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BecketTodd's profile
Rebecca Todd
Rebecca Todd
Rebecca Todd
@BecketTodd

Tweets

Rebecca Todd

@BecketTodd

University of British Columbia. Tweets about neuroscience, cognitive neuroscience, psychology and sometimes bears.

Vancouver, British Columbia
mclab.psych.ubc.ca
Joined June 2012

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    Rebecca Todd‏ @BecketTodd 28 Jul 2019
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    Help stats gurus! Am reviewing a paper: N=24 w 7 2x2x2x2 RM anovas reporting 4-way interactions (& no correction for mult comparisons). ISO authoritative source to cite in support of statement that N = 24 is way underpowered for 3 and 4-way within-subject interactions. thx!

    2:19 PM - 28 Jul 2019
    • 11 Likes
    • Adaeze Egwuatu Michael Mullarkey kristina roberts Robin N. Kok Dr. Lot! VE RoastDuck Gaëtan Mertens SHONN SLC | 程聖倫 Corey Yanofsky truly and sincerely
    7 replies 0 retweets 11 likes
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      2. Rebecca Todd‏ @BecketTodd 28 Jul 2019
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        And btw this is for a very high impact journal!

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      3. Rebecca Todd‏ @BecketTodd 28 Jul 2019
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        And they are running between subject correlations with an N of 24...

        0 replies 0 retweets 1 like
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      4. End of conversation
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      2. Casper Albers‏ @CaAl 29 Jul 2019
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        Replying to @BecketTodd

        Interpreting fourway interactions is senseless if you haven't also interpreted the (4) threeways, (6) twoways and (4) oneways. That's 15 p-values to interpret. You shouldn't need an authoritative source to see that n=24 is insufficient here. Common sense will do. 1/2

        3 replies 1 retweet 6 likes
      3. Rebecca Todd‏ @BecketTodd 29 Jul 2019
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        Replying to @CaAl

        And they have all those interactions in 7 different ANOVAs. But I thought common sense would have suggested a) the authors not do it and b) the journal not pass it on to reviewers.

        0 replies 0 retweets 1 like
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      2. Michael J. Kane‏ @Kane_WMC_Lab 29 Jul 2019
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        Replying to @BecketTodd

        Paging @lakens @RogertheGS . And seehttps://www.journalofcognition.org/articles/10.5334/joc.72/ …

        1 reply 0 retweets 5 likes
      3. Rebecca Todd‏ @BecketTodd 29 Jul 2019
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        Replying to @Kane_WMC_Lab @lakens @RogertheGS

        Thanks that's a great reference. Beyond reviewing, my lab runs up against the challenge of how to properly power within-subject designs all the time.

        1 reply 0 retweets 3 likes
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      2. Jake Westfall‏ @CookieSci 29 Jul 2019
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        Replying to @BecketTodd

        Some skepticism probably appropriate, but IMO this isn't obviously a problem. Thoughts: - In 2^k factorials, testing interactions statistically equiv. to testing simple effects: both compare half of obs to other half. No power penalty - Big fx sizes less implausible in RM designs

        1 reply 0 retweets 4 likes
      3. Thom Baguley‏ @seriousstats 29 Jul 2019
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        Replying to @CookieSci @BecketTodd

        Yes - it is the multiplicity that is more of a problem - rarely do people predict 4-way interactions (though I think I had at least one in my thesis). Also designed experiments have greater power to detect interactions because the factors are usually extremes (+++ vs.---)

        0 replies 0 retweets 3 likes
      4. End of conversation
      1. Peter Clayson‏ @clayson_peter 29 Jul 2019
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        Replying to @BecketTodd @minzlicht

        Don’t forget that this will increase the experimentwise error rate 👇🏻 it’s an often overlooked issue in multifactorial anova. I hope the link helps!https://link.springer.com/article/10.3758/s13423-015-0913-5 …

        0 replies 0 retweets 4 likes
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      1. Thom Baguley‏ @seriousstats 29 Jul 2019
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        Replying to @BecketTodd

        What about: https://statmodeling.stat.columbia.edu/wp-content/uploads/2017/07/maxwell2004.pdf …

        0 replies 0 retweets 4 likes
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