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EDA for HLM: Visualization when Probabilistic Inference Fails

Published online by Cambridge University Press:  04 January 2017

Jake Bowers  and
Katherine W. Drake
Jake Bowers*
Affiliation:
Department of Political Science, Center for Political Studies, University of Michigan, Ann Arbor, MI 48109
Katherine W. Drake
Affiliation:
Department of Political Science, Center for Political Studies, University of Michigan, Ann Arbor, MI 48109. e-mail: kwdrake@umich.edu
*
e-mail: jwbowers@umich.edu (corresponding author)
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Abstract

Nearly all hierarchical linear models presented to political science audiences are estimated using maximum likelihood under a repeated sampling interpretation of the results of hypothesis tests. Maximum likelihood estimators have excellent asymptotic properties but less than ideal small sample properties. Multilevel models common in political science have relatively large samples of units like individuals nested within relatively small samples of units like countries. Often these level-2 samples will be so small as to make inference about level-2 effects uninterpretable in the likelihood framework from which they were estimated. When analysts do not have enough data to make a compelling argument for repeated sampling based probabilistic inference, we show how visualization can be a useful way of allowing scientific progress to continue despite lack of fit between research design and asymptotic properties of maximum likelihood estimators.

Somewhere along the line in the teaching of statistics in the social sciences, the importance of good judgment got lost amid the minutiae of null hypothesis testing. It is all right, indeed essential, to argue flexibly and in detail for a particular case when you use statistics. Data analysis should not be pointlessly formal. It should make an interesting claim; it should tell a story that an informed audience will care about, and it should do so by intelligent interpretation of appropriate evidence from empirical measurements or observations.

—Abelson, 1995, p. 2

With neither prior mathematical theory nor intensive prior investigation of the data, throwing half a dozen or more exogenous variables into a regression, probit, or novel maximum-likelihood estimator is pointless. No one knows how they are interrelated, and the high-dimensional parameter space will generate a shimmering pseudo-fit like a bright coat of paint on a boat's rotting hull.

—Achen, 1999, p. 26

Information

Type
Research Article
Information
Political Analysis , Volume 13 , Issue 4: Special Issue on Multilevel Modeling for Large Clusters , Autumn 2005 , pp. 301 - 326
Copyright
Copyright © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: We owe many thanks to James Bowers, Andrew Gelman, Orit Kedar, Burt Monroe, Kevin Quinn, Phil Shively, Laura Stoker, Cara Wong, and the anonymous reviewers of the first version of our manuscript for their many helpful comments.

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