Factor analysis – help

Home Forums Methodspace discussion Factor analysis – help

Viewing 4 posts - 1 through 4 (of 4 total)
  • Author
  • #2988

    My name is João
    I do my PhD in geriatric care and use factor analysis in two scales with 28 items and 48 items. I have more than 1000 participants (20 participants: 1 item).

    My question: what is the best option to remove the items: the communalities or the factor loading. In my data i have some items with communalities less them 0,4 but have factor loading high than 0,3 (for Hair et al 2000) is acceptable for sample with more than 300 participants. If I remove some items I increase the % total of variance (just a few) and reduce the nº of factors (10 to 9 or 8)


    I attach in file with same of results
    What is the best choice?


    if i were to weigh on this issue, unless you have a VERY sound theory of why you should keep a 10-dimensional scale, i would completely ignore SPSS’ software default of eigenvalues-greater-than-one and use a much more robust method for factor extraction like Horn’s Parallel Analysis…

    just out of pure intuition, with a sample size greater than 1000 i think i a lot of those eigenvalues are more of a sampling artifact than real dimensions within your scale…unless you are really expecting to have 10 dimensions 


    Hi Oscar


    Thanks for or answer. I translate the scale, in the original the authers found 6 factors.

    Best Regards


    uhm… so you’re in the process of validating a translated scale, huh?

    in that case, i think your method of choice may not be the most appropriate one to answer your research question. this scale as already been developed and it’s already known that 6 dimensions exist within it. what you should be trying here is a confirmatory factor analysis and not an exploratory one, because what you’re trying to do is testing for invariance across cultures/language translation of whatever latent variable that test is measuring.


    you have quite a big sample size so you should have no problems running a confirmatory factor analysis or some sort of structural equation model of your choice. but definitely not an exploratory factor analysis (unless you’re using it as some sort of descriptive guidance)

Viewing 4 posts - 1 through 4 (of 4 total)
  • You must be logged in to reply to this topic.