I have a technology acceptance model where I assume that 10 independent variables (each latent variable is composed of three items, based on a 1-5 likert scale) should influence the actual use (dependent variable) of a contactless payment system. Only the dependent variable is composed of two items, the first one measure the frequency on a 1-5 likert scale (1.never - 2. few times per year - 3. few times per month - 4. few times per week - 5. every day) while the second one measure the number of uses (so it is an open ended question, linked to the previous item).
I need to conduct a SEM analysis so I should start with an exploratory factor analysis.
My sample is composed of 350 cases and I've found that for the two items measuring actual use the distributions are strictly not normal. In particular, only 48% declared to use the system and usage ranged from 1 to 730 per user over a period of one year. The first item has a Skewness of 1,535 and a Kurtosis of 2,180 and the second item has a Skewness of 6,934 and a Kurtosis of 54,817.
So I would like to ask if it is possible to conduct the analysis. Should I first standardize all the variables, because of the different scales of measure, and then transform both the two actual use variables to normalize the distributions?
Thank you so much, it is for my thesis and I have big big troubles