Response Propensities

Loading...

From the course by University of Maryland, College Park

Dealing With Missing Data

77 ratings

University of Maryland, College Park

Dealing With Missing Data

77 ratings

Course 5 of 7 in the Specialization Survey Data Collection and Analytics

This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®.

From the lesson

Specific Steps

Specific steps in weighting include computing base weights, adjusting if there are cases whose eligibility we are unsure of, adjusting for nonresponse, and using covariates to calibrate the sample to external population controls. We flesh out the general steps with specific details here.

Response Propensities4:16

Meet the Instructors

Richard Valliant, Ph.D.
Research Professor
Joint Program in Survey Methodology

In this video, we'll talk more about how to estimate those response propensities

for non-response adjustment.

Now, one general procedure is this.

We can do a binary regression.

So we regress our response variable on covariates.

And logistic regression is the typical thing that gets used, but

you could use other things, probit, complementary, log-log.

They'll all tend to give similar predicted probabilities of response.

So we've got to have on each unit, whether it responded or not, and

on both respondents and non-respondents we've got to have the covariate values.

No missing data there to fit this model.

So we get all these propensities estimated for both the non-respondents and

the respondents, and we form groups at that point.

So we sort the respondents and

non-respondents from low to high based on the estimated response propensity.

And then we divide them into groups.

So in the group of low-response propensity units, we put respondents and

non-respondents into this group that have the lowest propensity.

And then down among the high ones, we've grouped ones that are similar,

in the sense of having similar response propensities.

The nice thing about this is we've created this one variable that we can use for

sorting that is kind of an amalgamation of the different covariates.

So this is a nice summary method of doing things.

So you divide the file into groups, as I said.

5 is popular for the number of groups, but it doesn't have to be 5.

If you've got a big sample, you can certainly create more than that.

And it'll make more homogeneous groups,

in the sense that the range of propensities is pretty small.

So you may recognize this as the same sort of thing that people use for

analyzing observational data, where ideally you'd like to have

had a designed experiment where you randomized people to be treated or not.

But you've just got found data, and

you want to estimate kind of a pseudo-assignment

probability to treatment or control.

The same sort of thing.

This idea is just playing off the developments by Rosenbaum and

Rubin for observational data analysis.

So the general procedure then is use one non-response adjustment within each group.

So what it does, by using a single adjustment, it smooths out

the effects of any extreme propensities produced by binary regression.

So that's usually thought of as being

good because we don't necessarily trust the model completely.

So if we form five classes,

that means we've just got five non-response adjustments.

So within a cell, what do we do?

We could use the unweighted response rate.

We could use the survey weighted response rate.

We could use the average of the propensities, or

we could use the median propensity.

All those will be quite similar if the range in each cell is pretty small.

So that's one reason why creating a lot

of cells is a good idea if you've got the sample to support it.

So later on we'll see some software that will actually do this for you.

Explore our Catalog

Join for free and get personalized recommendations, updates and offers.

Coursera provides universal access to the world’s best education, partnering with top universities and organizations to offer courses online.

© 2019 Coursera Inc. All rights reserved.

Download on the App Store

Get it on Google Play