14
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Difference between Repeated measures ANOVA, ANCOVA and Linear mixed effects model
First there is the question of whether it is OK to use percent change as the outcome. In a regression model with baseline as a regressor this is a very bad idea because the outcome is mathematically ...
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7
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Difference between two-way ANOVA, factorial ANOVA and ANCOVA: as analogies of linear regression
Compared to one way ANOVA:
Two way ANOVA adds one more categorical independent variable to the regression (and possibly the interaction between the two IVs).
Factorial ANOVA adds any number of ...
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7
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GPower: Difference in Sample Size for ANCOVA vs. Repeated Measures ANOVA in clinical trials
This is a topic for seriously misunderstanding G*Power! Thank you for bringing this up.
We thought the following was an explanation for the matter:
"When calculating the sample size for "...
7
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Justifying a smaller control group in survey study
I think you've answered your own question.
If your goal is to independently assess the psychometric properties within the control group, collect enough data to do that. If your goal is only to compare ...
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7
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Comparison of 2 groups with covariate
I agree with @Sointu's comment.
There is nothing "outdated" about ANCOVA. In what I see it's most often just approached through regression. The techniques aren't alternatives, at least not ...
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7
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Comparison of 2 groups with covariate
First, as others have pointed out, ANOVA, ANCOVA and linear regression are equivalent. Mathematically, if there are $n$ observations and $p$ independent variables (or predictors, or whatever term you ...
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7
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Comparison of 2 groups with covariate
The other answers seem to already address the core question of which techniques to use. As others have mentioned, this seems to be a question that can be easily solved with regression or ANCOVA, and ...
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6
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Should covariates that are not statistically significant be 'kept in' when creating a model?
We really need more information about your goals to answer this question. Regressions are used for two main purposes:
Prediction
Inference
Prediction is when your goal is to be able to guess at ...
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The lines on my scatterplot for ANCOVA results doesn't look right, personal error or model error?
You appear to be misunderstanding the output from your model.
In your code, the line:
abline(fit.mice$coefficients[1:2], col="skyblue3")
plots a line with the ...
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6
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Two-Way ANCOVA: It is necessary to include a non-significant interaction term in the model?
There are two different definitions or understandings of the term ANCOVA.
The first and a broader one is "Any linear model containing continuous/scale predictors besides factors (categorical ...
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6
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Why is ANCOVA not appropriate for modelling post-intervention outcome, controlling for baseline
This is explained very clearly in Lüdtke and Robitzsch (2020). I'll briefly summarize their arguments below but the paper is clear and easy to read, and I recommend you read it.
Essentially, the ...
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6
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ANCOVA with all continuous variables
Instead of ANOVA use regression, which is the more general method: it's perfectly okay to do a regression with any mix of categorical and continuous variables. ANOVA is equivalent to a regression with ...
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6
votes
Change score as predictor
As discussed further here there are many serious problems with change scores. When analyzing change in an independent variable it is usually the case that the most recent measurement best predicts ...
- 98.4k
5
votes
Fat tail? Short tail? Long tail? Where do I go from here?
Growth rates must be distributed as some variation of the Cauchy distribution. I have written a series of papers on this. The Cauchy distribution has no mean so it has no variance or covariance. ...
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5
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Post-Hoc pairwise comparison for slope (interaction term) in R?
If you are using R to do the analysis, the emmeans package has an emtrends function that estimates estimated marginal slopes. e.g.,
...
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5
votes
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Cohen's d from a linear regression model
In your regression model, $c_1$ (an unstandardized regression coefficient for a 0/1 indicator variable) is an adjusted mean difference, adjusting for the other variables in the model. As such, you can ...
- 1,838
5
votes
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Pairwise comparisons of regression coefficients
Use the emmeans package, specifically pairs(emtrends(m, ~grp, var="var")) ... where grp is ...
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5
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Two-Way ANCOVA: It is necessary to include a non-significant interaction term in the model?
There is nothing in statistical theory or practice which requires you to include any interaction, or any main effect for that matter. You include in your model the variables which your scientific ...
- 18.4k
5
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Two-Way ANCOVA: It is necessary to include a non-significant interaction term in the model?
It looks like other responses have already addressed the fact that there is no absolute rule that an interaction needs to be included. I'll just echo briefly that the decision of including an ...
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Questions concerning visualizing model results with the R-package visreg
The paper by Patrick Breheny and Woodrow Burchett (the authors of visreg) discuss all the issues raised in your question. Before I go into the specific questions, ...
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5
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Can this be classified as an ANCOVA?
ANCOVA is terminology that some fields use to mean "linear regression with continuous and categorical variables". If Section is categorical and t is continuous, then I'd say this fits the ...
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What is the difference between CUPED and regression adjustment？
There is no difference.
First, note that the estimator $\theta$ you describe in CUPED is identical to the slope estimate from simple linear regression.
Second, note that $Y_i^{cv}$ looks curiously ...
- 39.6k
4
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Two-way ANOVA vs ANCOVA in R
Stumbled on this question a couple of years after it was posted while looking for some info on ANCOVA with R. Wound up writing a much longer example using the diamonds dataset I won't try and cram it ...
- 216
4
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Help with change scores and/or ANCOVA design
For anyone who stumbles upon this question and is interested in determining whether for their type of non-randomized study design, ANCOVA or change scores (or a comparison of both) is more suitable, I ...
- 41
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Fat tail? Short tail? Long tail? Where do I go from here?
Your Q-Q plot doesn't look like it has a fat tail. I'll show you how a fat tail looks like:
Your tail is like a Victoria Secret's model compared to the above. I wish some of my model residuals had ...
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Why is ANCOVA not appropriate for modelling post-intervention outcome, controlling for baseline
DiD vs. ANCOVA has been discussed in my workplace, a health insurance company, where DiD has been the industry standard for some time. I agree that DiD does not adequately account for regression to ...
- 6,286
4
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Questions concerning visualizing model results with the R-package visreg
I'm not a huge fan of such plots for presentation. You have one continuous covariate and one continuous response variable, so it's easy to make a scatterplot. Use different symbols and colors to ...
4
votes
Using PCA to combine variables in a randomized trial with a baseline and a follow-up measurement
The method of collapsing SBP and DBP into one measure should be guided by subject matter expertise. If you are interested in MAP then compute MAP. PCA is guided by variance maximization not by ...
- 98.4k
4
votes
Accepted
Different ways to include pre-test performance as a covariate in a linear mixed-effect regression. Which is correct?
A wide variety of interaction terms could be "correct" here, as comments from Robert Long and COOLSerdash suggest. To decide which if any interaction to use involving the ...
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4
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ANCOVA vs linear mixed model in R, using different functions gives me different results
To summarise: You've discovered that different packages & functions may implement different methods, so you shouldn't expect to get the same results when you throw your data at a sundry collection ...
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