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Part 1: Document Description
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Citation |
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Title: |
Replication data for: Empirical Strategies for Various Manifestations of Multilevel Data |
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Identification Number: |
doi:10.7910/DVN/ASCAQJ |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2010-02-16 |
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Version: |
1 |
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Bibliographic Citation: |
Robert J. Franzese, Jr., 2010, "Replication data for: Empirical Strategies for Various Manifestations of Multilevel Data", https://doi.org/10.7910/DVN/ASCAQJ, Harvard Dataverse, V1 |
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Citation |
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Title: |
Replication data for: Empirical Strategies for Various Manifestations of Multilevel Data |
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Identification Number: |
doi:10.7910/DVN/ASCAQJ |
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Authoring Entity: |
Robert J. Franzese, Jr. (University of Michigan) |
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Producer: |
Political Analysis |
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Date of Production: |
2005 |
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Distributor: |
Harvard Dataverse |
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Distributor: |
Murray Research Archive |
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Date of Deposit: |
2010-02-16 |
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Holdings Information: |
https://doi.org/10.7910/DVN/ASCAQJ |
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Study Scope |
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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. <a href= "http://pan.oxfordjournals.org/cgi/content/full/mpi024/DC1" target= "_new">subscribe to Political Analysis to access the supplementary data</a> |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Robert J. Franzese, Jr. 2005. "Empirical Strategies for Various Manifestations of Multilevel Data." Political Analysis, 13(4), 430-446. <a href= "http://pan.oxfordjournals.org/cgi/content/full/13/4/430" target= "_new">full article freely available here</a> |
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Bibliographic Citation: |
Robert J. Franzese, Jr. 2005. "Empirical Strategies for Various Manifestations of Multilevel Data." Political Analysis, 13(4), 430-446. <a href= "http://pan.oxfordjournals.org/cgi/content/full/13/4/430" target= "_new">full article freely available here</a> |