The following set of questions was sent to me by email from a colleague in a university in the Gulf:
I am conducting a research where I am trying to compare expectations of the teachers and expectations of the students about English studies under five different categories. The questions are designed differently, but mostly on a likert scale with four options (Strongly Agree to Strongly Disagree). The survey was distributed through Qualtrics which calculated the median for me. I contacted a statistician and he suggested Factor Analysis for statistical significance. […]Here are the questions that I wanted to ask:
(1) Is Factor Analysis a perfect test for this study?
(2) How can I spread data on an SPSS sheet? Screen shots will be very helpful in this regard.
(3) Running the test-a step by step process (screen shots).
As I have written often in this blog, the kinds of statistical procedures one should use depend on research questions. Factor analysis can used in order to discover ‘hidden’ psychological constructs (latent variables) in a data set, or to confirm whether such constructs exist. However, the research objective is “to compare expectations”, and it does not involve finding hidden constructs, as far as I can tell. It is difficult for me to see how Factor Analysis would be useful to answer this question.
It would be possible to use ‘confirmatory’ factor analysis if you want to make sure that all the items in each scale (the observed variables) are measuring the same thing (latent variable). However, the interpretation of factor analysis output is not always straightforward. In fact it has been suggested that, inexpertly done, factor analysis is no more useful than a set of tarot cards. Besides, there are simpler methods to confirm the internal consistency of each scale (e.g., estimate Cronbach’s α).
Note that even if you do perform a confirmatory factor analysis that will not answer your research question, which involves comparing two populations. One way to make this comparison, after consolidating the data, is a cross-tabulation, followed up by an x-square test (or similar) to check for statistical significance.
A good resource for learning how to use SPSS is Andy Field’s Discovering Statistics Using SPSS
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Featured image: Programmable calculator by Weedwhacker128 [CC BY-SA 3.0 or GFDL], via Wikimedia Commons
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