Combining SD and statistics

[R.M.Mooy@telecom.tno.nl]

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Dear all,

In a project dealing with the effects of (among other things) ICT
services on the interconnectiveness of society, we've come to the point
at which we would like to quantify an SD-model. To do this, we hope to
use statistical techniques. However, my knowledge of statistics is very
basic and I'm struggling with the process of quantification.

I'd appreciate it if anyone could help me out with this! Details of the
issue are below.

Thanks!

Rutger Mooy
TNO Telecom
the Netherlands
From: <R.M.Mooy@telecom.tno.nl>


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BACKGROUND
We have available a fairly large data set with survey results across a
population. These data are non-longitudinal, that is, they do not follow
specific individuals over time. Survey questions range from (for
example) ""do you have a mobile phone?"" to ""are you satisfied with the
amount of communication you have with your close friends?"".=20
We hope to structure this information into a model that can help us deal
with policy questions. The focus of the model would be the mutual
interaction between Information and Communication Technologies (ICTs)
and certain Social Processes and phenomena (also known as Social
Capital).

APPROACH
>From theory, literature, expert opinion and intuition, we have come up
with a qualitative model that maps out a number of variables relating to
each other, showing some very interesting feedback structures. To
explore this further, we'd like to use statistical techniques, probably
regression and path analysis, to locate the direction and strength of
the various effects we'd expect to be present. We then plan to feed
these quantified effects into the model, which can then be used in
simulation runs.

TIME
One of the main issues is that it's very hard to specify the time
component in the modeled processes. That is to say, we cannot validate
the speed at which things are happening, and we will have to make
assumptions about that.

COMBINING STATISTICS AND SD
Let's say we have questioned the individuals in the population on
variables X and Y, and that we (from theory, expert opinion, intuition,
etc.) hypothesize that X is actually causing Y. We can run a statistical
regression analysis estimating the effect of X on Y, all other variables
held constant. We would be, in fact, estimating the coefficients a and
b in the formula: Y =3D a + b*X.
The main question is: how do we put those coefficients into an
appropriate formula in the SD Model? How does one 'translate' the
statistical output into input for the system dynamics model? This is the
process that I'm most unsure about. Additional questions may be: do we
have enough information to be able to estimate the relationships in the
model? Can we include the concept of change-over-time working with this
data?

Any help is appreciated!
From: <R.M.Mooy@telecom.tno.nl>