0:08

As we sample people, or records, or networks, we often encounter

situations in which the frames aren't quite what we want them to be.

They're cheap, they're convenient, they're accessible but

they're not quite what we want them to be.

We're lacking something in them.

Or there's some feature of them that poses a problem for us.

And we find ourselves encountering this issue called weights.

Weights come up in the survey framework quite often.

And what we're going to do in this part of unit six in our lecture

on weights, a first of three lectures on weights.

We're going to talk about weights for

a particular problem that we face that we've already looked at.

But I want to go back through it in more detail and

talk about the basic principles of weighting.

That way, when you encounter these kinds of things in survey data,

you'll be a little more comfortable with them, I hope.

So that you will understand some of the origins of the weights and

why they're created.

And why they may be useful to you as you're analyzing data.

1:14

You may also find this useful as a way to think about how you might do weighting for

your own survey, although this is a bit sketchy.

It's a bit brief as a treatment.

And you really ought to consult someone who's done this before,

before attempting some of the things I'm going to talk about.

Even though the illustrations here are fairly simple.

So we're going to talk about weights for over or undersampling,

something we looked at actually with respect to stratified sampling.

And we're going to talk about the framework for weighting,

what the basic idea of weighting is about.

And then weighted estimation, how one computes weighted estimates.

And then specifically what happens in weighting for

under and oversampling or oversampling.

So the basic weighting framework is something like a funnel and

undoing a funnel.

We start with a frame, a population list or a pseudo population list.

As we know sometimes the frame is not an exact one to one representation of

the population with capital N elements.

And we know very little about them.

So this blue bar here is meant to represent a long skinny data file,

a spreadsheet.

That has lots and lots of records but

very little information that we know about each.

And then what we do is apply our funnel, our sampling fraction.

We're going to take a subset of that population through some

probability mechanism as we've been talking about it so far.

To get a sample that is much shorter, but

the data is much more detailed, much fatter.

And so that change in the shape of the box is very significant.

Fewer lines, but a lot of information about each of the elements, and

that's our sample.

A sample of size lowercase n, with the sampling fraction n/N.

Some probability of being selected or

being included in the sample that we'll call pi.

3:09

But there we're going to compute estimates, as we've seen before.

That was the fourth step in our operation.

We're going to compute something like a mean.

And what we want to be able to do is project that to a population.

And you'll notice that we're actually going backwards now, to the population.

We want to make statements about the capital N elements in the population now.

We want to create if you will an artificial box out there, a projected box.

It's not dark blue, it's a light blue.

It is the same number of rows as our original population but

the same number of columns as our sample.

And there we're going to in order to do that undo our funnel.

We're going to push thing back out and

allow them to flare out to that full population.

But it's going to be sparsely populated.

Because we only have a limited number of things in the sample that we're going

to use for this projection.

And so we're going to go backwards with an inverse of our sampling rate.

Capital N over lowercase n is kind of an inflation factor to go from

sample to population.

4:06

And there then we can compute things in that artificial population

that might involve the original mean of the population.

We know it's going to be an estimate.

And we know that estimate is an error.

And we have a way to calculate what that error is by looking at standard errors.

And using that then in making statements about the uncertainty of our estimates.

So that's the weighting process then.

From frame to sample to artificial predicted population

upon which we create estimates.

And we retain information from the sample that will allow us to calculate standard

errors.

Estimates that we can use in making uncertainty statements about our

sample estimates for the population.

4:52

Now, in an epsem sampling system, that's one that we've looked at before,

equal probability of selection method.

The sampling probability, pi sub i for

each case is the same, it's pi, it's the same for every case.

And it turns out that in many of these circumstances it's the same sampling.

It's the same as the sampling fraction, lower case n over capital N.

And suppose that's what we've got.

We have a system then that has equal chances for everyone and

we've calculated on weighted estimates.

We've calculated estimates on which we sum up the value for

some characteristic y across the lower case n elements in the sample.

And we divide that sum by the total number of cases in the sample.

But everything is getting a representation that is the same.

And the numerator, every case gets its y and

the denominator, every case gets counted one time.

So for example, if we had a population list of 2,000, and

we drew a sample of 20 with equal chance.

So that the chance selection of any given element is 20 from 2,000, 1 in 100.

Then we're going to undo that not at a global sample, or an overall sample level.

What we're going to do is undo it case by case.

We're going to take the inverse of that selection probability as a weight,

everybody in that sample of 20 will have a weight of 100.

That is that weight means that every case has a representation

of itself and 99 others in the population.

Now we know that's wrong,

we know that they are not 99 other people exactly like each of those 20 cases.

