Michael Atherton wrote:
In other words,
the three variables (parent involvement, teacher quality, and class size)
may account for say 20% of the total variance, leaving 80% of the
variance unaccounted for by this model, but we don't know
because this article never reports what amount of the influence
on student MATH achievement was not accounted for by their
model. This is really important because without knowing the
influence of the unknown factors you can't truly calculate the
influence of the known factors.
MB:
Bravo, really for this succint description of the strengths and pitfalls of
regression analysis -- especially the way incomplete models (and labels in fine
print) distort conclusions. I believe its also true that "predictive value" isn't
the same as "causation," however much social scientists would like it to be.
Regression analysis can predict the likelihood of certain outcomes (like an
increase or decrease in MATH achievement) because the equation shows how much math
achievement has changed when the model variables (like teacher quality, class
size, etc.) changed. Just because these things move together, doesn't mean one
causes the other....
Martha Bolinger
ECCO
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