This was extremely helpful. I didnt find any other video on you tube on factor analysis that mentions how to remove a variable if its loading on more than one component. I read it in a book, but didnt quite understand it. Your video has cleared it so very well. Thank you very much Todd Grande.
Being able to actually see the numbers and how they are input into SPSS and excel in other videos is much more helpful then simply reading how to apply and use from a textbook. All of the videos on SPSS and excel will be good to go back over again and again in helping me understand the stats and tests used.
Thorough walk through. There was a lot that I admittedly did not initially understand, so I appreciate the depth of analysis. In all likelihood, I will watch it again in the future.
I actually enjoyed being walked thru this analysis. When I see the process unfold, it helps me better comprehend the concepts that underlie the analysis. That was very helpful, thank you Dr. Grande.
This video contained a lot of new terms and information to me. It was slightly confusing as factor analysis and principle components analysis are different but are used interchangeably. I will need to watch this again and have further reading on these terms to get a grasp on factor analysis.
Well well well. I watch your new videos for entertainment and when I’m avoiding university work. But here I am at 10:30pm actually trying to do university work and I’m again at your channel.
I appreciate Dr. Grande discussing the difference between factor analysis and principal components analysis. At first, I thought these terms were used interchangeably. I personally think that SPSS is confusing in itself, so this may be why I found it challenging following the steps in this video.
The video is helpful in understanding what a factor analysis does, which is to explore the underlying constructs in an assessment in order to determine what the assessment measures. The demonstration on how to conduct a factor analysis is helpful, but I find it somewhat difficult to follow and would likely need further clarification and practice.
Thanks for the video! Anyway, I want to make sure... If there is any component that performed below 0.45 in Rotated Component Matrix, we have to remove it and check again until all components are greater than 0.45, right?
Hi Dr Grande. Thank you for this video. One question for you, what are we supposed to do if the correlation matrix demonstrates variables with more than 0.8 value? If removal is required, is it the variable in the column or in the row?
This is a marvelous video for those who have spent the whole morning struggling to understand PCA! I would like to ask what is the minimum value you consider acceptable in the Rotated Component Matrix
Could you explain why you mentioned that the correlation matrix value should stay withing -0.8 - (+0.8) because I am getting values such as 0.9 and more. So should I remove those items and try again? What exactly does a value over 0.8 mean?
Exploratory factor analysis simply tries to determine which items measure which variables with no expectations of number of variables or which items measure which variables. Confirmatory factor analysis requires you to set the number of expected components prior to running analysis to confirm results are as expected. What would help would be to use the actual item names (labels) in the results....might make it clearer for students to understand what is going on. The high loading simply means that item is measuring that variable (or construct). If the loading isn't high enough, the item is not measuring anything which is bad. If it loads on more than one component, then it is measuring more than one thing which is bad too. Also, I never like to accept any loading less than .600. I think .450 is quite low. Also, important point to remind students to remove one bad item at a time an re-run analysis...typically the lowest or item that loads on more than one component. Removing multiple bad items at a time might lead to the removal of good items being confounded by really poor items. Good luck.
@@mahasarwar5513 Possibly both...more that likely with what you are measuring. If an item loads on more than one component/dimension, then it isn't discriminant from measuring other factors or variables. Or it could just be a bad or vague question that needs to be removed from the model.
Great video Sir, thank you. Could you clarify a little bit on components loading. We look for the item set for which the components "load well" with particular items in rotated component matrix. Would you elaborate more on "loading well"? e.g. 14:19 : why component 4 does not load well with items 7 while 4 having values .844 and .476? both values should be greater than 0.5? or both should be either greater than 0.5 and should similar values? Thank you.
+Melissa Clendaniel I agree this video was confusing. I am no familiar with factor analysis and without using this on a daily basis I would still need to rely on refreshers.
Certainly a wonderful video, not very suitable for one with no background about PCA. I am a beginner, and had read three chapters about PCA from 3 different books to get the background. This video showed me how to implement the theory. Toward the end of the video, when Todd does some iterations to find the best number of secondary variables and PCs, I was wondering how many times the process should be iterated, and found the scree diagrams of iterations become more and more similar to each other, till you reach a point where no variable is required to be eliminated form the list. In other words all of them contribute fairly well to one of the PCs. I should say, I am using MATLAB for this. Thanks Again Todd :)
Sir, could you please explain how to name the extracted factors if two types of variables are loading good on same factor?On what basis the variables are deleted and PCA is run again?
Sir , I'm using stata, are there any specific commands for principal component analysis PCA in PANEL DATA Or Just simply run PCA after standardizing variables?
