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By B. Guy Peters (auth.)

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Thus, any set of findings from this research design may be over-determined, with a large number of possible and plausible explanations, none of which can be ruled out. The most similar systems design may eliminate a number of possible explanations, but it also admits and fails to address a large number. Further, as argued above, the major problem in isolating extraneous variance may be that it is not possible to identify all the relevant factors that can produce differences among systems. 1) - usually taken to constitute a reasonably homogeneous grouping of countries for analytic purposes - we can easily identify a number of social and economic factors that may have political relevance, all of which vary significantly.

Error variance is that portion of the variance observed in the dependent variable that is a function of random occurrences and errors in measurement. It is the slip of the pen of the data recorder, or by the data entry technician, or sloppy fieldwork on the part of an interviewer, or a host of other occurrences that can produce data points that are just inaccurate. In addition, no measurement of a social phenomenon is a perfect indicator of that underlying characteristic, so a certain amount of measurement error is almost inevitable.

For example, Germany may appear to be a homogeneous country from outside, but the religious cleavage remaining from the seventeenth century still influences the manner in which politics and policy-making function in the two parts of the country today. Other countries such as the Netherlands, Belgium and Switzerland are divided by religion, language, or both (see Lane and Ersson, 1994a). Furthermore, an entire country may be characterised as an outlier in some distribution, because one part of it (geographically or socially) has very extreme values, while the remainder is quite 'normal', lying within the distribution.

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