Some Consequences of Measurement Error in Survey Data (original) (raw)

1974, American Journal of Political Science

socialization data, this paper first presents some estimates of the extent of measurement error in several standard face sheet items. After the presence of measurement error is demonstrated, two techniques involving multiple indicators and observations over time are employed to estimate the effects of measurement error on bivariate correlation coefficients with party identification providing the substantive vehicle of the analysis. In general, the analysis suggests that random measurement error may have a major impact on our coefficients and thereby result in misleading inferences. The advent of data archives such as the Inter-University Consortium for Political Research has been a boon to researchers wishing to engage in secondary analysis.' However, the reliance on data collected by others has a number of limitations, some quite obvious and others less so. In the former category is the likelihood that important variables were omitted in the data collection or that key concepts were not operationalized in a way suitable for the secondary analyst. But a more subtle problem of secondary analysis is that the investigator often has little feel for the quality of the data, for the extent and nature of the measurement error in the data. Hence, this paper will present some estimates of the amount of measurement error for some standard face sheet items in two survey data sets collected by a social science institute renowned for its quality control procedures. Then the effects of measurement error on correlation coefficients will be evaluated by a multiple-indicator approach and an observations-overtime strategy, both of which involve the use of path analysis techniques. By measurement error is meant any deviation from the true value of a *I am grateful to Aage Clausen, David Leege, and Robert Lehnen for their helpful comments and suggestions, and to M.