Missing Data Analysis: Multiple Imputation and Maximum Likelihood Methods

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  • Опубліковано 5 вер 2024

КОМЕНТАРІ • 24

  • @A0G7
    @A0G7 11 місяців тому +1

    The inappropriate pictures cracked me up. It made me able to understand a thing I have been feeling board with since days. Keep going!

  • @larissadahinden7517
    @larissadahinden7517 3 роки тому +3

    Love that video, I'm stuck in a dataset for my thesis and this was the right content and the right amount of seriousness today xD It reminds me of Andy Field who always gets sidetracked by cats while providing high quality help for SPSS :)

  • @chawiwawie
    @chawiwawie 3 роки тому +2

    Missing data is often a sleeper subject! Glad you made it fun and neat to listen to. :^)

  • @migi7787
    @migi7787 2 роки тому

    I love that 10 bucks issue! You;re probably the best in teaching statistics so far, and I look forward to see more of your books, teaching, thanks for being here!

  • @HarmonicaTool
    @HarmonicaTool 2 роки тому

    CRAN says: Package ‘fifer’ was removed from the CRAN repository.
    Formerly available versions can be obtained from the archive.
    Archived on 2018-04-30 as requested by the maintainer Dustin Fife

    • @QuantPsych
      @QuantPsych  2 роки тому +1

      You can install fifer through github, or it may be better to use flexplot (also through github).

  • @happycharisma
    @happycharisma Рік тому +1

    Love it! Never laughed so much during studying statistics

  • @amanrastogi5184
    @amanrastogi5184 4 роки тому +1

    Please explain Expectation Maximization as well in some video. Good luck

  • @marianruiz4573
    @marianruiz4573 2 роки тому

    Thank you so much!! I appreciate the comedy relief :))

  • @rebeccayiu2401
    @rebeccayiu2401 4 роки тому +2

    Thanks so much for the content!! golden.

  • @elenao.857
    @elenao.857 2 роки тому

    Hi! Only one quick question: why do we actually do Multiple Imputation if we can directly do Maximum Likelihood?

  • @reyanbakht1203
    @reyanbakht1203 4 роки тому +3

    Jokes may help some people to learn and memorize things better!Thanks

  • @qianyunling8356
    @qianyunling8356 2 роки тому

    Hi! How can we sign up for your classes? I am very interested in taking them (especially the one for maximum likelihood, thank you!)

  • @gsm1410
    @gsm1410 4 роки тому

    I like the content. I would like to ask, can we apply EM for missing value of secondary data (e.g., annual report)?

    • @QuantPsych
      @QuantPsych  4 роки тому

      Yes, unless I'm misunderstanding your question.

  • @barbarazw6360
    @barbarazw6360 4 роки тому

    I want to use the Maximum Likelihood Method to estimate my missings to do a Confirmatory Factor Analysis on the Strengths and Difficulties Questionnaire. My question is, if i can include all participants with 1 to 5 missings per person? Or if it would be better just to include the participants with 1 missing to estimate its value?
    That means, is there a maximum number of missings per person, that can be estimated by maximum likelihood?
    Thank u!

    • @QuantPsych
      @QuantPsych  4 роки тому

      It's going to be better to just include everyone, regardless of how many they're missing.

  • @TheBjjninja
    @TheBjjninja 2 роки тому

    Your datasets must be tiny if all of that can take three seconds

  • @tomi_ade
    @tomi_ade 5 років тому +9

    Explanation is clear but you really need to stop the jokes!

  • @Faisalkhan-ce3dk
    @Faisalkhan-ce3dk 4 роки тому +3

    I stopped watching this video at exactly 8:00.. i couldn't tolerate anymore. 😖😖😖

    • @QuantPsych
      @QuantPsych  4 роки тому +2

      You probably should have stopped earlier. Statistics shouldn't make you cry.

    • @Faisalkhan-ce3dk
      @Faisalkhan-ce3dk 4 роки тому +2

      @@QuantPsych it was NOT statistics, it WAS your jokes and "over"acting.

  • @riversisaacson8293
    @riversisaacson8293 3 роки тому +1

    Thanks for the information, but it would really improve the learning experience if you left out the comedy routine. It's distracting and not terribly funny.