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This paper reports trends in educational assortative marriage from 1940 to 2003 in the United States. Analyses of census and Current Population Survey data show that educational homogamy decreased from 1940 to 1960 but increased from 1960 to 2003. From 1960 to the early 1970s, increases in educational homogamy were generated by decreasing intermarriage among groups of relatively well-educated persons. College graduates, in particular; were increasingly likely to marry each other rather than those with less education. Beginning in the early 1970s, however; continued increases in the odds of educational homogamy were generated by decreases in intermarriage at both ends of the education distribution. Most striking is the decline in the odds that those with very low levels of education marry up. Intermarriage between college graduates and those with "some college" continued to decline but at a more gradual pace. As intermarriage declined at the extremes of the education distribution, intermarriage among those in the middle portion of the distribution increased. These trends, which are similar for a broad cross section of married couples and for newlyweds, are consistent with a growing social divide between those with very low levels of education and those with more education in the United States.

Most discussions of ordinal variables in the sociological literature debate the suitability of linear regression and structural equation methods when some variables are ordinal. Largely ignored in these discussions are methods for ordinal variables that are natural extensions of probit and logit models for dichotomous variables. If ordinal variables are discrete realizations of unmeasured continuous variables, these methods allow one to include ordinal dependent and independent variables into structural equation models in a way that (I) explicitly recognizes their ordinality, (2) avoids arbitrary assumptions about their scale, and (3) allows for analysis of continuous, dichotomous, and ordinal variables within a common statistical framework. These models rely on assumed probability distributions of the continuous variables that underly the observed ordinal variables, but these assumptions are testable. The models can be estimated using a number of commonly used statistical programs. As is illustrated by an empirical example, ordered probit and logit models, like their dichotomous counterparts, take account of the ceiling andfloor restrictions on models that include ordinal variables, whereas the linear regression model does not. Empirical social research has benefited during the past two decades from the application of structural equation models for statistical analysis and causal interpretation of multivariate relationships (e.g., Goldberger and Duncan, 1973; Bielby and Hauser, 1977). Structural equation methods have mainly been applied to problems in which variables are measured on a continuous scale, a reflection of the availability of the theories of multivariate analysis and general linear models for continuous variables. A recurring methodological issue has been how to treat variables measured on an ordinal scale when multiple regression and structural equation methods would otherwise be appropriate tools. Many articles have appeared in this journal (e.g.

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The study of intergenerational mobility and most population research are governed by a two-generation (parent-to-offspring) view of intergenerational influence, to the neglect of the effects of grandparents and other ancestors and nonresident contemporary kin. While appropriate for some populations in some periods, this perspective may omit important sources of intergenerational continuity of family-based social inequality. Social institutions, which transcend individual lives, help support multigenerational influence, particularly at the extreme top and bottom of the social hierarchy, but to some extent in the middle as well. Multigenerational influence also works through demographic processes because families influence subsequent generations through differential fertility and survival, migration, and marriage patterns, as well as through direct transmission of socioeconomic rewards, statuses, and positions. Future research should attend more closely to multigenerational effects; to the tandem nature of demographic and socioeconomic reproduction; and to data, measures, and models that transcend coresident nuclear families.