Advance Access publication July 20, 2005 Political Analysis (2005) 13:430–446 doi:10.1093/pan/mpi024 Empirical Strategies for Various Manifestations of Multilevel Data Robert J. Franzese, Jr. Department of Political Science, University of Michigan, Ann Arbor, MI e-mail: franzese@umich.edu Equivalent separate-subsample (two-step) and pooled-sample (one-step) strategies exist for any multilevel-modeling task, but their relative practicality and efficacy depend on dataset dimensions and properties and researchers’ goals. Separate-subsample strategies have difficulties incorporating cross-subsample information, often crucial in time-series cross- section or panel contexts (subsamples small and/or cross-subsample information great) but less relevant in pools of independently random surveys (subsamples large; cross-sample information small). Separate-subsample estimation also complicates retrieval of macro- level-effect estimates, although they remain obtainable and may not be substantively central. Pooled-sample estimation, conversely, struggles with stochastic specifications that differ across levels (e.g., stochastic linear interactions in binary dependent-variable models). Moreover, pooled-sample estimation that models coefficient variation in a theoretically reduced manner rather than allowing each subsample coefficient vector to differ arbitrarily can suffer misspecification ills insofar as this reduced specification is lacking. Often, though, these ills are limited to inefficiencies and standard-error inaccuracies that familiar efficient (e.g., feasible generalized least squares) or consistent-standard-error estimation strategies can satisfactorily redress. 1 Introduction Multilevel data are lower, micro-level data nested within higher, macro-level units. Political science examples include survey respondents nested within countries or states, elections nested within countries, time periods nested with nations or nation dyads, and many more. The levels may exceed two, such as survey respondents within elections within countries, voters within districts within countries, time periods within directed- dyads within dyads, etc. Level 1 or the micro-level is the lowest level or smallest unit of analysis; higher levels are Level 2, Level 3, etc., or macro-level(s). Common multilevel datasets in political science include cross-context surveys, which contributions to this volume analyze; panel (survey) data, containing repeated surveys of the same individuals; time-series cross-section (TSCS) datasets common in comparative/international politics and political economy, which typically nest time periods within countries; and datasets Author’s note: Gratitude to the contributors to this issue for helpful discussion of some of the issues addressed here and, especially, to the editors of this issue for extremely kind and constructive comments on this manuscript. Ó The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org 430 by guest on June 26, 2015 http://pan.oxfordjournals.org/ Downloaded from