3.2 - Design Effects and Intraclass Correlation - Part 1

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Sampling People, Networks and Records

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University of Michigan

Sampling People, Networks and Records

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Cours 4 sur 7 dans Specialization Survey Data Collection and Analytics

Good data collection is built on good samples. But the samples can be chosen in many ways. Samples can be haphazard or convenient selections of persons, or records, or networks, or other units, but one questions the quality of such samples, especially what these selection methods mean for drawing good conclusions about a population after data collection and analysis is done. Samples can be more carefully selected based on a researcher’s judgment, but one then questions whether that judgment can be biased by personal factors. Samples can also be draw in statistically rigorous and careful ways, using random selection and control methods to provide sound representation and cost control. It is these last kinds of samples that will be discussed in this course. We will examine simple random sampling that can be used for sampling persons or records, cluster sampling that can be used to sample groups of persons or records or networks, stratification which can be applied to simple random and cluster samples, systematic selection, and stratified multistage samples. The course concludes with a brief overview of how to estimate and summarize the uncertainty of randomized sampling.

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Saving money using cluster sampling

3.2 - Design Effects and Intraclass Correlation - Part 118:40

3.2 - Design Effects and Intraclass Correlation - Part 215:51

Rencontrer les enseignants

James M Lepkowski
Research Professor
Survey Research Center, Institute for Social Research

Hello, and welcome back to our continuing discussion about cluster sampling here in

unit three of our six unit session on sampling people, records and network.

In this lecture, the second of our lectures on cluster sampling, we're going

to be talking about the effect the impact of cluster sampling on our estimation.

And as we do this, we're going to take the simple complex

sampling ideas that we just developed in lecture one.

And compare and

contrast them to what we find in simple random samples of the same size.

And we will have in this particular case then some new expressions,

some new terminology.

Even some new tools to use in helping us design samples,

design effects and intraclass correlation that we will describe as we go along.

Now as we do this in the lectures, there are three parts to this about

the so-called design effects, which I've abbreviated in the upper left as d-e-f-f.

We'll see that abbreviation in a minute.

And roh, r-o-h, that we'll describe in a minute.

And then we have some calculations.

And between design effect and roh, or

somewhere in the middle of the roh, we're going to take a little break and

then come back to the rest of it to finish off discussing roh in calculation.

So let's begin by talking about the idea of

comparing a cluster sample to a simple random sample.

And I just grab this image here, because when we do comparisons there are all sorts

of things that we do in the way of comparisons.

Here's a comparison of heights and buildings.

Now this doesn't include the really tall buildings in the Persian Gulf today.

But, we might be looking at the comparison

of the height of buildings such as the Taipei 101 in Taiwan.

And the Petronas towers in Malaysia.

The Sear's tower in the United States.

The Jin Mao building which has been superseded but in Shanghai.

And the Empire State building in the Unite States.

And then I put this one in here just because this relates to my

background where this is being recorded

here at the University of Michigan Is very near to Detroit.

And Detroit sits on big salt deposits.

It's not well known for this, but those salt mines go way down.

Almost to the depth equivalent of the building.

So we would compare these in terms of this measure.

What we're going to do is the same thing for cluster sampling.

We need a way to compare cluster samples to simple random.

We need some quantitated measure.

We're going to use the sampling variance.

Because if you recall for cluster samples, simple cluster samples they're

unbiased for the mean, just like simple random samples.

So whether we have a simple random sample or

a cluster sample on average we're getting the right answer.

There is no effective design there.

But those sampling variances are computed in different ways.

For simple random samples based on element variability for

cluster samples based on the cluster variability.

And so we need a way to take that quantified statement and

compare between the two.

So we're going to be comparing precision.

Well precision you recall would involve standard errors, but

it's really that measure of precision our sampling variance we're going to be doing.

We need to make sure that there's some commonality in the comparison.

I don't want to compare a simple random sample of a 1000 to a cluster sample

of 240.

That wouldn't make sense.

So we're going to put them on the same basis, the equal sample size,

not equal costs.

Simple random samples will be much more expensive than cluster samples.

So if we had to have equal costs,

then the cluster samples will have much better precision in simple random samples.

But, when we have equal sample size, what we find is,

the cluster samples have much larger variances than simple random samples.

All right, so we're going to compare sampling variances,

in this particular case.

Now, if you recall our illustration in lecture two, where we had ten classrooms,

each with 24 children selected from a 1,000 classrooms.

So the total population had 240,000 children.

We selected 240 children across the school classrooms.

And did that by taking ten classrooms of 24.

And then we asked each of those children, or obtained for each of the children,

their immunization history, and 160 of them, turned out weren't fully immunized.

That portion is an unbiased estimate of the population proportion

among all 24,000 children.

Well, we could have, at that point, done a simple random sample though instead, and

suppose we got the same basic result, or something neighboring that.

