each of A and B) is close to your second suggestion, with one modification:
the interaction between A and B provides a ...
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What does it mean when in a mixed design the "within" subjects is significant only when the "between*within" interaction is excluded from the model?
As Sal Mangiafico said in a comment, this is probably due to small sample size combined with an attempt to estimate the additional coefficient for the interaction term. On the bright side, if this is ...
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LMM Results interpretation: Change in results when adding interaction
Under treatment/dummy coding as you are using, this is what happens with coefficients when predictors are involved in interaction terms. The individual coefficient for a predictor is then defined for ...
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interaction term in Cox PH model
... the results showed HR and 95% CI for 'sex' was very high and wide. I assume this is because I put 'sex' twice in the [multivariable] model, one is in its original form and one as an interaction ...
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Controlling for an effect by adding it as covariate in R
That looks quite good.
Just a few notes:
First, you should always visualize your data. And if you deal with interactions, consider using some of the interaction visualization tools, see e.g. here or ...
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Interpretation of interaction between variable and time in cox regression
Unless you took special precautions to extend your data set to include separate rows for each individual at each time the individual was at risk (which your coding doesn't seem to have done), your ...
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Interpretation of interaction of a continuous and a dummy variable in linear regression
$\beta_3$ is the difference in slopes of $\mathbb{E}(Y|X_1)$ vs. $X_1$ between observations that have $D=1$ and these that have $D=0$. The slope for the former is $0.5+0.7=1.2$ and for the latter $0.5$...
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Different p-values for normal regression and interaction terms
When you modify your model by adding or removing predictors, whether main effects or interactions, the coefficient estimates, standard errors and (therefore) $p$ values of all the terms already in the ...
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How to consider time from vaccination on final outbreak size
If Vax represents a fraction of individuals vaccinated in a facility
The time since vaccination might be considered a moderator of the effect of vaccination, ...
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Estimating interactions from non-interacting features
Unfortunately, no. You only observe the main effects. Your observations would be perfectly consistent with any interaction effect, small or large, positive or negative.
You can verify this yourself: ...
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4 way mixed ANOVA--Need help interpreting a 3 way interaction!
A significant 3-way interaction (here among all of the within-subject variables) means that you can't properly assess the association between any one of those predictors and outcome unless you know ...
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Need help with analyzing data from within subjects design study
I get terribly confused by the "level 1," "level 2" etc. terminology. If you are using lme4 you shouldn't have to worry about that.
You specify ...
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Probe interaction between treatment integrity (fidelity) and treatment effect in RCT with blank/placebo controls
Without equal measures of fidelity, I agree that adjusting for fidelity is infeasible. An interaction term does not help the situation.
This is an example of how the quality of a randomized trial is ...
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How to find the significance of different levels of an interaction in a generalised linear mixed model?
I had a similar question in this post and answered my question. Please review it for further explanation.
You can use car::Anova function:
...
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How to find the significance of different levels of an interaction in a generalised linear mixed model?
With interactions and default coding in R, the regression coefficients at each level represent differences of outcomes from what would be predicted based on lower-level coefficients. That is, the ...
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Correct notation for cross-level interaction
As @Frank notes in a comment, what you show in your question does not evaluate an interaction between months and gender. If we ...
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How to do moderation / mediation anaylsis in a binary logistic regression?
There's no fundamental problem here. It's simplest to explain for the moderator, which is equivalent to an interaction term between predictors in a regression. The interaction coefficient between a ...
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