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Empirical Strategies for Various Manifestations of Multilevel Data

  • Robert J. Franzese (a1)

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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Beck, N., and Katz, J. 1995. “What To Do (and Not to Do) with Time-Series-Cross-Section Data in Comparative Politics.” American Political Science Review 89(3): 634647.
Beck, N., and Katz, J. 1996. “Nuisance or Substance: Specifying and Estimating Time-Series-Cross-Section Models.” Political Analysis 6: 136.
Beck, N., and Katz, J. 2005. “Random Coefficient Models for Time-Series-Cross-Section Data.” Presented at the 2001 meetings of the Political Methodology Organization Section of the American Political Science Association.
Bowers, J., and Drake, K. 2005. “EDA for HLM: Visualization When Probabilistic Inference Fails.” Political Analysis doi:10.1093/pan/mpi031.
Brambor, T., Clark, W. R., and Golder, M. 2005. “Understanding Interaction Models: Improving Empirical Analyses.” Political Analysis doi:10.1093/pan/mpi014.
Davidson, R., and MacKinnon, J. 1993. Estimation and Inference in Econometrics. New York: Oxford University Press.
Franzese, R., and Kam, C. 2005. Modeling and Interpreting Interactive Hypotheses in Regression Analysis: A Refresher and Some Practical Advice. Unpublished manuscript. (Available at∼franzese/Interactions_Michigan.030305.pdf.)
Franzese, R., and Hays, J. 2005. Spatial Econometric Models for Political Science. (Available at∼franzese/FranzeseHays.SpatialEcon.Book.pdf.)
Greene, W. H. 2003. Econometric Analysis. Upper Saddle River, NJ: Pearson Education, Inc.
Jusko, K. L., and Shively, P. 2005. “A Two-Step Strategy for the Analysis of Cross-National Public Opinion Data.” Political Analysis doi:10.1093/pan/mpi030.
Leoni, E. 2005. “How to Analyze Multi-Country Survey Data: Results from Monte Carlo Experiments.” Paper presented at the 2005 Midwest Political Science Association Conference.
Lewis, J., and Linzer, D. 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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