Exploratory Factor analysis is built on the premise that the variables in your dataset are correlated. If your variables are independent (meaning they don’t share any common variance), factor analysis simply won’t work. You can’t extract a “construct” from variables that have no relationship to one another. Here Bartlett’s Test of Sphericity becomes important to … Read more
If you are preparing to conduct factor analysis for a thesis, dissertation, or research paper, one of the first questions you must answer is whether your data are suitable for exploratory factor analysis. The Kaiser-Meyer-Olkin (KMO) test helps answer this question by showing whether your questionnaire items share enough common variance to justify extracting underlying … Read more
If you are writing a Master’s thesis, PhD dissertation, or journal article using survey data, you may have reached the stage where you need to check whether your questionnaire items actually measure the constructs you intended to measure. This is where Exploratory Factor Analysis (EFA) becomes important. and this page discuss about how to conduct … Read more