17. Finite Mixture (FIMIX) Model in SmartPLS-4 || Dr. Dhaval Maheta

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  • Опубликовано: 2 дек 2024
  • #sem, #smartpls, #construct, #latent, #model, #fimix, #observed, #unobserved, #heterogeneity, #segment, #smartpls4
    Email: dhavalmaheta1977@gmail.com
    Twitter: / dhavalmaheta77
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Комментарии • 15

  • @jazcpcuev9915
    @jazcpcuev9915 Год назад

    Thanks is a ver very good lesson

  • @ruchiragarai8227
    @ruchiragarai8227 Год назад

    Hello sir, thankyou for this informative video. I have run fimix on a dataset, however after creation of the new data file, there is no Final Partition row present alongwith the other new FIMIX segment rows that were created. Does that mean the data does not have unobserved heterogeneity?

  • @michaelpaulse1
    @michaelpaulse1 Год назад +1

    Hello professor thank you for a great lesson. Just a question: The size of Partition 2 is less than the minimum sample determined by G Power, why is it then considered?

    • @michaelpaulse1
      @michaelpaulse1 Год назад

      Could you send me your PPT as well? I did send you an email. Could you also send me your PPT on Micom? Kind regards, Michael PhD Student, South Africa

  • @lamthihoanganh406
    @lamthihoanganh406 Год назад

    Thanks for your video; however, I have a question: How can I detect the second group for observed variables like Class Type? Unbalance percentage >> Observed variables?

    • @DhavalSaifaleeAaryash
      @DhavalSaifaleeAaryash  Год назад

      you will have to check manually in spss or excel

    • @lamthihoanganh406
      @lamthihoanganh406 Год назад

      ​@@DhavalSaifaleeAaryash Thanks for your prompt response but I don't know what criteria that we use to identify the Observed variable? Is it the same percentage?
      For example, FIMIX-PLS group 1: 155 - 49.68%
      FIMIX-PLS group 2: 157 - 50.32%
      >> Gender:
      Male: 229 - 73.4%
      Female: 82 - 26.3%
      >> Age Group
      18 - 24: 57 - 18.3%
      25 - 34: 179 - 57.4%
      35 - 44: 69 - 22.1%
      45 - 54: 7 - 2.2%
      >> Marital Status
      Single: 171 - 54.8%
      Married: 138 - 44.2%
      Others: 3 - 1%
      >> We choose "Marital Status" right?

    • @DhavalSaifaleeAaryash
      @DhavalSaifaleeAaryash  Год назад

      @@lamthihoanganh406 fimix is used for unobserved heterogeneity. So we use percentages

    • @DhavalSaifaleeAaryash
      @DhavalSaifaleeAaryash  Год назад

      You can connect with me on mail: dhavalmaheta1977@gmail.com

  • @cata.maican
    @cata.maican Год назад

    Hi,
    in the last slide you mentioned something about quality standards for the partitions. Are these criteria the same as the step 2 (and 3) from MICOM/MGA? What happens if these standards are not met?
    Thank you.

    • @DhavalSaifaleeAaryash
      @DhavalSaifaleeAaryash  Год назад

      can you mention the time line

    • @cata.maican
      @cata.maican Год назад

      Hi, about 38:40, step 4, the last part of the first bullet point, combined with the second bullet

    • @DhavalSaifaleeAaryash
      @DhavalSaifaleeAaryash  Год назад

      @@cata.maican so this was related to reliability and validity of separate models you make considering fimix

  • @Franzi634
    @Franzi634 Год назад

    Thank you! :) Just one question, why did you keep the mean replacement (Data) while running FIMIX? I thought it is recommanded to click on casewise deletion or am I wrong?