![]() Factor scores are dimensionless variables, so it doesn’t matter if the absolute value changes with the number of items. Don’t use cut offs for factor loadings, but plot them for easier interpretation. Kaiser's criterion is the worst way to select number of factors, we established that like 20 years ago. There is also consensus on many things you have mentioned in psychometrics literature. You need to practice coding more or just use a GUI like Jamovi. Most of what you mentioned suggest a lack of training.Īs for software, this sounds like a lack of programming skills. To be perfectly honest, factor analysis is an advanced technique that you as a "budding data scientist" probably shouldn’t be doing in a first place. ? Bro Likert scales aren’t even continuous how do I expect them to be normally distributed? It has potential but as of right now it seems so disorganized.Įdit: I saw one paper that said in order to use ML procedures your data has to be sufficiently normal. I just don’t get why people like these sets of analyses. I can keep going but I think I’ve proved my point enough. Yet the number of items itself is arbitrary, as shown above! But…if you get rid of it, I may bump your reliability score up just a smidge to get you into your journal’s recommended range.Īlthough it’s comical, it’s still important to realize that one’s factor score is a function of the number of items that factor has. Turns out I don’t really like variable, although I give little to no justification why. Psss, hey you! Yeah, you! Want a high reliability estimate? Well, you may be in luck. 30.īut reliability analyses just feel so scheme-y. 50 in the social sciences and some saying it can be as low as. I see one paper saying lambda should be higher than. No one seems to agree on what value these loadings should be. I see a version of EFA that uses ordinal data instead, but alas it’s paywalled behind a very expensive, niche statistical software.įactor loadings aren’t spared from criticism. It’s clear that EFA requires continuous scales, but it doesn’t seem to be obvious why using Likert scales is an acceptable replacement. An example is with retaining those factors with eigenvalues greater than one - almost half of the literature accepts this and half rejects it! Yet the most obvious involves Likert scaled items. Not to mention, there are certain techniques that have almost no modern literature support that are considered the gold standard. From gathering items to reliability analysis, the fact that there is a potentially infinite number of models that could work is incredibly scary. R has a function available but, in my opinion, it is incredibly complicated even for me.īut even if you do manage to find a software package, literally every step of the EFA process is so damn subjective. R-bloggers - blog aggregator with statistics articles generally done with R software.Īs a budding data scientist, I love all things statistics except exploratory factor analysis and I can’t wrap my head around why it’s so popular.įor starters, it’s incredibly difficult to find a publicly available software package that can do EFAs with minor headaches. Kaggle Self posts with throwaway accounts will be deleted by AutoModerator Memes and image macros are not acceptable forms of content. Just because it has a statistic in it doesn't make it statistics. ![]() Please try to keep submissions on topic and of high quality. They will be swiftly removed, so don't waste your time! Please kindly post those over at: r/homeworkhelp. This is not a subreddit for homework questions. ![]() All Posts Require One of the Following Tags in the Post Title! If you do not flag your post, automoderator will delete it: Tag
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