Relational fixed designs measure the relationship between two or more variables
In a cross-sectional study, the forcus is on relationships between and among variables of a single group. The pattern of relationships between variables may be of interest in its own right, or there may be a concern for establishing causal links. The variables to be included in the study are those needed to provide answers to your research questions (independent-explanatory, dependent-outcome variables) and they are included because of their relevance to the research question. A rule of thumb sometimes proposed is a minimum of 15 participants per variable.
An issue is the homogeneity of the group. For instance, while you may not be interested in gender issues per se, may be males and females are affected by different variables and hence there will be increased variability on the outcome variable. A solution here is to analyse the 2 genders separately, i.e. to perform a subgroup analysis.
Category membership
The identification of membership of particular categories or groups can be difficult and complex. This may appear straightforward for a variable such as age. However, there are situations where even age can be problematic. Gender, as social construct, can raise category difficulties in particular cases.
Possible challenges
Establishing the statistical significance of the relationship between gender and attitude does not enable us to conclude that these variables are causally related. Nor does it, in itself, help in understanding what lies behind this relationship. We need to come up with plausible mechanisms and seek evidence for their existence. Further sub-division of the group allows to control statistically for the effects of such variables (gender-knowledge-concern with safety, see p.159), providing the relevant information has been collected.
References:
Real World Research: A Resource for Social Scientists and Practitioner-Researchers, 2nd Edition. Colin Robson. (p.154-109)
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