Random vs fixed effects in ANOVA

In summary, the conversation discusses the concept of random effect in ANOVA testing, specifically in identifying whether a factor is random or fixed. The speaker gives an example of comparing the densities of different foods, where the measured difference between the groups is considered a fixed effect, while the variation within each subgroup is a random effect. The distinction between fixed and random effects is determined by the scope of the analysis and can be simplified as between subject factors being fixed and within subject factors being random.
  • #1
thrillhouse86
80
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I am having a lot of trouble conceptually understanding the idea of a random effect in ANOVA testing - more specifically identifying whether a factor is random or fixed

Thanks,
Thrillhouse86
 
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  • #2
Say you were interested in comparing the densities between different foods, and you took samples from a bunch of twizzlers, slices of bread, and banana splits.

The measured difference between the three groups is a fixed effect.

Now each subgroup will have its own random effect. Twizzlers are all about the same, so the variation in them will be small. Your breads may be a little different, but should be pretty close to the same, so you will have some variation within the breads. Likewise the banana split could have large variations, depending on who made it, and how much they like whip cream.

I think the general idea is that *between* different variables is "fixed", and *within* a type/factor is "random".

The scope of your analysis decides also determines what is fixed and what is random. If you are interested in comparing the group dessert to the group sandwich, then each dessert is now a random effect inside of the fixed dessert category.

I'm sure there is someone who can explain it better, but that's more or less how I think of it.
 
  • #3
hmmm ... is it as simple as:
'between subject factors' = fixed variable
'within subject factors' = random variable
?
 

Related to Random vs fixed effects in ANOVA

1. What is the difference between random and fixed effects in ANOVA?

Random effects in ANOVA refer to factors that are selected randomly from a larger population, while fixed effects refer to factors that are specifically chosen by the researcher to be included in the study. Random effects are typically used to account for variability within a population, while fixed effects are used to test specific hypotheses.

2. How do you determine whether to use random or fixed effects in ANOVA?

The decision to use random or fixed effects in ANOVA depends on the research question and the design of the study. Random effects are more appropriate when the goal is to generalize the results to a larger population, while fixed effects are more suitable for testing specific hypotheses.

3. Can both random and fixed effects be included in the same ANOVA model?

Yes, it is possible to include both random and fixed effects in the same ANOVA model. This is known as a mixed effects ANOVA and is commonly used in research studies with complex designs or multiple factors.

4. What are some examples of random and fixed effects in ANOVA?

Examples of random effects in ANOVA include gender, age, and geographic location, as these factors are typically selected randomly from a larger population. Fixed effects in ANOVA may include experimental conditions, treatment groups, or specific variables that the researcher wants to test.

5. How do random and fixed effects impact the results of an ANOVA?

Random effects can increase the generalizability of the results, as they account for variability within a larger population. On the other hand, fixed effects can provide more specific and focused information about the factors being tested. The inclusion of both random and fixed effects can provide a more comprehensive understanding of the data in an ANOVA analysis.

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