Hello Dr! Can we apply PCA to two variables in order to find an index which would describe both those variables? For example, the first principal component of PCA applied to height and weight as an index of overweightness. If the case is ranking, does PCA have meaning at all or will the result be the same as an average of height and weight? Thanks!
very nice video explanation. i have one doubt, after caliculation factor analysis, gouping factors , how to find or compare influence factor with dependent or demographic variable
The video was a little loaded for me to follow, but Dr. Grande does a great job of thoroughly explaining the importance of a factor analysis and how to conduct such.
+Candace Fernandez I don't like SPSS so this may be why it was confusing and hard for me to follow. But overall, I also liked the explanation of the importance of a factor analysis.
what you are explaining regarding the correlation matrix around minute 7 is only applicable to the varimax rotation where you wouldn't expect the factors to be correlated, right? i am using direct oblimin as i expect my factors to be somewhat correlated and i'm wondering whether i should be interpreting that table in the same way as you're describing in this video. any help from anyone would be greatly appreciated!
I had a hard time following this but did jot down a few statements. Factor analysis will not tell us what the constructs are (that's somewhat of a subjective judgment), but it will give groupings to look at. Principal Components Analysis is actually different that Factor Analysis although they're often used interchangeably. KMO and Bartlett's Test - KMO cutoff rate is 0.5 (ideally closer to 0.7 or 0.8... anything less is starting to cut it pretty close).
Hello Dr Grande. I am doing a thesis conducting factor analysis. I am using 3 items in my categories. So I am getting only one component and spss is not running my rotated component matrix. Please help me
can somebody tell me why it would be 'a red flag' if the correlation matrix shows values higher than .8 or lower than -.8? If the items measure the same construct I would think it is normal that the responses are highly correlated. I ran the analysis with my data and it showed me high correlations of sometimes above .8, so what does this change about the interpretation now? I would be SO grateful if somebody can help!
ohh wait I search for PCA and start listening to his video and im thinking this voice sounds familiar only to check the channel name and it is non other than Dr Grande!!!
To be honest, I didn't understand a word of this at all. I was confused on how to do it and what the end goal means. I would like Dr. Grande to go over this in class.
Although this seems very complex I could see how it would be helpful in deciding which items to use especailly when creating an instrument. I can imgine this is what my professors in undergrad did to check to see if a test question was a good question or not.
+Mandy Moore I was pretty lost throughout this video, but I agree, determining whether or not test questions are good questions or not is definitely important and I can see how this method can help to decide that.
If the data is binomial (1=correct response, 0=incorrect response) then this technique does not really work. However, there is a way to get 'factors' using the R program. See: it.unt.edu/sites/default/files/binaryfa_l_jds_sep2014_0.pdf
Hello, Respected Sir, EFA of 79 line items scale shows their are 15 factors but all values are loaded in first column. i dont know why.... it means there is only one dimension of scale ? but i does not seem logical. kindly guide me how to handle this.... thanks in anticipation
Hi..thanks for your video - it will definitley help me in understanding and conducting the factor analysis. I have a few questions and hope you can help me. I am asking questions with respect to doing factor analysis for likhert scaled questions. 1. Is a component formed of less than 3 variables acceptable? will it hold onto the reliability test? 2. Do we need to make the questions unidirectional ie either all positive or all negative? 3. what level of variance explained is acceptable like how low is accepted?
+Deepa Navin For latent variables (constructs), 2 items typically minimal so you can run a reliability assessment. No, simply ignore the +/- sign in the loadings..they are really irrelevant it interpretation. You can re-code the reverse items prior to running a reliability assessment so the alpha is correct and all correlation values are positive. No real answer...but higher than 50-60% is good.
A little more difficult to understand, but I do understand that it seeks underlying unobservable variables that are reflected in the observed variables.
+sherrie tilghman I was very lost on this video. I think it was because there were so many different things to check once the factor analysis was completed. I will need a lot of assistance understanding this.
What happens if I change the eigen value from 1 to 0.5 ....by putting it 1 I'm not getting my rotation matrix...it says only one factor is extracted. Please help
What it means is that there are no variables [items] that can be reduced. Thus, it is better to use the whole items to represent the construct you are intending to measure.
I think its more to see i items are actually measuareing the contruct they are intended to measure. This was complex so I'm not sure i fully understood it either.
There was a lot of information to sift through and to examine. I felt lost for the majority of the time. I will need an additional explanation to understand this video fully.
I could understand what you were doing when doing the analysis but it went a bit too fast in the actual steps especially when you were checking off the boxes to run the analysis. I will need to watch this agin.