And then calculated the simple random sampling variance.

And so here's the calculation from a simple random sample of the same size

of 240.

Actually, it uses the results from our cluster sample.

We didn't draw a separate sample.

What we're going to do is treat our cluster sample both as a cluster sample,

we know what that variance is and we'll come back to it in a minute.

And as a simple random sample of the same size.

And just take the data and ignore the clustering in it and

calculate the variance.

Now that allows us then from one sample to get

the two estimates that we need to compare.

And here is the sampling variance from the cluster, not the cluster sample, but

the simple random sample.

You probably don't remember what we had before, but

we're going to compare what we have for the cluster sample.

And as we see it here that we have this quantity that

involves variance of the mean.

Where what we're going to do is take that variance of the proportion

actually rather than a mean, although I think about them in the same way, and

compare it to the variance of a simple random sample.

For the same estimate, so we're taking a ratio of variances.

We won't take a difference.

When you're comparing building heights you'd say,

well this one is 250 feet taller than the other.

But what we're doing here is saying, no, no, this one 6% larger.

Or this one is 100% larger by taking the ratio.

And we're going to label this where we're comparing the variance of the proportion

for our cluster sample for sample 240.

That's that numerator I circled to the variance of a simple random sample of

the same size 240 school children.

The sampling variance in the denominator.

And that's what we're going to label as the Design Effect.

Design Effect.

And we're using an abbreviation here.

This was invented by a social scientist, who was among statisticians known

as a statistician, and among social scientists known as a sociologist.

And he came from that the different tradition statistication would

treat this not a single symbol that might use something like a delta.

Or maybe just to denote that it's on a squared scale a delta squared.

Okay, they're going to go with the Greek letter, why Greek?

Well, classical reasons but, spare notations as possible.

Whereas, the social science community tends to prefer mnemonic devices,

memory devices so here D for design and e-f-f for effect,

deff which is a ratio of variances for that sample proportion p.

All right, same sample size numerator and denominator and

at the last line in the very bottom of our display.

We see the ratio, you probably didn't recall that the sampling

variance we computed from our sample of size 240 was 0.00276.

And for the simple random sampling equivalent taking the same data and

treating it as a simple random sample came out to be 0.0009.

Those numbers are hard to.

Taking differences here would be a real headache.

This is the scale we're getting for a sample of this size involving proportions.

This is what variances would tend to look like.

But what's important is the ratio.

And we notice that the cluster sampling variance is three times larger than

the simple random sampling variance.

A three fold increase.

The same sample size, much reduced cost.

Now you could raise the question.

Well why would I do the cluster sample if I'm going to lose invariance compared to,

I'd rather do the random sample for the same sample size.

No you wouldn't because the simple random sample would cost

much more money to build the list necessary to select it.

Now if you already got that list assembled, that's a different matter.

But here, we don't have a list assembled, and

that's what happens in most of these cases with cluster sampling.

We have a list assembly cost, and

that list assembly cost could be a hundredfold higher.

Let's say it is a hundred fold higher.

It costs us 100 times as much to assemble that list of all of the names as it does

to get a list of the thousand classrooms, select 10 of them and

then go to those classrooms and get the classroom rosters for those 10.

And if it's a hundred fold difference,

a threefold loss in precision from cluster sampling is nothing.

We're still ahead by 33 to 1.

So we're willing to suffer this loss because we save so much money.

Now, we don't often do that calculation.

Often? I don't think I've ever done it at all.

It's just so obvious that the cost would be so much less with a cluster sample.

And I haven't even mentioned the travel cost to go from classroom to classroom.

So it's not about cost here, it's about variance and equal sample sizes.

We lose in precision on that comparison, but it's a convenient kind of comparison.

It's not about now getting these things so

that we can compare this on the basis of the same budget.

That's typically not done although it could be done.

All right, now this design effect comes up all over the place in literature and

it comes up in many different ways.

For example, one way that it comes up is the following.

That design effect is a ratio of variances.

Well if I multiply both sides of it, if I multiply the right hand side and

left hand side of the simple sampling variance.

I get the variance of the proportion, just the numerator the one side is equal to

the design effect times the simple random sampling variance.

That is, the design effect can be thought of as an adjustment

on what happens in simple random sampling.

In this case, as I said,

a three-fold increase in the variance compared to simple random sampling.

And that sometimes is used by people to assess the impact of the cluster sampling

on their design, when they're looking at the analysis.

They've done simple random sampling calculations and they say,

but I think the design effects here, based on other calculations,

are a factor of two or three.

They're going to take the simple random sampling variances and

inflate them correspondingly.

That's one way to use them, that's a little bit crude.

Typically you just want to compute the design effect and be done with it, and

know exactly what that increase in variance was.

But actually this expression is much more useful in designing new surveys, which is

in the next lectures that are coming up, in lectures three and four in this series.

