Multilevel

Version 1

Figure 1: Multi-level model

The independent variable $X_2$ is measured at the macro-level (e.g. neighborhood) and impacts the dependent variable, Y, measured at the micro-level (e.g. neighborhood residents). The independent variable $X_1$ is measured at the micro-level and impacts the dependent variable, Y, as well.

Version 2 (preferred)

Figure 2: Multi-level model (version 2)

Please note that the independent variable at the macro-level $X_2$ will impact all individuals within the same macro-level unit equally. Hence, it impacts the mean score of Y within a macro-level unit. To make it more concrete, a country-level variable like National Income may impact the mean health level of individuals within a country. National Income explains between-country variance in health, not within-country variance in health. To make this more explicit, I recommend you to use the conceptual model above.

Version 3 (subscripts)

Figure 3: Multi-level model (version 3)

Unfortunately, readers who look at version 2 may now think you have two different dependent variables, one at the micro-level and one at the macro-level. You could add subscripts to make more explicit what you mean.
In the figure above we make explicit that we have a dependent variable, Y, that is measured at the micro-level. We explain (between-level) variance in mean scores of Y at the macro-level. The subscript j indexes the different macro-level units. $Y_{.j}$ is shorthand notation for mean score of $Y_{ij}$ for unit j.
At the micro-level we explain (within-level) variance in deviation-scores from the macro-level unit mean.
The subscripts for the independent variables make clear we have two independent variables, one measured at the macro-level and one measures at the micro-level.

In my experience, readers who do not immediately grasp version 2 don’t understand version 3 either. I would therefore recommend sticking to version 2 and perhaps add between and within in the boxes. Add a note to your conceptual model and explain in words what you mean with your labels.

Independent micro-level variable

Figure 4: Multi-level model (version 3)

So far, we only made explicit that our dependent variable (measured at the micro-level) can be split in a within and between component. But the same holds true for independent variables measured at the micro-level.

Independent variables measured at the micro-level may impact:

1. within macro-unit level variation in the dependent variable
   
2. between macro-unit level variation in the dependent variable

Ad1: The between macro-level unit variation in the IV may be related to the between macro-level unit variation in the DV. Stated otherwise, the mean score of our IV across the macro-level units may be related to the mean scores of our DV across the macro-level units. This is path b in the figure above.
Ad2: The within macro-level unit variation in the IV may be related to the within macro-level unit variation in the DV. This is path a in the figure above.

Normally, path b is forgotten. Drawing a conceptual model may help you to remember to at least consider this possible pathway. If you do not include this relationship you are actually implicitly assuming that path a and b are the same. If this is not the case your estimation of path a will be biased.