SEM Series (2016) 3. Exploratory Factor Analysis (EFA)

all right moving right along next is exploratory factor analysis so what I would do first things first I would go back to the data set and save it we've made several minor changes here and there I mean who had save this as and I'm going to save it as trimmed and no missing let's see no missing there we go save so now all of our changes are recorded and if we make a mistake somewhere we're good to go and I'm going to go ahead and reorder these variables look we have these guys in the end here because we imputed them we replaced the missing values I'm going to resource this column sort ascending smus a sure you want to do this the answer is yes but do you want to save it as a different thing no okay we're in order now I'm just going to put age ID gender frequency experience all at the bottom again okay there we go the next thing to do is a factor analysis I'm excited I like factor analyses so analyze dimension reduction factor analysis what I'm going to stick in there we're going to start with everything throw it all in there all the the reflective latent measures this is critical to bear in mind you must have reflective not formative reflective latent meaning multi indicator measures if you have formative measures don't include them in the EFA if you have categorical variables like gender don't include them the EFA if you have demographics that are clearly not part of a reflective latent construct don't include them in the EFA or CFA they don't belong in a reflective measurement model you're only going to include reflective latent factors hope that was clear enough cool throw this in here if you're not sure what reflective versus formative means please refer to my YouTube video called formative versus reflective measures and a factor analysis I think it's called that something like that okay throw these all over descriptives I've done this times before reproduced kmo continued extraction I like to use maximum likelihood why well that's the same algorithm that Amos is going to use when we do the confirmatory factor analysis I like to do it based on I gain values instead of a fixed number of factors at least initially just to see what it's going to give me how many iterations do we want to allow 25 is fine continued rotation I like to use Promax it's less forgiving but we might have to switch if we have issues continue noting in scores although just FYI if you wanted to save each of the factors as a variable is called factor scores then you click here and save variables and that'll give you however many factors you came up with in your pattern matrix it will create that many more new variables to represent each of those factors in it if set I'm not going to check that okay cancel in the options I am going to suppress small coefficients at point three because I'm really not interested in loadings less than 0.5 and we need them to be at least point to difference I've talked about this in other videos I'm not going to go into depth here okay this is more procedural video anyway we want to look at km okay Emily looks good 935 you want this above 0.7 um ideally about 0.9 you're at the sig value to be significant this is all part of adequacy we're going to talk about adequacy validity convergent discriminant and reliability okay back here we look at the extraction column and we want to see if there's anything less than about 0.3 we're at point three a decision called the eighth order the line dude do looking pretty good okay moving on and we have total variance explained that we will look at this cumulative column it came up with six factors how many were we expecting well if we go back to our model we were expecting one two three four five six it came up with exactly what we wanted this rarely happens so I'm kind of surprised and this doesn't provide us an opportunity to do mitigation strategies so maybe see my other videos for mitigation strategies okay it explains sixty four and a half percent of the variance in the model that's good we want more than sixty percent at a minimum we want more than fifty percent but again above sixty percent is ideal skip the factor matrix skip the goodness fit go down to the reproduced we want a number here less than about five percent we're looking pretty good and pattern matrix is looking stellar ish who actually we do have some issues we have a few issues this is fun I don't like it when just works so let's do this one time a typical use looks fabulous is there no cross loadings anywhere um all the items loaded on to a single factor factor five decision quality not so fabulous I'm still good but not fabulous look we have 0.40 seven that's fairly low we also have these two other items from information acquisition that loaded with all the decision quality items that's a problem we'll have to resolve that separately information acquisition loaded onto its own factor but look at those loadings they're awful so I'm not sure what to do about that I'll have to address that next joy looks joyful no problems there whatsoever playfulness looks incredible and usefulness looks incredible as well so the only real problem is this discriminant validity issue between the decision quality and information acquisition which my guess is is causing the convergent validity issue with information acquisition so what would you do here I would actually just run another factor analysis but get rid of everything except decision quality and information acquisition there we go and just run it again with just those two sets of items and looking good looking good really what I want to do is go down to the pattern matrix good it did come up with two factors that's what we wanted but you can see there are some issues decision quality six is loading most equally on both sides that is the first one I would delete so let's do that factor analysis again decision quality six sayonara K write it again jump down to the pattern matrix decision quality one loading on both sides hey look at these loadings though those are looking better okay this one no good