I'm building a Structural Equation Model and I'm trying to operationalize my Dependent Variable, the construct of "Investor Behaviour" (= whether or not an investor is willing to invest in a startup). Would it be possibile to operationalize this construct by using as variables 8 different scenarios, obtained via fractional factor design (each one of them made of the following three attributes, combined differently: expert/non expert team, tested/not tested product and tested/not tested market fit)? For each scenario, I will ask to the investor if he/she is willing to invest or not, on a likert scale from 1 to 7?

For instance, one scenario would be: 1) Are you willing to invest in a startup wth a non expert team, but a tested product with a tested market fit? Put a x between 1(not at all) and 7 (for sure) below.

... and so on for each scenario...

Basically, at the end I would have, depending on 8 different scenario, 8 numerical variables, that I could use to operationalize the "Investor Behaviour" construct and insert into my Structural Equation Model. Is that possibile or I'm missing something? Any suggestion?

Thank you in advance, Fed


This is certainly possible; participant responses to your eight scenarios, rated on a 7-point scale, would make fine indicators for an SEM. However, it seems that you are already anticipating three "first-order" latent variables, pertaining to investment behavior based on (1) expertise, (2) product tested-ness, and (3) market tested-ness. As such, you might want to consider specifying a second-order latent variable model (see Beaujean, 2014, for a conceptual description), in which your scenario responses load onto their respective first-order latent variables, which subsequently load onto a second-order "investor behavior" factor.

If you were going to pursue the second-order latent variable modeling strategy, I'd encourage you to consider ensuring that you have at least three indicators (i.e., scenarios) per first-order latent variable, so that each of them would be just-identified (if not over-identified).

The measurement model in lavaan would look something like this (using a marker-variable method of scale-setting):

investor.model<-' expertise=~x1+x2+x3
summary(output.investor<-cfa(investor.model, data=df), fit.measures=TRUE)

Note that this second-order latent variable model model will fit the data equally as well as a model in which only the first-order latent variables were specified. If you want to see, just remove inv.beh=~expertise+prod.test+mark.test from the script. I recommend the second-order model simply because it seems to better represent the theoretical model for investor behavior you seem to be outlining above.


Beaujean, A. A. (2014). Latent variable modeling using R: A step-by-step guide. New York, NY: Routledge.


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