Time series, or longitudinal, data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time series properties of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. Janet M. Box-Steffensmeier, John R. Freeman, Jon C. Pevehouse and Matthew P. Hitt cover a wide range of topics including ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting. This book is aimed at researchers and graduate students who have taken at least one course in multivariate regression. Examples are drawn from several areas of social science, including political behavior, elections, international conflict, criminology, and comparative political economy.
I nterest groups often make their preferences known on cases before the U.S. Supreme Court via amicus curiae briefs. In evaluating the case and related arguments, we posit that judges take into account more than just the number of supporters for the liberal and conservative positions. Specifically, judges' decisions may also reflect the relative power of the groups. We use network position to measure interest group power in U.S. Supreme Court cases from 1946 to 2001. We find that the effect of interest group power is minimal in times of heavily advantaged cases. However, when the two sides of a case are approximately equal in the number of briefs, such power is a valuable signal to judges. We also show that justice ideology moderates the effect of liberal interest group power. The results corroborate previous findings on the influence of amicus curiae briefs and add a nuanced understanding of the conditions under which the quality and reputation of interest groups matter, not just the quantity.
Numeric political appeals represent a prevalent but overlooked domain of public opinion research. When can quantitative information change political attitudes, and is this change trumped by partisan effects? We analyze how numeracy-or individual differences in citizens' ability to process and apply numeric policy information-moderates the effectiveness of numeric political appeals on a moderately salient policy issue. Results show that those low in numeracy exhibit a strong party-cue effect, treating numeric information in a superficial and heuristic fashion. Conversely, those high in numeracy are persuaded by numeric information, even when it is sponsored by the opposing party, overcoming the party-cue effect. Our results make clear that overlooking numeric ability when analyzing quantitative political appeals can mask significant persuasion effects, and we build on recent work advancing the understanding of individual differences in public opinion.
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