Stratified Random Assignment

But, we recognize that our sample has several intact or homogeneous subgroups.For instance, in a study of college students, we might expect that students are relatively homogeneous with respect to class or year.It should be clear from the graphs that the blocking design in this case will yield the stronger treatment effect.

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And, there is no reason that the people in different blocks need to be segregated or separated from each other.

In other words, blocking doesn't necessarily affect anything that you do with the research participants.

If you are wrong -- if different college-level classes aren't relatively homogeneous with respect to your measures -- you will actually be hurt by blocking (you'll get a less powerful estimate of the treatment effect). You need to consider carefully whether the groups are relatively homogeneous.

If you are measuring political attitudes, for instance, is it reasonable to believe that freshmen are more like each other than they are like sophomores or juniors?

Instead, blocking is a strategy for grouping people in your data analysis in order to reduce noise -- it is an strategy.

Second, you will only benefit from a blocking design if you are correct in your hunch that the blocks are more homogeneous than the entire sample is.

Notice that the mean difference is still the same as it was for the entire sample -- about 10 points within each block.

But also notice that the variability of the posttest is much less than it was for the entire sample. The two figures show that we haven't changed the signal in moving to blocking -- there is still about a 10-point posttest difference.

This will assure that the groups are very homogeneous.

Let's look at what is happening within the third block.

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