If you want to report bivariate correlations of salary
with your other variables, I'd do so in a table-formatted correlation matrix. Readers will generally appreciate knowing other bivariate correlations as well, as between age
and competence
, and they really don't take any extra space to include, e.g.:
Table 1. Fake correlation matrix.
Variable Salary Gender Age Education
Competence -.50** -.70*** .15 .40*
Education .30 -.10 .20
Age .30 .00
Gender .20
Note. *$p<.05$. **Dilbert Principle. ***Tactical surrender.
In Table 1, $|r|<.40$ do not achieve $p<.05$; only larger, significant correlations get asterisks. This is the easy, conventional way to embed information about null hypothesis tests of no relationship.
As @Penguin_Knight says, your figure does imply a structural equation model where salary
is regressed onto the other four variables simultaneously (i.e., multiple regression, not Pearson's $r$). Since you're not estimating latent variables, it would please the structural equation modelers among us if you use rectangular boxes instead of circles around your variables – this indicates that you measured your variables directly (simple sums or averages of several items on a scale would also count as measured directly enough). Figures with all rectangular boxes represent path analyses, which are just structural equation models with only measured variables...and multiple regression models are just very simple path analyses! In that sense, you actually have fit very simple SEMs.
Standard path diagrams put regression coefficients next to unidirectional (regressive or asymmetric) paths. I would recommend following this convention as well. If you want to represent information about null hypothesis tests visually, path diagrams sometimes do this by drawing paths as dotted lines instead of solid lines to indicate that the path coefficient did not differ significantly from zero. If you have more unusual hypotheses in mind, I don't think you should try to depict these visually, unless you've got very many unusual hypotheses of the same kind to represent.