Volume 3, Issue 1

Political science data often contain grouped observations, which produces unobserved "cluster effects" in statistical models. Typical solutions include (1) ignoring the impact on coefficients and only adjusting the standard errors of generalized linear models (GLM) or (2) addressing clustering in coefficient estimation while relying on a parametric assumption for the cluster effects and/or a large number of clusters for standard errors. I show that both approaches are problematic for inference. Through simulation I demonstrate that multilevel modeling (MLM) and generalized estimating equations (GEE) produce more efficient coefficients than does GLM. Next, I show that commonly-used MLM and GEE standard error methods can be biased downward, while bootstrapping by resampling clusters (BCSE) performs better, even with a misspecified error distribution and/or few clusters. I recommend the use of MLM or GEE to estimate coefficients and BCSE to estimate uncertainty, and show that this approach can produce divergent conclusions in applied research.

Quantitative economics has advanced dramatically in the past eighty years and much of this took place in the three decades following World War II. Many contributions were initiated and sponsored by the defense community. For the resolution of major military management and planning problems it was essential to create new quantitative economic models and methodologies. Accomplishments served the military while providing substantive and lasting new directions in economics. This paper is devoted to highlighting these results by describing the research on Input-Output economics, linear programming, game theory, decision theory, probabilistic models, and high-speed computing.

Policy analysts involved in quantitative research have many options for handling missing data. The method chosen will often greatly influence the substantive policy conclusions that will be drawn from the data. The most frequent methods for handling missing data assume that the data are missing at random (MAR). The current paper notes that an omnibus, nonparametric test of the MAR assumption is impossible using the observed data alone. Nonetheless various purported tests of the missingness mechanism (including tests of MAR) appear in the literature. The current paper clarifies that all of these tests rely on some assumption that cannot be tested from the data. The paper notes that tests of the missingness mechanism are frequently misinterpreted and it clarifies the appropriate interpretation of such tests. Policy analysts are encouraged not to develop the false impression that modern procedures for handling missing data in conjunction with tests of the missingness mechanism provide protection against the ill effects of missing data. Any justification for a particular approach to handling missing data must be come from substantive knowledge of the missingness process, not from the data.

The main lines of evidence for climate change are outlined. Alternative explanations to increased greenhouse gas concentrations are described. After discussing some statistical issues, attribution approaches are briefly described. Policy issues dealing with mitigation and adaptation are mentioned.

Making published, scientific research data publicly available can benefit scientists and policy makers only if there is sufficient information for these data to be intelligible. Thus the necessary meta-data go beyond the scientific, technological detail and extend to the statistical approach and methodologies applied to these data. The statistical principles that give integrity to researchers’ analyses and interpretations of their data require documentation. This is true when the intent is to verify or validate the published research findings; it is equally true when the intent is to utilize the scientific data in conjunction with other data or new experimental data to explore complex questions; and it is profoundly important when the scientific results and interpretations are taken outside the world of science to establish a basis for policy, for legal precedent or for decision-making. When research draws on already public data bases, e.g., a large federal statistical data base or a large scientific data base, selection of data for analysis, whether by selection (subsampling) or by aggregating, is specific to that research so that this (statistical) methodology is a crucial part of the meta-data. Examples illustrate the role of statistical meta-data in the use and reuse of these public datasets and the impact on public policy and precedent.

Lyons (2011) offered several critiques of the social network analyses of Christakis and Fowler, including issues of confounding, model inconsistency, and statistical dependence in networks. Here we show that in some settings, social network analyses of the type employed by Christakis and Fowler will still yield valid tests of the null of no social contagion, even though estimates and confidence intervals may not be valid. In particular, we show that if the alter's state is lagged by an additional period, then under the null of no contagion, the problems of model inconsistency and statistical dependence effectively disappear which allow for testing for contagion. Our results clarify the setting in which even "flawed" social network analyses are still useful for assessing social contagion and social influence.

VanderWeele et al.'s paper is a useful contribution to the on-going scientific conversation about the detection of contagion from purely observational data. It is especially helpful as a corrective to some of the more extreme statements of Lyons (2011). Unfortunately, this paper, too, goes too far in some places, and so needs some correction itself.