But that error is what we're going to estimate in our variance estimation and

standard error calculation.

6:35

But the weighting then is an inflation.

But here the weights are all the same and so an unweighted estimate is fine.

Because we don't need to worry about that.

But what we find are that there are circumstances where we have non-epsem

samples.

And we're going to look at a particular case in a moment where those probabilities

are not the same across the cases.

And now when we go to do our computation.

We're going to need to take into account the inverse of that sampling rate.

The inverse of that pi sub i for each case.

As a weighting factor for each case in the numerator and

each case in the denominator of our mean.

So that we have a weighted mean now that is more complicated.

But we've got software to help us do this calculation.

Where the wi is the inverse of that probability of selection and

the yi is the same thing we had before.

It's just that we're multiplying the two together to get a weighted contribution.

And in the denominator of our mean, we sum up not counts of one but

counts of their equivalent to the weights.

All right, this unweighted mean that we just looked at is a special case of this.

When where all the ws are the same they're all equal to 100.

They're going to cancel from across the numerator and denominator.

So constant weights cancel.

Our unweighted mean's just a special case of this one.

7:55

Again, our basic approach then is to weight by 1 over that

selected probability.

That is we're going to count each sample person 1 over Pi sub i times.

That means we've got to keep track for

every case of the probability of selection.

Now, you should know this only to know that in the background that's what

the sampling statistician, the survey folks are doing.

They're keeping track of this kind of thing.

At least in this particular probability sampling framework.

8:23

So let's go through an illustration to look at this weighted estimation and

how it works when we do over sampling.

And I'm going to take a simple example of assuming that we're going to sample 10th

grade students in the US.

The US education system has 12 levels,

first through the 12th.

And the 10th graders are two years away from completing the system.

We might be selecting them for purposes of studying them in terms of test scores.

We want to understand how well they're doing.

We're going to compare then to students in other countries.

By administering a very similar test across the countries.

And do some cross national comparisons.

Now, this particular layout here is kind of a way of beginning to think about this.

There are about 4 million, it's actually somewhat less than that.

But just as a round number, 4 million 10th grade students in the United States.

And suppose that we've got a sample design that says that we're

going to select 12,000 of them.

Now, 12,000 from 4 million, that's a complicated kind of rate.

12,000 divided by 4 million, I've converted that into a sampling rate that

you'll notice in that sampling rate column.

Basically I took 12,000 divided by 4,000 and

divided the numerator by 12,000 and the denominator by 12,000.

So the numerator is now 1, the denominator is 333.3333.

That is the selection probability.

Which means that the weight is the inverse of that, 333.33.

Every student in our sample represents themselves and 332 and

a third other students in the population.

But if we're doing an equal chance selection here,

the weight may as well be 1.

It's the same for everybody.

So we're going to need to think about the base weight here and

alternative representations.

Because we're going to think about these in two different ways.

That weight would cancel.

10:24

Now again, here we've got our overall population.

And we could do a simple random sample.

We don't need to worry about anything else other than randomization.

No, except that we know that we want to stratify.

We want to group them in such a way.

That we can control the distribution of the sample across dimensions of variables.

That we know about in advance in our sample selection stratification.

Suppose that what we could do is take our 10th grade students and

divide them into two groups.

Those that have potentially lower incomes and those that have potentially higher.

Except the way we're going to do this is use identification for

each of the students.

About whether or not their school has programs for free or

reduced priced lunches.

Whether there's a high fraction of the students in their school who receive

free or reduced price lunches.

Those schools that have higher fractions of their children receiving free or

reduced price lunches, subsidized lunches if you will.

Tend to have students who come from lower socioeconomic status.

And those that have low proportions, those tend to have students coming from higher.

So we're going to have to keep our highs and lows mixed together.

A high proportion means lower income.

A low proportion means higher income.

And that's what we're doing in this particular case.

And we can stratify our population as shown here.

Now we've added two rows.

We've filled in the table.

The high row, about 20% of our students come

from schools with higher fractions of free or reduced price lunches.

And the low, the 3.2 million, the 80%,

they are coming from schools that have higher incomes among the students.

But there's a sample size now that we draw from each of these.

Our 12,000 are now divided up across these two groups, stratified sampling.

In this particular case what we're doing.

Is doing stratified proportionally allocated sampling.

We're using the saved sampling rate in each of these.

And if we apply that overall sampling rate to each of the two groups,

the 800,000 and the 3.2 million.

We get the sample size that's shown here, 2,400 and 9,600.

Or if we prefer 2,400 is 20% of the 12,000.

That's a proportionate representation in terms of our miniaturization of

our population.