It's confusing that the principal components analysis is considered a factor analysis but isn't? That's what I think I gathered. Because this is SPSS, I found it simpler and the fact that there is a function called "principle component." It is always harder when there are so many different names.
This was extremely helpful. I didnt find any other video on you tube on factor analysis that mentions how to remove a variable if its loading on more than one component. I read it in a book, but didnt quite understand it. Your video has cleared it so very well. Thank you very much Todd Grande.
Being able to actually see the numbers and how they are input into SPSS and excel in other videos is much more helpful then simply reading how to apply and use from a textbook. All of the videos on SPSS and excel will be good to go back over again and again in helping me understand the stats and tests used.
This video provided a lot of information at once but after watching it a couples times I have a better understanding.
Thanks for the video really! I had no idea what factor analysis was, but with your help I understood it pretty well!
Matched with the text this was a great review of factor analysis. Great video.
Thorough walk through. There was a lot that I admittedly did not initially understand, so I appreciate the depth of analysis. In all likelihood, I will watch it again in the future.
Agree Rosa, good info. Will likely watch again when needed.
I actually enjoyed being walked thru this analysis. When I see the process unfold, it helps me better comprehend the concepts that underlie the analysis. That was very helpful, thank you Dr. Grande.
I agree being a visual learner, seeing all the steps so clearly laid out was very helpful.
I thought this video was a little confusing but I appreciated the steps that showed how to formulate this function.
This video contained a lot of new terms and information to me. It was slightly confusing as factor analysis and principle components analysis are different but are used interchangeably. I will need to watch this again and have further reading on these terms to get a grasp on factor analysis.
I like your presentation Doc. Thanks for this
Well well well. I watch your new videos for entertainment and when I’m avoiding university work. But here I am at 10:30pm actually trying to do university work and I’m again at your channel.
thank you somuch.igot what i waslooking for.
thank you so much. Got the concept of factor analysis clear now!!
You're welcome, thanks for watching -
I appreciate Dr. Grande discussing the difference between factor analysis and principal components analysis. At first, I thought these terms were used interchangeably. I personally think that SPSS is confusing in itself, so this may be why I found it challenging following the steps in this video.
right! i took notes, but until i sit down and do it with an applicable data set, it may remain more of a mystery!
I really appreciate, this video is very helpful
The video is very helpful.
Loads of Thanks Sir
Great lecture, thank you very much.
Could you please explain how to use the results from factor analysis and create a linear model?
The video is helpful in understanding what a factor analysis does, which is to explore the underlying constructs in an assessment in order to determine what the assessment measures. The demonstration on how to conduct a factor analysis is helpful, but I find it somewhat difficult to follow and would likely need further clarification and practice.
Thank you so much! It was very helpful.
You're welcome, thanks for watching -
Thanks for the video! Anyway, I want to make sure... If there is any component that performed below 0.45 in Rotated Component Matrix, we have to remove it and check again until all components are greater than 0.45, right?
Hi Dr Grande. Thank you for this video. One question for you, what are we supposed to do if the correlation matrix demonstrates variables with more than 0.8 value? If removal is required, is it the variable in the column or in the row?
This is a marvelous video for those who have spent the whole morning struggling to understand PCA! I would like to ask what is the minimum value you consider acceptable in the Rotated Component Matrix
0.45 I think!
Great video! Could you please explain why a value more than +/- 0.80 in the correlation matrix would be a red flag?
Could you explain why you mentioned that the correlation matrix value should stay withing -0.8 - (+0.8) because I am getting values such as 0.9 and more. So should I remove those items and try again? What exactly does a value over 0.8 mean?
Exploratory factor analysis simply tries to determine which items measure which variables with no expectations of number of variables or which items measure which variables. Confirmatory factor analysis requires you to set the number of expected components prior to running analysis to confirm results are as expected. What would help would be to use the actual item names (labels) in the results....might make it clearer for students to understand what is going on. The high loading simply means that item is measuring that variable (or construct). If the loading isn't high enough, the item is not measuring anything which is bad. If it loads on more than one component, then it is measuring more than one thing which is bad too. Also, I never like to accept any loading less than .600. I think .450 is quite low. Also, important point to remind students to remove one bad item at a time an re-run analysis...typically the lowest or item that loads on more than one component. Removing multiple bad items at a time might lead to the removal of good items being confounded by really poor items. Good luck.
If any item is loading on more than one component, does that signify issues with our data or issues with the model that we chose to study?
@@mahasarwar5513 Possibly both...more that likely with what you are measuring. If an item loads on more than one component/dimension, then it isn't discriminant from measuring other factors or variables. Or it could just be a bad or vague question that needs to be removed from the model.
