We use longitudinal data incorporating three generations of Americans to reevaluate the character and consequences of political socialization within the family. Findings about parental influence based on youth coming of age in the 1990s strongly parallel those based on youth socialized in the 1960s. As expected on the basis of social learning theory, children are more likely to adopt their parents' political orientations if the family is highly politicized and if the parents provide consistent cues over time. The direct transmission model is robust, as it withstands an extensive set of controls. Early acquisition of parental characteristics influences the subsequent nature of adult political development.W riting 40 years ago, Jennings and Niemi (1968) questioned the conventional wisdom about the role of parents in shaping the political character of their children. Working from the perspective of social learning theory and drawing on data collected independently from adolescents and their parents, they demonstrated high variability in the political similarity between parents and their children. Especially when judged against the expectations laid down by reliance on retrospective accounts of parental attributes, the results appeared to downgrade the direct transmission model, wherein parental attributes were passed on, wittingly or unwittingly, to their offspring. These outcomes seemed all the more surprising in view of the considerable overall aggregate congruence between the two generations.Somewhat lost in the (over) generalizations flowing out of this and related research were a number of important qualifications. Transmission rates tended to vary in a systematic fashion according to type of political trait. The more concrete, affect-laden, and central the object in question, the more successful was the transmission. More abstract, ephemeral, and historically conditioned attributes were much less successfully passed on. Salience of the political object for the parents was an important conditioner of successful reproduction, as was perceptual accuracy on the part of the child (Acock and Bengston 1980;Percheron and Jennings 1981;Tedin 1980;Westholm 1999). The presence of politically homogeneous parents, and other agents allied with the parents, enhanced the fidelity of transmission (Jennings and Niemi 1974, chap. 6;Tedin 1980). Contextual properties such as larger opinion climates (Jennings and Niemi 1974, 81-82, 161-62) and party systems (Westholm and Niemi 1992) also affected within-family consonance. These specifications and qualifications also lent support to social learning theory explanations of how children come to resemble their parents more in some respects than others. Although not in the tradition of the transmission model, but fully compatible with social learning theory, other inquiries have revealed the importance of communication patterns within the family in shaping the political make-up of the child (e.g., Tims 1986; Valentino and Sears 1998).In this paper we return to the topic of intergener...

In randomized experiments, treatment and control groups should be roughly the same-balanced-in their distributions of pretreatment variables. But how nearly so? Can descriptive comparisons meaningfully be paired with significance tests? If so, should there be several such tests, one for each pretreatment variable, or should there be a single, omnibus test? Could such a test be engineered to give easily computed p-values that are reliable in samples of moderate size, or would simulation be needed for reliable calibration? What new concerns are introduced by random assignment of clusters? Which tests of balance would be optimal?To address these questions, Fisher's randomization inference is applied to the question of balance. Its application suggests the reversal of published conclusions about two studies, one clinical and the other a field experiment in political participation.

If an experimental treatment is experienced by both treated and control group units, tests of hypotheses about causal effects may be difficult to conceptualize, let alone execute. In this article, we show how counterfactual causal models may be written and tested when theories suggest spillover or other network-based interference among experimental units. We show that the “no interference” assumption need not constrain scholars who have interesting questions about interference. We offer researchers the ability to model theories about how treatment given to some units may come to influence outcomes for other units. We further show how to test hypotheses about these causal effects, and we provide tools to enable researchers to assess the operating characteristics of their tests given their own models, designs, test statistics, and data. The conceptual and methodological framework we develop here is particularly applicable to social networks, but may be usefully deployed whenever a researcher wonders about interference between units. Interference between units need not be an untestable assumption; instead, interference is an opportunity to ask meaningful questions about theoretically interesting phenomena.

If "All politics is local," as Tip O'Neill famously stated, then studying politics requires studying place. Yet place, as defined in many studies of "context effects" throughout the social sciences, is often so vague as to hinder the development of understandings about how place and politics interact. In this paper, we borrow from Parsons and Shils to offer a formal conceptualization of "context," for the purposes of using "context" to learn more about individual level political outcomes. Our conceptualization, and a recognition of the statistical Modifiable Areal Unit Problem, lead us to a new measurement strategy: We propose a map-based measure to capture how ordinary people use information about their environments to make decisions about politics. Respondents draw their contexts on maps-deciding the boundaries and limits of the relevant environment-and describe their perceptions of the demographic make-up of these contexts. The evidence is clear: people's pseudoenvironments do not resemble governmental administrative units in shape or content. By "bringing the person back in" to the measurement of context, we are able to marry psychological theories of information processing with sociological theories of racial threat. And, our measure of context allows us to sidestep the Modifiable Areal Unit Problem, a major stumbling block in research on context that prevents scholars from knowing whether they have substantive findings or simply statistical artifacts.

Nearly all hierarchical linear models presented to political science audiences are estimated using maximum likelihood under a repeated sampling interpretation of the results of hypothesis tests. Maximum likelihood estimators have excellent asymptotic properties but less than ideal small sample properties. Multilevel models common in political science have relatively large samples of units like individuals nested within relatively small samples of units like countries. Often these level-2 samples will be so small as to make inference about level-2 effects uninterpretable in the likelihood framework from which they were estimated. When analysts do not have enough data to make a compelling argument for repeated sampling based probabilistic inference, we show how visualization can be a useful way of allowing scientific progress to continue despite lack of fit between research design and asymptotic properties of maximum likelihood estimators.Somewhere along the line in the teaching of statistics in the social sciences, the importance of good judgment got lost amid the minutiae of null hypothesis testing. It is all right, indeed essential, to argue flexibly and in detail for a particular case when you use statistics. Data analysis should not be pointlessly formal. It should make an interesting claim; it should tell a story that an informed audience will care about, and it should do so by intelligent interpretation of appropriate evidence from empirical measurements or observations.—Abelson, 1995, p. 2With neither prior mathematical theory nor intensive prior investigation of the data, throwing half a dozen or more exogenous variables into a regression, probit, or novel maximum-likelihood estimator is pointless. No one knows how they are interrelated, and the high-dimensional parameter space will generate a shimmering pseudo-fit like a bright coat of paint on a boat's rotting hull.—Achen, 1999, p. 26