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Empirical Strategies for Various Manifestations of Multilevel Data

  • Robert J. Franzese (a1)
Abstract

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.

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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

N. Beck , and J. Katz 1995. “What To Do (and Not to Do) with Time-Series-Cross-Section Data in Comparative Politics.” American Political Science Review 89(3): 634647.

J. Bowers , and K. Drake 2005. “EDA for HLM: Visualization When Probabilistic Inference Fails.” Political Analysis doi:10.1093/pan/mpi031.

K. L. Jusko , and P. Shively 2005. “A Two-Step Strategy for the Analysis of Cross-National Public Opinion Data.” Political Analysis doi:10.1093/pan/mpi030.

J. Lewis , and D. Linzer 2005. “Estimating Regression Models in which the Dependent Variable Is Based on Estimates.” Political Analysis doi:10.1093/pan/mpi026.

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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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