Handling Missing Values in Longitudinal Panel Data With Multiple Imputation

This article offers an applied review of key issues and methods for the analysis of longitudinal panel data in the presence of missing values. The authors consider the unique challenges associated with attrition (survey dropout), incomplete repeated measures, and unknown observations of time. Using simulated data based on 4 waves of the Marital Instability Over the Life Course Study (n = 2,034), they applied a fixed effect regression model and an event‐history analysis with time‐varying covariates. They then compared results for analyses with nonimputed missing data and with imputed data both in long and in wide structures. Imputation produced improved estimates in the event‐history analysis but only modest improvements in the estimates and standard errors of the fixed effects analysis. Factors responsible for differences in the value of imputation are examined, and recommendations for handling missing values in panel data are presented.     

The Challenges in Developing an Online Applied Statistics Program: Lessons Learned at Penn State University

Numerous professional fields have an increasing need for individuals trained in statistics and other quantitative analysis techniques. Today there exists great potential to fulfill this need by providing opportunities through online learning. However, to provide a high-quality education for returning adult professionals seeking advanced degrees in applied statistics online, many challenges need to be overcome. Based on our experience developing Penn State University’s online program in applied statistics, we discuss the evolution of the program’s curriculum, recruitment and development of online faculty, and meeting the requirements of students as important areas that require consideration in the development of an online program. We also highlight program evaluation strategies employed to ensure innovation and improvement in online education as cornerstones to a program’s success.     

Fragmented identities, frustrated politics: transsexuals, lesbians and 'queer'

This paper seeks to explore the complex position transsexualism has in relation to a range of personal identities and identity politics. In recent years there has been considerable debate over 'borderline identities' particularly between lesbians, transgenderists, and FTM and MTF transsexuals who transgress the identity category 'lesbian.'2 Stronger political affiliations between transsexuals, lesbians and feminists are argued for, and a greater recognition of the futility in attempting to demarcate the infinitely blurred ontological boundaries between 'sex,' gender and sexuality. Rather, we should focus on 'issue' based politics that challenge heterosexist and normative gender epistemologies and practices that restrict non-conformist subjectivities.     

Public Opinion and Dynamic Representation in the American States: The Case of Environmental Attitudes*

Objective. We investigate causal processes linking environmental conditions, attitudes, and policies in the American states: Is public opinion about ecology shaped by environmental conditions? Are state policymakers responsive to environmental opinions? Does public opinion respond to policy adoption? Methods. Using public opinion data from the DDB Worldwide Life Style Survey to measure aggregate state attitudes about the environment, as well as measures of water quality and policy intervention, we capture the dynamics of representation in the American states on the environment during the late 1980s and early 1990s. Results. Our findings support a thermostatic model of representation—state environmental opinions are influenced by environmental conditions and are responsive to policy outputs alongside improved environmental conditions. Conclusions. This model of the opinion‐policy linkage refines our understanding of representation and focuses us not just on the passage of public policy to address public desires, but the effectiveness of that policy as well.     

Electile Dysfunction

This essay combines Burke's burlesque frame and Jamieson's double binds to create a hybrid gendered theory for rhetorically analyzing the aggressive sexualization of 2008 United States vice presidential candidate Sarah Palin. The essay develops our theory titled the “burlesque binds” and explicates a specific form of a burlesque bind: the “MILF frame.” Palin's aggressively sexualized representation was united under the crass acronym MILF, which stands for “Mom I'd Like to Fuck.” The MILF frame emphasized Palin's feminine qualities, positioning her as a “trophy vice” with no political acumen, and highlighted her masculine behaviors, but deflated them through sexual objectification. The unification of the binds and burlesque theories reveals how Palin's role as a social actor was not simply circumscribed but shut down: she was deemed unfit by both masculine and feminine standards, by both public and private sphere conventions. Although Sarah Palin has arguably been the most prevalent subject of the political MILF frame, our essay catalogues other instances of female political leaders experiencing this type of symbolic degradation, suggesting the MILF frame's vast influence.     

Split Hamiltonian Monte Carlo

We show how the Hamiltonian Monte Carlo algorithm can sometimes be speeded up by “splitting” the Hamiltonian in a way that allows much of the movement around the state space to be done at low computational cost. One context where this is possible is when the log density of the distribution of interest (the potential energy function) can be written as the log of a Gaussian density, which is a quadratic function, plus a slowly-varying function. Hamiltonian dynamics for quadratic energy functions can be analytically solved. With the splitting technique, only the slowly-varying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics. Another context where splitting helps is when the most important terms of the potential energy function and its gradient can be evaluated quickly, with only a slowly-varying part requiring costly computations. With splitting, the quick portion can be handled with a small stepsize, while the costly portion uses a larger stepsize. We show that both of these splitting approaches can reduce the computational cost of sampling from the posterior distribution for a logistic regression model, using either a Gaussian approximation centered on the posterior mode, or a Hamiltonian split into a term that depends on only a small number of critical cases, and another term that involves the larger number of cases whose influence on the posterior distribution is small.     

D-efficient window experimental designs

In the paper we present a new method of calculating sampling intervals, so-called windows, allowing an experimenter some flexibility in timing the sample collection, while a minimum required design efficiency for parameter estimation is assured. The method is based on the Equivalence Theorem for D-optimality what makes the length of each window related to the parameter sensitivities. An example of calculating the windows in a pharmacokinetic study is presented. Some other methods of calculating efficient sampling windows are briefly discussed.