DATA WITHOUT (OPERATIONAL) VARIABLES.

Quantitative sociology has grown by burrowing methods from the experimental sciences even though. for the most part, our data are observational. Where the procedures of experimental science can he applied, data analysis can exploit simplifying assumptions because good experimental design removes correlations among independent variables that exist nature, outside the experiment, as well as effects or unmeasured variables. Where the science is of necessity, observational, these simplifications can not be guaranteed and, as a result, the analyses reached through by use of some of the standard "workhorse" techniques of the statistical repertoire may not be valid and conclusions reached by the application of these techniques are in doubt, This paper explores an alternative framework for data analysis in quantitative sociology, bypassing the statistics associated with experimental methods. Specifically, it explores generalizations of the method and quantitative theory used by physical surveyors, generalizing them to the needs of observational data. Application of this framework to text, including editorials and free answers to questionnaires as well as application to (social) survey data, supports their its for these purposes and the cartographic methods suggest that micro theories embedded in these methods reduce the load of a priori assumptions "normally" required for both text analysis and survey analysis. The applications suggest a research path applicable to "ordinary" sociological variables, including education, income, occupation, and gender. that shifts the burden of argument away from variance explained criteria and toward an integration of theory and method, guided by principles of parsimony and consistency. [ABSTRACT FROM AUTHOR]