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Although there are a great variety of experimental design variations, we can classify and organize them using a simple signal-to-noise ratio metaphor. In this metaphor, we assume that what we observe or see can be divided into two components, the signal and the noise (by the way, this is directly analogous to the true score theory of measurement). The figure, for instance, shows a time series with a slightly downward slope. But because there is so much variability or noise in the series, it is difficult even to detect the downward slope. When we divide the series into its two components, we can clearly see the slope.

In most research, the signal is related to the key variable of interest -- the construct you're trying to measure, the program or treatment that's being implemented. The noise consists of all of the random factors in the situation that make it harder to see the signal -- the lighting in the room, local distractions, how people felt that day, etc. We can construct a ratio of these two by dividing the signal by the noise. In research, we want the signal to be high relative to the noise. For instance, if you have a very powerful treatment or program (i.e., strong signal) and very good measurement (i.e., low noise) you will have a better chance of seeing the effect of the program than if you have either a strong program and weak measurement or a weak program and strong measurement.

With this in mind, we can now classify the experimental designs into two categories: signal enhancers or noise reducers. Notice that doing either of these things -- enhancing signal or reducing noise -- improves the quality of the research. The signal-enhancing experimental designs are called the factorial designs. In these designs, the focus is almost entirely on the setup of the program or treatment, its components and its major dimensions. In a typical factorial design we would examine a number of different variations of a treatment.

There are two major types of noise-reducing experimental designs: covariance designs and blocking designs. In these designs we typically use information about the makeup of the sample or about pre-program variables to remove some of the noise in our study.

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