But nonetheless, the weight within each of the groups is the same,

it's the inverse of that sampling fraction.

And we can go with that weight or a reduced value for it.

It's all going to cancel,

it's all going to wash out in the estimation in the end anyway.

All right, so that sample size allocation is a proportionate allocation,

2,400 from high, 9,600 from low.

2,400 from lower income schools that have a higher proportion of

children with lower incomes, 9,600 from those with higher.

13:33

This is that proportionate allocation that we've looked at before.

It has equal probabilities of the elements in each of the groups, but

some investigators may prefer something different.

They may find this constraining.

They may say, I'm really interested in providing estimates for

these two different types of students that are equally precise.

Or I want to compare the two groups.

I want to be able to compare the test scores between these groups and

draw some conclusions.

So they may prefer an allocation that looks like this.

Instead of taking a proportionate distribution in the sample,

they take an equal number distribution.

We've seen this before, 6,000 from each group.

But now our sampling fractions are different.

The sampling fraction in the high stratum,

the high fraction of reduced price or free lunches.

Has a rate of 1/133, not 1/333, but 1/133.

We're sampling at a higher rate,

by specifying that sample size, than the overall rate.

And similarly for the low, the 6,000 there is at a lower rate.

It's got a larger denominator in the sampling fraction, 1 in 533.33.

The weights as the inverse of those work out to be the same kind of

thing that we've seen before now, 133 and 533.

And those weights can be reduced as well.

They can be reduced to a ratio of 4 to 1.

Now, there's no overall weight.

There's no overall weight for Weight A or Weight B.

But nonetheless we have these different weights for the two different groups.

This is a case of oversampling.

We have oversampled the high group.

And under-sampled thereby the low group.

That means that we end up with a smaller weight when we want to combine for

the high group.

And a larger weight for the low group to compensate for the over- and

under-sampling respectively.

Now remember again, we did this because our goal was to do a comparison or

have equally precise estimates for the two groups.

But eventually we will want to combine them to give us an overall estimate, and

now the weights are necessary, and they will vary.

15:49

Okay, so the equal allocation,

we can say, at the risk of oversimplifying it, for comparing two groups.

The proportionate allocation for

representing the population, combining the groups.

Now let's look at the implication of this.

The consequences if we look at something like mean test scores among these 10th

graders.

And what we're going to do is look at the two different samples.

The proportionate and the equal allocation.

So for starting with the proportionate allocation here now I've stripped some

things out, I've taken out all the population data but

the mean test score for the high and the low children are different.

The high has a lower mean test score, than the low.

The average across both groups now in the population is about 88.

We have a score of 72 for the high group and 92 for the low group.

And again remember those correspond to low income and high income.

And now we have our proportion allocation of 2400 from the high and 9600 from

the low and because that sample is in the same distribution as the population.

16:57

If we take those means, we end up with the same mean for this particular sample.

Whether we use the weight of 333.33 or weight of one or

no weight at all but that's implied the weight of one, we get the same mean back.

So it's very good for combining these results without any particular

manipulations required to get it.

So those weights are the same as what we've seen before but

they are not really necessary in our calculations.

But now let's suppose that we look at the disproportioned allocation.

Same mean test scores within the two groups, but now we're doing 6,000 high.

Over sampling the high, and under sampling the low.

And when we do those averaging now across the sample

values where they've got a mean test score of 72 for the high.

And 92 for the low, we see that our mean is 82 and not 88.

Our mean has been reduced because we have a disproportionately

larger number of reduced or free lunches and

we've drawn the mean down to their lower mean test score.

18:04

The weights can be used to compensate for this.

And so the weights that we see here are shown in the next columns.

And the weights of 133 and 533, or 1 and 4,

can be used to calculate our weighted result.

And we get back to the actual population mean.

The weights restore so to speak the balance.

So in the unweighted case for

our disproportionate allocation if we've got 6,000 kids with means of 72,

and 6,000 kids with means of 92, the overall average is 82.

But if we apply our weights now of 4 for those children who

have scores of 92 and 1 for those who have scores of 72.

On average, we end up restoring our mean to 88.

And the same thing's true whether we use that weight of 4 and 1 or

we use the weight of 533 and 133.

We get that same weighted mean that's our population mean.

19:06

Okay, so this oversampling and undersampling can be compensated,

used very effectively for certain purposes.

And compensated to allow us to do not only the oversampling and

undersampling for comparisons but also combining.

But we have to be careful with the weights,

we have to keep track of the probabilities.

And then take their inverses as weighting factors,

and use those inverses as weighting factors in our estimation as well.