decision quality one you may say hey James wait wait wait what is this it's above one it was last time two we're going to ignore what it is called a Haywood case we're going to ignore this Haywood case until we resolve other issues because it'll probably just resolve itself so decision quality one you are gone kicked off the island there we go jump down the pattern matrix looking better but look at this decision quality eight not really contributing very well I'm going to drop decision quality gate and pattern matrix much better this is borderline we might keep it this is also borderline we might keep it what I'm going to do at this point is I'm going to recreate the larger pattern matrix and see if everything is resolved if not we can see where we'll go probably decision quality seven and into acquisition five will be the next to be eliminated so back to the full factor analysis we're going to throw everything in there except decision quality one six and eight do yep run it again and I am just going to do a few cursory things I'm going to jump down here looks like we still have six factors excellent good variance explained actually better than before and we have only three percent on or doesn't residuals this time and here's the pattern matrix and it already looks better okay decision quality that looks really good information acquisition also very good Wow actually I wasn't expecting it to be that good um and everything else looks just as good as before well I might do is drop information acquisition five it is still fairly low and you can see these loadings here point seven point seven point seven 6m4 these aren't going to average out to above 0.7 which is problem if I want to verify this what I might do is do reliability analysis aniline is scale reliability and just stick in those information acquisition items here I'll pull it over and then go to statistics and do a scale of item deleted continue and okay and what this is going to tell us is if dropping that item will actually do us any good so it was me go back over here to the pattern matrix it was information acquisition five now if we go down to the reliability analysis click here if you look at this last column it says what our cronbach's alpha would be if we deleted each of these items the current convict self is 0.8 4 - but if we deleted information acquisition 5 it would go up to point 8 4 6 this isn't a big difference and so if I was struggling if I wanted to keep all these items I'm fully justified in keeping all these items even though that is a low loading most likely scenario is it will bump up a little bit during the confirmatory factor analysis in Amos so I can keep it if I really don't care and these are scales I made up myself and and I had the liberty to do so then I might just drop information acquisition 5 which is what I am going to do at this point so I'm going to run this one more time drop into X 5 watch what happens to see makes big differences that's an uptick which is good 3 percents the same ooh ok so it actually caused some problems it threw in a new loading here above 0.3 what what happened is information acquisition 5 helped distinguish us from decision quality whereas now we're having a hard time distinguishing yourself so I'm going to retain information acquisition 5 even though this is a greater than 0.2 difference it did bring up that discriminant sort of cost loading issue so my final pattern matrix is actually going to be the one with information acquisition 5 still in it here we go run it what do I report I report the kmo say it's awesome I report the cig say it's awesome these are all under adequacy under communalities this is this is another adequacy measure I look at the extraction column and I say all of mine all my communalities were above 0.3 looks like they are the lowest one is this one point three nine seven and then I'd say the six factor model explains sixty six point three percent of the variance which is good and then I'd say we had less than three percent non redundant residuals which is great and here's the pattern matrix and I'd say as evidence of convergent validity we have all the loadings above 0.5 except this one point 4 which way which I'd mention and then evidence of discriminant validity is we had no strong cross loadings another bit of evidence for discriminant validity is this factor correlation matrix we can look and see at all these non diagonal values and make sure they're not above 0.7 which would indicate sharing a majority of the variance so the closest one is this factor for two factors six I'm guessing that is information acquisition and decision quality I'd go here check four and six yep those are those two and they are highly related but not so related that they're sharing a majority of their variance so that's the closest one what would I report I would report the pattern matrix at a minimum you may also want to report this factor correlation matrix okay that is adequacy convergent validity discriminant validity if we want to do reliability you just do like I did before go do a cronbach's alpha scale reliability analysis we did it for information acquisition move those over we'll do another one for decision quality but not all decision quality items will they have two three four five and seven two three four five and do two six eight loops so two three four five and seven throw those in there okay and report this number here 0.90 one what I like to do is just stick it at the top of my pattern matrix so this point 901 I would go stick it right here that was decision quality so I'd replaced this 4 with 0.9 on one and that put cronbach's alpha right over here okay you want all those cronbach's alphas to be greater than 0.7 if they're not there's actually literature that says it can get down 0.6 particularly if you have only a few items 2 or 3 and that is the EFA

Channel: James Gaskin

Views: 51,161

Rating: 4.9450173 out of 5

Keywords: efa, spss, sem, factor analysis, maximum likelihood, validity

Id: VBsuEBsO3U8

Length: 13min 48sec (828 seconds)

Published: Fri Apr 22 2016