landlines and cellphones, you'll still get the same sort of phenomenon.
You'll only get about 5% of the people
to actually answer the phone and cooperate with you.
Well, a 5% response rate is hardly what you'd call a good sample.
And it's hardly what you would be willing to defend as a probability sample.
Now, even if we draw a probability sample, we may have coverage errors, but
certainly, we'll have coverage errors in non-probability samples.
It could be under- or over-coverage,
depending on the frame that we're drawing from.
What we try to do to combat that is
do something called calibrating the weights with auxiliary data.
So what we need is target population control totals that we know,
not necessarily for every individual in the population,
but at least we know grand totals for the population.
And we can adjust our sample weights so
that weighted estimates of these control
variables will match the population or census counts.
So if we do that, then what we hope is that the sample can be
projected to the target population using those covariates.
And we typically had to put a model-based interpretation on that.
So for example, some of the covariates might be counts if
we're doing persons, human population.
Counts by age, race, ethnicity, and
gender might be used as calibrating variables.
So the units we've got in our sample have to be expanded
using weights to represent the full population.
And at least we can do it in such a way that the weights will reproduce
the population control totals.
It doesn't necessarily mean that we do it for
all those other y-variables we're trying to estimate.
But if we can do it for
the control totals, then that's a step in the right direction.
So we'll learn more on how to do that in later sections.