<p>Is it correct to say that, when making inferrence on the results of an experiment, using a random-effects (RFX) rather than a fixed-effects (FFX) model merely makes the results <em>more generalisable</em>, as the quote below (from the fMRI study by <a href="http://www.jneurosci.org/content/29/8/2477.short" rel="nofollow noreferrer">Leaver et al., 2009</a>) seems to suggest?</p>

<blockquote>
  <p>Random-effects models attempt to remove intersubject variability,
  thereby making the results obtained from a limited sample more
  generalizable to the entire population (Petersson et al., 1999). Thus,
  we used random-effects models in our analyses. In a single instance,
  we additionally considered results of a fixed-effects model, in which
  intersubject variability is not removed.</p>
</blockquote>

<p>These authors later make the claim that those brain areas identified with the RFX analysis are more reliably/consistently involved.</p>

<p>I'd have thought that, with FFX, it is not the case that the results are simply "less" generalisable, but instead that they simply do NOT allow generalisation in quite the same way, since you are not accounting for the sampling noise added by the <em>random</em> selection of subjects to the sample. In other words, that there is a step jump rather than a gradual increase in reliability.</p>

<p>Furthemore, in my mind, this sounds like a fallacy somehow comparable to making inferrence on p-values that were not corrected for multiple comparisons: it's not that the corrected values are "more reliable" against false positives, it's simply that the uncorrected ones are UNreliable.</p>

<p>Does this analogy (or some other one) apply here, when using FFX (and not RFX) for sample-to-population inference?</p>

CC BY-SA 4.0
2018-06-27T15:26:45.980Z
Sounds like a question for https://stats.stackexchange.com/
got trolled too much this week
CC BY-SA 4.0
2018-06-28T08:11:12.713Z
Certainly does - [one](https://stats.stackexchange.com/questions/348722/ability-of-fixed-effects-analyses-to-enable-generalisation-of-results) that nonetheless sadly received no answers.
z8080
CC BY-SA 4.0
2018-06-28T17:56:33.953Z
@z8080 Is the question at stats still there? If so, could you link, please?
Steven Jeuris
CC BY-SA 4.0
2018-06-29T07:21:23.477Z
Hi Steven, the link is in my previous comment, here it is again: https://stats.stackexchange.com/questions/348722/ability-of-fixed-effects-analyses-to-enable-generalisation-of-results
z8080
<statistics><causal-inference>
Ability of fixed-effects analyses to enable generalisation of results
z8080
# Ability of fixed-effects analyses to enable generalisation of results

(Asked by: username_0 on 2018-06-27)

Is it correct to say that, when making inferrence on the results of an experiment, using a random-effects (RFX) rather than a fixed-effects (FFX) model merely makes the results *more generalisable*, as the quote below (from the fMRI study by [Leaver et al., 2009](http://www.jneurosci.org/content/29/8/2477.short)) seems to suggest?

> Random-effects models attempt to remove intersubject variability,
> thereby making the results obtained from a limited sample more
> generalizable to the entire population (Petersson et al., 1999). Thus,
> we used random-effects models in our analyses. In a single instance,
> we additionally considered results of a fixed-effects model, in which
> intersubject variability is not removed.

These authors later make the claim that those brain areas identified with the RFX analysis are more reliably/consistently involved.

I'd have thought that, with FFX, it is not the case that the results are simply "less" generalisable, but instead that they simply do NOT allow generalisation in quite the same way, since you are not accounting for the sampling noise added by the *random* selection of subjects to the sample. In other words, that there is a step jump rather than a gradual increase in reliability.

Furthemore, in my mind, this sounds like a fallacy somehow comparable to making inferrence on p-values that were not corrected for multiple comparisons: it's not that the corrected values are "more reliable" against false positives, it's simply that the uncorrected ones are UNreliable.

Does this analogy (or some other one) apply here, when using FFX (and not RFX) for sample-to-population inference?

**Tags:** statistics, causal-inference

> **Comments:**
> **username_1**: Sounds like a question for https://stats.stackexchange.com/  
> **username_0**: Certainly does - [one](https://stats.stackexchange.com/questions/348722/ability-of-fixed-effects-analyses-to-enable-generalisation-of-results) that nonetheless sadly received no answers.  
> **username_2**: @username_0 Is the question at stats still there? If so, could you link, please?  
> **username_0**: Hi Steven, the link is in my previous comment, here it is again: https://stats.stackexchange.com/questions/348722/ability-of-fixed-effects-analyses-to-enable-generalisation-of-results  

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## 0 answers

