The use of apparent time differences to study language change in progress has been a basic analytical construct in quantitative sociolinguistics for over 30 years. The basic assumption underlying the construct is that, unless there is evidence to the contrary, differences among generations of similar adults mirror actual diachronic developments in a language: the speech of each generation is assumed to reflect the language more or less as it existed at the time when that generation learned the language. In providing a mirror of real time change, apparent time forms the basis of a conceptual framework for exploring language change in progress. However, the basic assumptions that underlie apparent time have never been fully tested. This article tests those assumptions by comparing apparent time data from two recent random sample telephone surveys of Texas speech with real time data from the Linguistic Atlas of the Gulf States, which was conducted some 15 years before the telephone surveys. The real time differences between the linguistic atlas data and the data from the telephone surveys provide strong support for the apparent time construct. Whenever apparent time data in the telephone surveys clearly suggest change in progress, the atlas data show substantially fewer innovative forms. Whenever the apparent time data suggest stable variation, the atlas data are virtually identical to that from the more recent surveys. Whenever the relationships between real and apparent time data are unclear, sorting out mitigating factors, such as nativity and subregional residence, clarifies and confirms the relationships. The results of our test of the apparent time construct suggest that it is unquestionably a valid and useful analytical tool.
Although variationists have explored the diffusion of linguistic changes from one social group to another and from one linguistic environment to another in some detail, they have done much less work on the spatial diffusion of changes. In fact, Trudgill's (1974, 1975) use of Hagerstrand's gravity model to explain in hierarchical diffusion of innovations in East Anglia and Norway was the only systematic account of spatial diffusion in the literature. This article uses data from the random sample telephone survey portion of a Survey of Oklahoma Dialects (SOD) to explore the spatial diffusion of linguistic innovations in Oklahoma. It analyzes that data using a variety of techniques of computer cartography and the General Linear Models procedure in SAS. The data clearly show that, whereas some linguistic innovations diffuse hierarchically (as linguists have long contended), others diffuse contrahierarchically, while still others diffuse in complex patterns that show characteristics of both contagious and hierarchical diffusion. An analysis of the barriers to and amplifiers of diffusion suggests that these different types of diffusion are a consequence of the different social meanings that linguistic forms carry.
Although dialectology was among the first disciplines to use the survey as a research tool, dialectologists, unlike researchers in other social sciences, have done little work in assessing the effects of their survey methods on their results. This paper attempts to begin a dialog on the effects of methods on results in dialectology by comparing results from five surveys which overlap both in their geographic coverage and in some of the linguistic information they elicit. The surveys differ in their methods of administration (face-to-face interviews vs. telephone interviews), sample construction (purposive vs. random), and the kinds of data they elicit (observations of behavior vs. self-reports). Our comparison of the different surveys shows that while different modes of administration have little effect on results, even slight differences in survey populations affect results significantly, as does the kind of data elicited. Surprisingly, self-reports seem to reflect the linguistic behavior of a population for some features better than observations of behavior do. The effects of the type of sample used are not clear from this study, although random samples have the advantage of explicitly accounting for sampling error and allow for a wide range of inferential statistics that cannot be used with purposive samples. Finally, the comparison suggests that there is no single 'best' type of survey. Different research problems require different kinds of surveys. What is important is that samples not be constructed in haphazard ways and that we explicitly take into account the effects of our methods on our results.
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