hcistats:correlation

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hcistats:correlation [2014/07/23 05:17] Koji Yatani created |
hcistats:correlation [2014/08/14 05:24] Koji Yatani |
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^ ^small size^medium size^large size^ | ^ ^small size^medium size^large size^ | ||

- | | |Pearson's //r//|0.1|0.3|0.5| | + | |Pearson's //r//|0.1|0.3|0.5| |

I haven't figured out the effect size for other coefficients. I will put them here once I figure it out. | I haven't figured out the effect size for other coefficients. I will put them here once I figure it out. | ||

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**If your x and y are not symmetrical, in other words, if you controlled only either of them (we usually define it as x), you must use regression**. This is because the relationship between x and y should be described by saying how x explains y, and not how y explains x. For example, you got data of target acquisition tasks (target sizes and performance time). In this kind of studies, you likely controlled target sizes. So, you should do regression, and see how well target sizes can predict the performance time. | **If your x and y are not symmetrical, in other words, if you controlled only either of them (we usually define it as x), you must use regression**. This is because the relationship between x and y should be described by saying how x explains y, and not how y explains x. For example, you got data of target acquisition tasks (target sizes and performance time). In this kind of studies, you likely controlled target sizes. So, you should do regression, and see how well target sizes can predict the performance time. | ||

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+ | ~~DISCUSSION:open~~ |

hcistats/correlation.txt ยท Last modified: 2014/08/14 05:24 by Koji Yatani