Exploratory Factor Analysis 

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I learn all this in 15 minutes what has taken 4 years and $10,000. Thanks for this. Simple,clear with no distracting loud music.

Varimax is an orthogonal rotation, which assumes that factors are unrelated to each other. In your example, you've used personality traits, which are psychological constructs and would be expected to be related to each other (in psych, constructs are rarely if ever completely unrelated). An oblique rotation, which assumes factors are related, would normally be the most sensible choice in psychology research. The appropriateness of orthogonal vs oblique rotation for their variables/discipline is something people should be aware of and consider in EFA.

I was struggling with factor analysis and I had zero background in this. You video just saved my life, explained so nicely and such a clear visual representation within such short amount of time! Can't thank you more. Keep posting more please.

I have spent four years in learning these kind of courses and i found you. You are a life saver. Pls keep it up

Thank you so much. You provided a much clearer explanation that my lecturer or any text books!

You've done what my teacher couldn't, thank you

Thank you, this has been so helpful to me. I'm a 1st year PhD student in social sciences working on my first literature review.

This is so simple, straightforward, and helpful. Thanks a lot!

One of the best lectures on EFA I have sat through

Lack words to appreciate your exemplary lecture!

I understood the concept communalities after listening your explanation. Thank you so much.

Great video! Great Explanations. This made me try the one month subscription to conduct an exploratory factor analysis. The software is easy to use and accessible immediately after purchase. I can highly recommend it! Facilitated my research A LOT!

Why was I able to understand all of this, this was so understandable, thanks!

Easy and clear. Above my expection,very helpful. Thank you.

Veryyyyyyyyyyyyyyyyyyyyyyy helpful. that helped me so much! Thank you very much and please keep going.

Really great. I have just started following all videos after watching this.

one of the best videos on explanation of FACTOR ANALYSIS. THANKS A LOT. BY HEART 🧡🧡

Loved your explanation! So easy to understand!

Very easy to understand video. One of the best on RU-vid ❤

I really appreciate the videos that you are covering Test Theorie und Test Konstruktion lecture.

I wish my stats professor in undergrad was even a tenth as good as you.

Amazing! This is very well and simply explained. Thank you!

I finally understood many concepts.

Thank you so much for your very clear explanation

this is fantastic! thank you so much!

Omg you're so good. I literally cannot thank you enough!!! Thank you so very much.

Thank you for clear presentation

Thanks a trillion. Brilliantly explained.

good. extremely useful for beginners

She's a life saver.

Thank you so much! love the simple and logical explanation!

In PCA, proportion of eigenvalues > 80% is also considered as third method.

Thank you a lot!! Need to review all your videos to pass the exam😂

Excellent mam.Thank you so much for clear explanation

you are an amazing instructor, thanks a lot

Thank you very much for your explanation. I appreciate your work and effort.

Thanks very much for this video! Very helpful!

You are my savior. thank you so much

This Video was very helpful .

very nice presentation. well explained.

Well done!!! energetic and engaging explanation! bravo

Thank you so much for the detailed explanation. This helped a lot!

Great teaching!

Perfect explanation

Great explanations (y)

Super helpful 😊

Thank you so much for these videos

A very good presentation

Woooow excellent video. Thanks

Beautiful talk

quite useful

Thanks ma'am it really helped me a lot

Love this! thank you!

This is great!! Thank you so much! I was just wondering why you used 3 as the amount of factors at the beginning? How did you know to use that instead of 2,4,5 etc.

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You just saved a life.

very helpful. thank you

Really great

Thank you.

So, please correct me if I'm wrong (I'm revisiting the topic). When you identify the each factor, you can create a factor score for each individual by using the relevant component values in each factor and the actual individual observations, right?

Excellent. Kindly upload videos on Research Designs

Thank you very much for your clear & precise explanation. Is there any difference(s) on conducting PCA, EFA and CFA?

Thanks

Excellent....

what you talked about was principal component analysis instead of factor analysis.

thank you

which one is 1 and which one is 5 is not mentioned. some times scalling starts from 5,4,3 ..1 or some times , in ascending order. which order has been followed here? i think 5,4,3,2,1 . Am I correct?

What about factor loading, how to calculate that, am I missing something ?

Thanks. This is awesome

Thank you. Do we calculate the Eigean value first to assign the numbers of factor?

hi, and thanks for the video . i wanted to ask what variance is. plz use an example . Thank u

thank you so much ... if the kink is formed below eigen value 1 then what to do?

What's the difference between factor analysis and PCA?

Sorry I have a question here.... If I want to perform bivariate correlations of the factors after EFA, how do I transform data from the multiple variables into data for a single factor for the correlation analysis with other factors? Thanks for your time and attention.

Hi can you also create a video on confirmatory factor analysis please?

I have a query, the variance of the variables, for each factor, in the component matrix, is same, as the variance of the variables for each factor in the rotation matrix, I don't see any difference between the two methods, what is the difference between the two methods?

I just dont understand the difference between EFA and PCA then? Isnt this PCA?

Can you Do a video for principal axes and maximum likelihood?

pls if you are online please help me by how to analysis logistic regression with more than 13 independent variable and how to check and write interpreted report

PCA is not EFA. You said "In Statistics Exploratory Factor Analysis is also called Principal Component Analysis (PCA)" but that's not true. Those are two different analyses. That's a common confusion, but pls, check the differences.

Thanks for the wonderful lecture. I have a question in EFA: The assumption that the measurement errors are not correlated between them? This assumption isn't valid in reality ?

At 9:22 figures do not match your explanation. This creates confusion. Can you please check it?

I appreciate the simplification but EFA and PCA are two different tecniques based on different extraction methods. The data used as example require EFA but your procedure is about PCA....

thanks for the video. this is mostly PCA , rather than factor analysis. they are different.

Hello. thanks for your video. i have a qoestion. Is it necessary to do exploratory factor analysis to perform structural equation analysis of a theoretically determined structure?

Hi, thank you very much! It reminded me a lot about multicolinearity in linear regression, are they related somehow?

🙏🙏🙏🙏

what is identity matrix? is it component matrix?

Is it possible to conduct Strucutural Equation Modeling in datatab?

Hello Hannah, can the underlying factors be seen as independent variables and the observeable phenomena as dependent variables? Best regards!

Can i have only one factor?

Am I missing something here??? Why is the entire video on PCA...

Nice....Thank you so much..How many respondents are needed to do this EFA? Is there any literature to support this? Im planning to do pilot test on factors that affect staff retention.But for sure, its a lot.So, I would like to lessen the factors.Thank you in advance

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