Stratified Random Assignment Van Gogh Thesis Statement
So how does blocking work to reduce noise in the data?
To see how it works, you have to begin by thinking about the non-blocked study.
If the blocks weren't homogeneous -- their variability was as large as the entire sample's -- we would actually get worse estimates than in the simple randomized experimental case.
We'll see how to analyze data from a randomized block design in the Statistical Analysis of the Randomized Block Design.
Ultimately the decision to block involves judgment on the part of the researcher.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.A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study.For example, one might divide a sample of adults into subgroups by age, like 18-29, 30-39, 40-49, 50-59, and 60 and above.The Randomized Block Design is research design's equivalent to stratified random sampling.Like stratified sampling, randomized block designs are constructed to reduce noise or variance in the data (see Classifying the Experimental Designs). They require that the researcher divide the sample into relatively homogeneous subgroups or blocks (analogous to "strata" in stratified sampling).It should be clear from the graphs that the blocking design in this case will yield the stronger treatment effect.But this is true only because we did a good job assuring that the blocks were homogeneous.Within each of our four blocks, we would implement the simple post-only randomized experiment. First, to an external observer, it may not be apparent that you are blocking.You would be implementing the same design in each block.