So we'll come back to this.

We're going to take that design effect and use it as an adjustment factor for

simple random sampling variances in order to build up projections

of what sampling variance would look like for cluster samples.

Now the design effect is a function of the differences between clusters.

If you remember, the design effect,

the numerator that cluster variances variability among cluster characteristics.

And so what we're doing is we're comparing variability for

cluster characteristics to variability for element characteristics.

And that design effect is greater than 1.

The clusters are more variable than the elements.

Why?

You know if the elements happened to be.

I got this illustration of sheep down here but

if the elements happened to be tufts of wool, taken from me to the sheep.

We can see there's big differences between white sheep and black sheep.

And that what's going on here,

we're getting bigger differences between than we're getting within.

There's not an even uniform distribution of color of the wool across those sheep.

So what we're contrasting here is heterogeneity between the clusters,

that's the term that's used.

Based on the Greek, hetero for

between if you will and that it's what's in the numerator of our variances.

If we have heterogeneity between

that is we have very great differences between like those sheep.

It also implies something that's very important and that is homogeneity within.

So the maximum contrast that we've got right there is between black and

white sheep which is also the difference between black and white tufts of wool.

And so, that homogeneity within, if it's high,

means that there's also great differences between.

This is, kind of, the essence of the social sciences anyway, particularly,

sociology, in studying groups and group differences.

But, if you study group differences, you're also studying the extent to which,

within groups, there's homogeneity.

The elements are more like one another within the group than they are alike those

that are coming from different groups.

So, the more different clusters are from one another,

the more similar are the elements within clusters one to another.

Now, this is observed empirically.

They revealed that this design effect Is a function of indeed

the extent to which the elements within the clusters are like one another.

They're also it turns out related to how large the clusters are.

So it's not just one factor, it's two.

And so there is a measure of this within cluster correlation that

we're going to be developing called Intracluster correlation intra-

much like the difference between inter- and intra- comparisons.

So I drive on highways in the United States and you'll pass vehicles and

they'll say the intra-state [COUGH] commerce commission.

Between state it's a federal agency that regulates what goes on between the states

as opposed to interstate,

within state commerce where things don't pass outside the state borders.

Well, that's what we're dealing with here.

We're dealing with the Intra,

the within kind of thing, and the cluster phenomena.

And we're going to measure that in terms of the correlation,

the extent to which the elements resemble one another.

Well now for correlation, we typically would use the symbol r for relation.

Right, correlation has that built in to the middle.

But again remember the statisticians.

They prefer the Greek symbol here.

They prefer that Greek letter for the letter r, roh.

All right, I hope that doesn't look like a p.

It's meant to look like that Greek letter roh.

So they're capitalizing on the same sound.

But that kind of thing is spelled R-H-O.

We live in a university environment in which there is housing here for

a community called the Greek Community, organized by the students.

And they have these Greek letters on there.

They're called the Greek Community because their houses are named with

typically two, three letter sequences of Greek letters.

And so you'll see rho used in that letter.

In that case though see, we've got something spelled r-o-h.

Well, that's because again this was specified by a social scientist.

That social scientist wanted a memory device, and

he wanted us to remember what this thing was.

So the term r-o-h is a memory device rather than r-h-o, the Greek letter.

Its a memory device and it stands for

rates, r- a-t-e or R of homogeneity.

Beginning with the letter h, just a rearrangement of the letters of roh.

So it's to remind us that what we're dealing with here is not the Greek letter,

a concept that's outside of our understanding.

It's dealing with the homogeneity within the clusters, which is a direct reflection

of the heterogeneity among them that's driving that variance.

So the rate of homogeneity is a part of this in our design effect then is given

as a factor.

As a part of this and it is built up in terms of the rate of homogeneity and

the number of elements per cluster.

Now A was the number of elements, number of clusters.

B going alphabetically as is done in this particular notation,

is the number of elements we've selected per cluster.

In our case it's all 24 students in the classroom.

But we will do some subsampling in our next lecture.

So in this particular case then, that design effect is driven by,

if you will, a 1.

That's simple random sampling.

That's the base that's there, by how many elements we take per cluster, and

by the rate of homogeneity within the clusters.

Those three factors.

Given that, that allows us then to estimate that homogeneity.

What is it, we expected to be a number that's less than one.

And indeed, we can manufacture an estimate by taking the design effect

expression we just were looking at.

And deriving, backing out of it, that rate of homogeneity.

We subtract one from the design effect and divide by b minus 1.

And we see that here our rate of homogeneity is 0.088.

So we've got our result now that tells us what the homogeneity is and

the consequences of it.

When we have 24 elements per cluster selected.

Okay, let's take a little break from this.

We're going to resume and talk about homogeneity a little bit further,

and the consequences of homogeneity in our next part of lecture two, thank you.

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