Відео

Sorry , but you only read out the effects. You do not interpret mediation analysis and what if means if the indirect effect is not significant. This video thus not helpful at all.

Great

Thank you Arthur. suppose we have 3 main variables (independent variable), (dependent Variable) and (Mediating variable). each variable is measured by some already established and validated scale. and each scale has about 4 Dimensions with about 3 statements for each dimension. and all items were scored on a 7-point Likert scale 1=Strongly Disagree to 7=Strongly Agree. How would be the correct analysis process for each dimension and each scale? and how would be the correlation analysis between the 3 variables? and for the total scores of dimensions/scales, should we use averages (means) or sums and why?. thank you very much in advanced for your kind clarification.

thank you

You actually solved my biggggggeeessst problem.... thanku very much

Hey, thank u for a useful video. I want to create a new variable with different groups. I have 6 different gaming genres with I want to have under one category Calle gaming genre... but I have tried everything and don't understand. Hope someone can help! <3

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Thank you Mr. Arthur. This really helped me for my graduate statistics class!

Hello Sir, I have attended you Tube trainings, and I found it interesting and have used it. I am PhD candidate in India Punjabi University, Patiala. I am Ethiopian who came to India getting scholarship. I have submitted my synopsis that has one dependent variable and 3 independent variables all of whose data are collected through likert scale each having multiple items. I planned to do multiple linear regressions in my synopsis but after attending different UA-cam presentations and reading different books, I have come to the conclusion that I cannot because linear regression makes use of mean. But data from rating scales is non-parametric data that has to be analyzed with non-parametric tests and from ordinal data it is not possible to calculate mean. Now, I have decided to analyze my data via ordinal regression in IRT (Item response theory). However, I have not got enough information how to conduct it. All presentations of IRT graded response I have come across are about discriminations, difficulty level of items, guessing and ability of persons. Hence, how can I conduct ordinal regression analysis focused on dependent and independent variables? Please help me maam in whatever ways. Or you can make me have contact with any known statisticians you know.

Dear Arthur, I have tried to find the sortl in package in stata 17 but it doesnt show up. Could mind guiding me another pkg? thank you so much

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Great video! what do you do if you only want one subgroup?

thanks so much dear🌹

a good video, but sounds too small.

Nice explanation sir thank you so much

Thank you, this helped

big thanks, sir...

Wonderful explanation!

Hi aurthur, how do we use those factors as independent variable while running multi nominal logistic regression ?

could you please show how to import excel file fo SEM alaysis? and its from like how the variables should be arranged?

Very helpful, thx:)

what do yo do if the RMSEA is not a close fit?

Hi Arthur, thank you so much for putting together this great video! It's been very helpful. Do you have another video explaining how to rename Factors after identifying the theme behind some variables (the construct that some variables are capturing )? And, how do you drop the Factors that ended up not being significant (.80 criteria)? I would like to have my FA tailored before running my regression models. Let me know what you think? Thanks again :)

Wth is this. My username isnt referencing this btw.

Can Anyone please explain how to do a chi square test after combining the data. I followed the same step to club different variables in to one main category but i am getting weird results now.

Thanks for u r Quick and clear explanation.

Very useful video. Thank you so much

what is the recommended action to take when CFA demonstrates a poor fit of your sample to the a priori specified model? What is the rationale for improving model fit, and how is this done? Is EFA an alternative/substitute to improving model fit of a CFA?

Great video. It is very helpful!

Hello Arthur, I don’t think you have the idea of how important this video meant to me. Keep it up

This was really helpful thank you!

Hi, thank you so much for the video . Can Stata be used to do a chi square analysis for images ? I am trying to apply it in image staganography .

Good presentation

Hello, thank you for this video. I have questions regarding the kind simple mediation for video why it doesn’t show chi2 . It shows 1 for RAMSA

Thank you so much

Thank you soo much!! I really needed this

This is really helpful for my biostats class. Thanks

Thanks so much! You have no idea how much this has helped me!

Super helpful video! Thanks a lot

respect!

I don't have sufficient words to express my gratitude, but all I can say Arthur you have helped many including me. thank you so much.

Nicely explained sir Yesterday l did this analysis for the first time and today you explained with more details. Thanks

ua-cam.com/video/rBj3gyNaTW4/v-deo.html

Thanks for this great and very informative video. I am really grateful for this. Never have I ever learnt the PCA in this easy manner.

Thank you so much for this video! Related to post-hoc power calculations for logistic regression in G*Power I don't understand how to determine Prb(Y=1 | X=1) H0. I found these two formulas: Odds1SD=p/(1−p)∗OR(Sx) P(Y=1|X=Sx)=Odds1SD/(1+Odds1SD) So I was wondering: 1. Can I use these 2 to determine Prb(Y=1 | X=1) H0? 2. If so, is OR(Sx) the odds ratio reported in the study? Do you happen know the answer to them? Best regards, Zora

I have been hanging around SEM and Chi Squared Analysis but never reached until I came across to this video. Thank you very much!

May God blessed you too much. it is very interesting presentation

May God blessed you too much. it is very interesting presentation

Very informative, thank you!

Great, informative video. Thank you!