Hello Jemechi,

yes, that's no problem. PDO just represents the context in which Climate and behavior are related.

I would be much more concerned with the conceptual similarity of both climated and behavior, that is confounder bias (e.g., common method bias) or simultaneity, see

Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21, 1086-1120. doi:10.1016/j.leaqua.2010.10.010

There is a really nice book by Phil Rosenzweig (the halo effect) which describes your situation quite well, namely when people within organizations or workgroups are requested to rate some exposure or process variable they draw on their experienced success or performance when making a judgement ("we are sucessfull--hence our communication style seems to be good").

The same might be the case in your study. People re-infer the climate on the basis on their innovation behavior..

Best,

--Holger

Statistically, your result can happen if the interaction term (PDO x Climate) leads to changes in the slope of the independent variable (PDO). Informally, you could think of these changes in slope as "cancelling" each other out, so that the total effect of without regard to the interaction his non-significant.

If Climate is a categorical variable, this is will be easy to graph out, but if it is a continuous variable, then you will need to search for more detailed information on how to graph an interaction effect between two continuous variables.

Yes it is valid

Tendrías que revisar el constructo de las variables independiente y dependiente

das entender que es causal, Análisis de normalidad (paso1 si la variable es cuantitativo), si no es normal puedes optar (Paso 3), en la revisión de literatura qué dicen los investigadores al respecto, si se tratara de relación seria si existe relación entre lugar o espacio con el clima, revisar los instrumentos de medida, si se termina el trabajo tendrás que justiciar con evidencias que no se cumplió por tales motivos.

Folks,

as David already said, a non-significant main effect in the presence of interaction simply means that the regression plane of X and Z on Y spans an area ranging from negative to positive and the middle line (=first-order-effect) is non-significant / close to parallel. A non-significant main effect (i.e., the marginalized effect of X summed over values of Z) will then occur if Z is symmetrically distributed such hat negative relationships between X and Y (in the lower area of Z) and positive relationships between X and Y (in the upper area of Z) will cancel each other out.

No need for panic. Actually, this is the "best" result you can hope for as you show that the moderator really matters.

Here is an example:

https://online.stat.psu.edu/stat501/book/export/html/947

See the upper diagram and think of drawing a line in the middle of the cube (not matter which axis as both will come close to being parallel.

All the best,

--Holger

Thank you for your answer! What if the moderator and outcome variables have SIGNIFICANT relationship? Is the significant interaction effect still valid? Holger Steinmetz David L Morgan Arunima K V

Hi, think again in terms of a regression plane in a cube spanning the X-Z-Y space. There are different scenarios:

a) In the case of zero effects of X and Z, the plane is parallel to the ground

b) In the case B_X is nonzero but B_Z = zero, the plane has gradience with respect to X

c) In the case of B_X and B_Z being nonzero, the plane has a gradience in both dimensions, resulting in one corner of the plane being higher than the others (if both B's are positive).

In b and c (main effects of X and/or Z), the plane is stiff, that is, the degree of gradience is constant across all variables. For instance, the slope of X on Y is constant across all levels of Z and vice versa.

Now play around with all possible scenarios that result from bending the plane in all possible ways. In these cases, the slopes are not constant across all levels of the respective other variable. THIS bending of the plane is the essence of an interaction effect. The effect of X on Y becomes conditional on the very level of Z at which you estimate the effect.

Of course, X and Z can both have non-zero slopes (case c above) AND the plane is bent such that the slope of each variable varies with levels of the other variable.

Beyond statistical issues, however, I found the use of the term "moderator effect" more useful in cases of a zero-main-effect of Z (such that Z represents the context that determines the target effect of X) whereas in cases in which both X and Z have an effect, "interaction" should be used.

See this paper in this regard,

VanderWeele, T. J. (2009). On the distinction between interaction and effect modification. Epidemiology, 20(6), 863-871.

In addition, and following VanderWeele, I would prefer the term "effect modifier" instead of "moderator" as this is theoretically (=causally) more explicit (whereas moderator can be applied to any changes of a relationship due to a moderator).

All the best,

--Holger

Dear Holger Steinmetz ,

Thanks for your answers. The information and recommendations you provide are valuable to me.