+
Meta-Analysis

Meta-Analysis

Author: Jonathan Osters
Description:

This lesson will explain the meta-analysis of experiments.

(more)
See More

Try Our College Algebra Course. For FREE.

Sophia’s self-paced online courses are a great way to save time and money as you earn credits eligible for transfer to over 2,000 colleges and universities.*

Begin Free Trial
No credit card required

25 Sophia partners guarantee credit transfer.

221 Institutions have accepted or given pre-approval for credit transfer.

* The American Council on Education's College Credit Recommendation Service (ACE Credit®) has evaluated and recommended college credit for 20 of Sophia’s online courses. More than 2,000 colleges and universities consider ACE CREDIT recommendations in determining the applicability to their course and degree programs.

Tutorial

Video Transcription

Download PDF

This tutorial is going to teach you about mete-analysis. Now, meta-analysis is different than performing a regular experiment. With a regular experiment, someone decides that they're going to perform the experiment, they collect data from it, and they analyze it. And that's where the story ends.

But what if someone else does a similar experiment? Maybe their analysis and their data are similar to the first one, or maybe they're different, or maybe they're even contradictory? And what if someone else does a third experiment and they get different data, as well? Well, sometimes it's useful to pull all those analyses from the different experiments together and put them into a single document. That's called meta-analysis, where you take all of the previously done work and try and synthesize it.

So, meta-analysis is the process of gathering the data from multiple different experiments that multiple different people have done. In meta-analysis, the goal is not to analyze data, itself. It's to look for overall trends within the experiment. And the nice thing about meta-analysis is if the experimental designs were similar between the experiments that were done, we can, in theory, combine the results to have a more powerful result because of the larger sample size.

Essentially, you're pooling all the sample sizes from the previous experiments that were done. This increases the replication, and we saw earlier that the more you increase replication, the more powerful the result is going to be because you can see overall trends that you might have missed in smaller experiments.

And so, to recap, meta-analyses piggyback off stuff that's already been done before. The goal is to find overall trends in the data, not to produce data yourself. And the goal is to be able to combine the results of some experiments to find some larger trend and be able to confirm it. And then the terms that we used were meta-analysis. Good luck and we'll see you next time.

TERMS TO KNOW
  • Meta-Analysis

    The practice of gathering data from several similar studies to look for overall trends in the data that the studies may have overlooked individually.