Great video Sir, thank you. Could you clarify a little bit on components loading. We look for the item set for which the components "load well" with particular items in rotated component matrix. Would you elaborate more on "loading well"? e.g. 14:19 : why component 4 does not load well with items 7 while 4 having values .844 and .476? both values should be greater than 0.5? or both should be either greater than 0.5 and should similar values? Thank you.
Thanks for your video. Please which analysis will I use to analyse consumer choice for mobile phones using SPSS?
This is one of the videos that I will need to watch more than once I think. Factor analysis is something that is confusing to me.
+Melissa Clendaniel Yes, even with SPSS, this one was really complicated.
+Melissa Clendaniel I agree this video was confusing. I am no familiar with factor analysis and without using this on a daily basis I would still need to rely on refreshers.
Certainly a wonderful video, not very suitable for one with no background about PCA.
I am a beginner, and had read three chapters about PCA from 3 different books to get the background. This video showed me how to implement the theory.
Toward the end of the video, when Todd does some iterations to find the best number of secondary variables and PCs, I was wondering how many times the process should be iterated, and found the scree diagrams of iterations become more and more similar to each other, till you reach a point where no variable is required to be eliminated form the list. In other words all of them contribute fairly well to one of the PCs.
I should say, I am using MATLAB for this.
Thanks Again Todd :)
+Sina Milimon Glad you got a lot out of it! I agree that it is very thorough and explained well.
Sir, could you please explain how to name the extracted factors if two types of variables are loading good on same factor?On what basis the variables are deleted and PCA is run again?
Sir , I'm using stata, are there any specific commands for principal component analysis PCA in PANEL DATA Or Just simply run PCA after standardizing variables?
Thanks.Regarding Item 4 which has a cross loading, should we remove it ?
Hello Dr! Can we apply PCA to two variables in order to find an index which would describe both those variables? For example, the first principal component of PCA applied to height and weight as an index of overweightness. If the case is ranking, does PCA have meaning at all or will the result be the same as an average of height and weight? Thanks!
very nice video explanation. i have one doubt, after caliculation factor analysis, gouping factors , how to find or compare influence factor with dependent or demographic variable
Thank you Sir
The video was a little loaded for me to follow, but Dr. Grande does a great job of thoroughly explaining the importance of a factor analysis and how to conduct such.
+Candace Fernandez I found it to be somewhat confusing too, but it did help me to understand why a factor analysis is conducted.
+Candace Fernandez I don't like SPSS so this may be why it was confusing and hard for me to follow. But overall, I also liked the explanation of the importance of a factor analysis.
Dr. Grande. How do you calculate h2 for factor analysis?
very good
What happens if the Sig. Level in the KMO and Bartlett's Test is 0.000? Or what does it signify?
what you are explaining regarding the correlation matrix around minute 7 is only applicable to the varimax rotation where you wouldn't expect the factors to be correlated, right?
i am using direct oblimin as i expect my factors to be somewhat correlated and i'm wondering whether i should be interpreting that table in the same way as you're describing in this video.
any help from anyone would be greatly appreciated!
Super Helpful. My determinant is too low, how can I improve it?
Dear Professor,
If KMO is exactly .50, can we use it for further analysis?
I had a hard time following this but did jot down a few statements. Factor analysis will not tell us what the constructs are (that's somewhat of a subjective judgment), but it will give groupings to look at. Principal Components Analysis is actually different that Factor Analysis although they're often used interchangeably.
KMO and Bartlett's Test - KMO cutoff rate is 0.5 (ideally closer to 0.7 or 0.8... anything less is starting to cut it pretty close).
thank u, next
I applied this analysis between 10 factors that each one has a spatial data. How can I draw a map for the varimax-rotated factors between them?
I was very confused over this video. This is an area that I would like to have Dr. Grande discuss in class.
+Bethany Elstrom I liked this playlist because there was a lot that I want Dr. Grande to go over with us in class
Hello Dr Grande. I am doing a thesis conducting factor analysis. I am using 3 items in my categories. So I am getting only one component and spss is not running my rotated component matrix. Please help me
can somebody tell me why it would be 'a red flag' if the correlation matrix shows values higher than .8 or lower than -.8? If the items measure the same construct I would think it is normal that the responses are highly correlated. I ran the analysis with my data and it showed me high correlations of sometimes above .8, so what does this change about the interpretation now? I would be SO grateful if somebody can help!
Hi if u r experiencing non-normal data then its okay to continue.
I think it's because you'd be grouping almost the same variable
ohh wait I search for PCA and start listening to his video and im thinking this voice sounds familiar only to check the channel name and it is non other than Dr Grande!!!
