Abstract
Although scientists from various disciplines stress the importance of estimating random measurement error, these discussions have little effect on experimenters who do behavioral sociological research. There have been two important results: (i) many experimenters continue to adopt a laissez-faire approach to measurement error, and (ii) there are very few discussions that illustrate its practical importance in laboratory research. In this article I examine the role of abstract measurement theory in modern experimental sociology, giving particular emphasis to the improvement of data analysis and theory building. I detail the cause and effect of random measurement error in the laboratory and review how the reliability of behavioral data is estimated and interpreted. Discussed are issues of statistical power, Type I and Type II errors, internal and external validity, and the prospects for cumulative theory
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