Watts + Stenner Chapter 7 might be a good guidelines for a good factor interpretation

this should cover:

• research question: why the groundhog scenario?
• why not the apocalypse?
• why is Q an appropriate methodology for this question? (consider Brown 1993)

Headings

Heading Level 2

bold

italics

links

This should cover:

• selection and source of items
• Demographics and selection of participants
• experiences in the field

Baker et al 2017 might be relevant for the item generation.

this would, again, be written by everyone, basically as a reflection on your experiences with Q methodology on this topic. (you can be critical of the method, if you like).

As a jumping off point, I would suggest Brown 1980: the epilogue.

Do you find convincing/fulfilled what BRown promises for the method?

this should cover the steps in the analysis, and a brief explanation for how these (usual) statistical procedures work out in the context of Q:

• correlations
• principal components decomposition
• retention of principal components
• rotation of resulting loadings
• calculation of factor scores

Things to keep in mind that are different about Q:

• the data table is transposed (items are rows, people are variables), we're correlating people not variables
• the items were measured ipsatively i.e., relative to one another.
• standard deviation and mean of every sort was the same.

We probably will not have the space for this to be a fully-fledged statistics report, and that would also ask too much of @Elen93 here.
The task for this section should be to briefly explain how this analysis is slightly different, because it is used in the context of Q.
We can assume that readers roughly now what PCA and correlations are, but we have to talk them through what these mean in the context of Q.

Relevant literature would be Thompson Ch 1-3, and Brown 1980 Ch 4.