Mixed Effects Models: A Conceptual Overview Using R

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  • เผยแพร่เมื่อ 8 พ.ย. 2024

ความคิดเห็น • 11

  • @olearydj
    @olearydj 5 หลายเดือนก่อน +2

    This is one of, if not the best video on the topic here on youtube. Very well done.

  • @jelenachuklina271
    @jelenachuklina271 2 หลายเดือนก่อน

    absolutely fantastic overview of LMMs! Good balance of demonstrating many aspects of the model, tools, and of course, interpretation

  • @ifeanyionyekachi9800
    @ifeanyionyekachi9800 7 หลายเดือนก่อน +1

    this is the best video i have ever came across that simplified the link between longitudinal data and mixed effect models. It is really helpful and thanks a lot for this video

  • @janh7633
    @janh7633 ปีที่แล้ว +1

    Nice demonstration of testing competing models and of adding random intercepts and random intercepts & slopes

  • @deniecewilliams1981
    @deniecewilliams1981 ปีที่แล้ว +1

    This was very well presented and the explanations were easy to follow. Thank you. 😊

  • @charleydublin7304
    @charleydublin7304 5 หลายเดือนก่อน

    Excellent presentation, thank you

  • @Sam-tg4ii
    @Sam-tg4ii 3 หลายเดือนก่อน

    Very clear. Thank you.

  • @jelenachuklina271
    @jelenachuklina271 2 หลายเดือนก่อน

    I wonder, what is the demo tool you use, where you are able so elegantly to edit while doing the demo? :)

  • @Sam-tg4ii
    @Sam-tg4ii 3 หลายเดือนก่อน

    6:05 But previously you said 5-6 groups is ideal for a variable used as random effect. But if you also say participants are used as random effect, isn't usually 100s of participants in a study? Doesn't that mean hundreds of groups in the random effect variable (participant ID)?

  • @brianfisher6799
    @brianfisher6799 7 หลายเดือนก่อน

    I know this is slightly off topic, but maybe time of day when the sample was taken would account for a portion of that final unexplained variance.

    • @PsychEDD
      @PsychEDD  7 หลายเดือนก่อน

      Quite possible :)