Confirmatory factor analysis in AMOS | Part 2

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  • เผยแพร่เมื่อ 25 ส.ค. 2024

ความคิดเห็น • 24

  • @SG-cm7bt
    @SG-cm7bt ปีที่แล้ว +4

    Hi! I am a doctoral candidate and used your videos for my dissertation. I tried so many other videos, books, and resources. Yours was the only helpful one. Thank you soooooo much.

  • @Clonerianyambali
    @Clonerianyambali 2 ปีที่แล้ว

    Handy video. I am doing CFA with Amos and this video is so helpful.

  • @mic4308
    @mic4308 3 ปีที่แล้ว +2

    Very helpful content explaining the setting (Analysis Porperties) and especially for fit statistics (HOELTER > 200?). I was so glad to hear about this video includes explainations of Standardized Regression Weights and Covariances as well. Appreciated again if you could list all authors and references mentioned in this video.

    • @VahidAryadoust
      @VahidAryadoust  3 ปีที่แล้ว

      Some of the authors are cited in the book chapter published in the following book:
      See chapter 5. Structural equation modeling in language assessment (Xuelian Zhu, Michelle Raquel & Vahid Aryadoust)

  • @engrvarsi3774
    @engrvarsi3774 ปีที่แล้ว

    Generous Mentor!
    Love you sir!
    Of course subscribed.

  • @ducduong80
    @ducduong80 3 ปีที่แล้ว +1

    Thank you very much, your explanations are great!

  • @rachelTIS
    @rachelTIS 4 ปีที่แล้ว +1

    This hands-on instruction is easy to follow up. Why didn't it come up before my stats assignment? :-)

  • @nadeejadodamgoda2669
    @nadeejadodamgoda2669 2 ปีที่แล้ว

    Thank you. Very helpful content.

  • @mahnazbp1204
    @mahnazbp1204 5 หลายเดือนก่อน

    Perfect. Thank you for sharing this great video

  • @cb2497
    @cb2497 3 ปีที่แล้ว

    Thank you for the helpful content. Can you please give us a video or advice on how to bring fit in more complex models in path analysis.

    • @VahidAryadoust
      @VahidAryadoust  3 ปีที่แล้ว

      Please see the rest of the videos on SEM in this list from this link: th-cam.com/video/HKs9vIkpIXE/w-d-xo.html

  • @chokriimed8419
    @chokriimed8419 2 ปีที่แล้ว

    Thank you for the helpful content..
    If some items are not normally distributed (5 of 18), what should be chose for discrepancy? Knowing that p CMIN =0,000, all other indicators showing a good model fit when we use maximum likelihood

    • @VahidAryadoust
      @VahidAryadoust  2 ปีที่แล้ว +1

      Maximum likelihood is not an appropriate method if items deviate from normality.

    • @chokriimed8419
      @chokriimed8419 2 ปีที่แล้ว +1

      @@VahidAryadoust Thank you a lot for your valued assistance

  • @javeriayounus4045
    @javeriayounus4045 2 ปีที่แล้ว

    After conducted analysis my model is not showing any values

  • @morrisjohni.lobetos7874
    @morrisjohni.lobetos7874 2 ปีที่แล้ว

    I am a beginner with SEM. How can I make adjustments to my model to determine the best fit model for my data?

    • @VahidAryadoust
      @VahidAryadoust  2 ปีที่แล้ว

      There are many ways; start off by removing the paths with non-significant path coefficients and/or variables; OR first remove outliers and then re-ran the model; etc.

    • @morrisjohni.lobetos7874
      @morrisjohni.lobetos7874 2 ปีที่แล้ว +1

      @@VahidAryadoust Thanks a lot

  • @javeriayounus4045
    @javeriayounus4045 2 ปีที่แล้ว

    What if the data is not normally disctributed

    • @VahidAryadoust
      @VahidAryadoust  2 ปีที่แล้ว

      Use another parameter estimation method, other than maximum likelihood method.

    • @javeriayounus4045
      @javeriayounus4045 2 ปีที่แล้ว

      @@VahidAryadoust Many thanks

  • @engrvarsi3774
    @engrvarsi3774 ปีที่แล้ว

    Respected Sir, would you please suggest me a book?

    • @VahidAryadoust
      @VahidAryadoust  ปีที่แล้ว +1

      I suggest chapter 5 of the following book: www.routledge.com/Quantitative-Data-Analysis-for-Language-Assessment-Volume-II-Advanced-Methods/Aryadoust-Raquel/p/book/9781032091440

    • @engrvarsi3774
      @engrvarsi3774 ปีที่แล้ว

      @@VahidAryadoust
      You are very generous sir.
      Can't find words to thank you.