Thanks
You're welcome
To be honest, I didn't understand a word of this at all. I was confused on how to do it and what the end goal means. I would like Dr. Grande to go over this in class.
Although this seems very complex I could see how it would be helpful in deciding which items to use especailly when creating an instrument. I can imgine this is what my professors in undergrad did to check to see if a test question was a good question or not.
+Mandy Moore I was pretty lost throughout this video, but I agree, determining whether or not test questions are good questions or not is definitely important and I can see how this method can help to decide that.
Sir Todd Grande can you please suggest me a book and the author name which accept KMO measure of sampling adequacy value of 5 and above
very helpful
Thank you -
Could this same analysis be conducted using item responses from a multiple-choice test (1=correct response, 0=incorrect response)?
If the data is binomial (1=correct response, 0=incorrect response) then this technique does not really work. However, there is a way to get 'factors' using the R program. See: it.unt.edu/sites/default/files/binaryfa_l_jds_sep2014_0.pdf
This was a little complicated, and I wish I understood it better. Including SPSS.
Hello, Respected Sir, EFA of 79 line items scale shows their are 15 factors but all values are loaded in first column. i dont know why.... it means there is only one dimension of scale ? but i does not seem logical. kindly guide me how to handle this.... thanks in anticipation
Hi thanks for lecture ,may I know if you have lecture for mediation analysis ?
Hello! How do I only include factor loadings greater than 0.5
My KMO value comes as 0.459, which is less than 0.5. what does this siginify? Can someone help?
How to know finally which items under each component are extracted based on values
Hi..thanks for your video - it will definitley help me in understanding and conducting the factor analysis. I have a few questions and hope you can help me. I am asking questions with respect to doing factor analysis for likhert scaled questions.
1. Is a component formed of less than 3 variables acceptable? will it hold onto the reliability test?
2. Do we need to make the questions unidirectional ie either all positive or all negative?
3. what level of variance explained is acceptable like how low is accepted?
+Deepa Navin For latent variables (constructs), 2 items typically minimal so you can run a reliability assessment. No, simply ignore the +/- sign in the loadings..they are really irrelevant it interpretation. You can re-code the reverse items prior to running a reliability assessment so the alpha is correct and all correlation values are positive. No real answer...but higher than 50-60% is good.
+Brian Kinard Thanks much for your response
A little more difficult to understand, but I do understand that it seeks underlying unobservable variables that are reflected in the observed variables.
+sherrie tilghman I was very lost on this video. I think it was because there were so many different things to check once the factor analysis was completed. I will need a lot of assistance understanding this.
What happens if I change the eigen value from 1 to 0.5 ....by putting it 1 I'm not getting my rotation matrix...it says only one factor is extracted. Please help
What it means is that there are no variables [items] that can be reduced. Thus, it is better to use the whole items to represent the construct you are intending to measure.
How we can merge PCs into one variable ?
Hi, please can you give references to back up your analysis?
How do you tell a test is unidimensional or multidimensional? Help please!
by the number of predictors. if 1 it's visualized in unidimensional else more then 1 it's multidimensional
Sir where I can get the data file used in the video
how to write hypothesis for above problem
Factor analysis is to explore the underlying constructs and what they measure?
I think its more to see i items are actually measuareing the contruct they are intended to measure. This was complex so I'm not sure i fully understood it either.
Over my head and I (kind of) wish I could try some of these things in SPSS just to see what exactly happens.
There was a lot of information to sift through and to examine. I felt lost for the majority of the time. I will need an additional explanation to understand this video fully.
+Rachel Foster I agree. I too need additional explanation.
I agree
Michelle Robinson
While I am doing the same, but I am not getting the Rotated component metrix. Can anyone tell me the soloution?
*Rotated component matrix table
Dr. Todd you need to leave your email/contacts
I could understand what you were doing when doing the analysis but it went a bit too fast in the actual steps especially when you were checking off the boxes to run the analysis. I will need to watch this agin.
It's confusing that the principal components analysis is considered a factor analysis but isn't? That's what I think I gathered. Because this is SPSS, I found it simpler and the fact that there is a function called "principle component." It is always harder when there are so many different names.
+Alicia Zahn Yeah this was a bit confusing but I just have to watch this over and over to understand!
+Alicia Zahn Yeah this was a bit confusing but I just have to watch this over and over to understand!
Too soft... could not hear properly with maximum volume. I Will watch on repeat. Definitely serve the purpose.
Your volume is very low in each video. Please fix that.
voice is too low
bruh this is how to teach