Aiming for Excellence: Reflections on the Advanced Institute of Management Research and its Elite

In 2002, the UK government launched the Advanced Institute of Management Research, a major initiative intended to raise the quality of research in business schools. Rather than offering research grants in open competition, AIM deliberately funded a select few leading lights in management. Insufficient allowance was made for the Research Assessment Exercise, which measured research excellence in terms of papers published in top journals. The AIM's elite exploited its existing publishing advantage, and AIM provided further resources to aid their efforts. The AIM recruited willing acolytes to work with its elite in fashioning the sort of papers required by the top journals of management – positive papers, consensual and endlessly citable. Analysis of the publishing patterns of AIM senior fellows reveals research cliques and publication silos rather than a network organization. Much as the elite saw its AIM funding as recognition of its own excellence, so AIM itself came to be seen as acknowledgement of the excellence of management research as a whole. That AIM existed to raise management research from intellectual poverty was forgotten. The AIM was wound up in 2012, having spent £30 million, most of it on the subject's elite. The problems that beset management research remain.     

Modeling Correlated Discrete Uncertainties in Event Trees with Copulas

Modeling the dependence between uncertainties in decision and risk analyses is an important part of the problem structuring process. We focus on situations where correlated uncertainties are discrete, and extend the concept of the copula‐based approach for modeling correlated continuous uncertainties to the representation of correlated discrete uncertainties. This approach reduces the required number of probability assessments significantly compared to approaches requiring direct estimates of conditional probabilities. It also allows the use of multiple dependence measures, including product moment correlation, rank order correlation and tail dependence, and parametric families of copulas such as normal copulas, t‐copulas, and Archimedean copulas. This approach can be extended to model the dependence between discrete and continuous uncertainties in the same event tree.     

QMRA for Drinking Water: 2. The Effect of Pathogen Clustering in Single‐Hit Dose‐Response Models

Spatial and/or temporal clustering of pathogens will invalidate the commonly used assumption of Poisson‐distributed pathogen counts (doses) in quantitative microbial risk assessment. In this work, the theoretically predicted effect of spatial clustering in conventional “single‐hit” dose‐response models is investigated by employing the stuttering Poisson distribution, a very general family of count distributions that naturally models pathogen clustering and contains the Poisson and negative binomial distributions as special cases. The analysis is facilitated by formulating the dose‐response models in terms of probability generating functions. It is shown formally that the theoretical single‐hit risk obtained with a stuttering Poisson distribution is lower than that obtained with a Poisson distribution, assuming identical mean doses. A similar result holds for mixed Poisson distributions. Numerical examples indicate that the theoretical single‐hit risk is fairly insensitive to moderate clustering, though the effect tends to be more pronounced for low mean doses. Furthermore, using Jensen's inequality, an upper bound on risk is derived that tends to better approximate the exact theoretical single‐hit risk for highly overdispersed dose distributions. The bound holds with any dose distribution (characterized by its mean and zero inflation index) and any conditional dose‐response model that is concave in the dose variable. Its application is exemplified with published data from Norovirus feeding trials, for which some of the administered doses were prepared from an inoculum of aggregated viruses. The potential implications of clustering for dose‐response assessment as well as practical risk characterization are discussed.     

Bias and precision of measures of survival gain from right‐censored data

In cost‐effectiveness analyses of drugs or health technologies, estimates of life years saved or quality‐adjusted life years saved are required. Randomised controlled trials can provide an estimate of the average treatment effect; for survival data, the treatment effect is the difference in mean survival. However, typically not all patients will have reached the endpoint of interest at the close‐out of a trial, making it difficult to estimate the difference in mean survival. In this situation, it is common to report the more readily estimable difference in median survival. Alternative approaches to estimating the mean have also been proposed. We conducted a simulation study to investigate the bias and precision of the three most commonly used sample measures of absolute survival gain – difference in median, restricted mean and extended mean survival – when used as estimates of the true mean difference, under different censoring proportions, while assuming a range of survival patterns, represented by Weibull survival distributions with constant, increasing and decreasing hazards. Our study showed that the three commonly used methods tended to underestimate the true treatment effect; consequently, the incremental cost‐effectiveness ratio (ICER) would be overestimated. Of the three methods, the least biased is the extended mean survival, which perhaps should be used as the point estimate of the treatment effect to be inputted into the ICER, while the other two approaches could be used in sensitivity analyses. More work on the trade‐offs between simple extrapolation using the exponential distribution and more complicated extrapolation using other methods would be valuable. Copyright © 2015 John Wiley & Sons, Ltd.     

Prosecuting the prosecutor: The makings of the Michael Morton Act

Prosecutorial misconduct is not a rare event, but it often goes undetected, unreported, or no action is taken by the criminal justice system. However, when one Texas prosecutor, Ken Anderson, served jail time for wrongfully prosecuting an innocent man, Michael Morton, for murdering his wife, he made history. Anderson withheld exculpatory evidence leading to Morton wrongfully serving 25 years before being released with new DNA evidence. However, Anderson only served a five-day sentence and $500 fine. We discuss the case in the context of inequality and legal realism in the criminal justice system. Also, we look at the implications and new legal action taken by the state of Texas to try and combat this problem, along with looking at these secretive occupational subcultures.     

On Variational Bayes Estimation and Variational Information Criteria for Linear Regression Models

Variational Bayes (VB) estimation is a fast alternative to Markov Chain Monte Carlo for performing approximate Baesian inference. This procedure can be an efficient and effective means of analyzing large datasets. However, VB estimation is often criticised, typically on empirical grounds, for being unable to produce valid statistical inferences. In this article we refute this criticism for one of the simplest models where Bayesian inference is not analytically tractable, that is, the Bayesian linear model (for a particular choice of priors). We prove that under mild regularity conditions, VB based estimators enjoy some desirable frequentist properties such as consistency and can be used to obtain asymptotically valid standard errors. In addition to these results we introduce two VB information criteria: the variational Akaike information criterion and the variational Bayesian information criterion. We show that variational Akaike information criterion is asymptotically equivalent to the frequentist Akaike information criterion and that the variational Bayesian information criterion is first order equivalent to the Bayesian information criterion in linear regression. These results motivate the potential use of the variational information criteria for more complex models. We support our theoretical results with numerical examples.     

Covariate Decomposition Methods for Longitudinal Missing‐at‐Random Data and Predictors Associated with Subject‐Specific Effects

Investigators often gather longitudinal data to assess changes in responses over time within subjects and to relate these changes to within‐subject changes in predictors. Missing data are common in such studies and predictors can be correlated with subject‐specific effects. Maximum likelihood methods for generalized linear mixed models provide consistent estimates when the data are ‘missing at random’ (MAR) but can produce inconsistent estimates in settings where the random effects are correlated with one of the predictors. On the other hand, conditional maximum likelihood methods (and closely related maximum likelihood methods that partition covariates into between‐ and within‐cluster components) provide consistent estimation when random effects are correlated with predictors but can produce inconsistent covariate effect estimates when data are MAR. Using theory, simulation studies, and fits to example data this paper shows that decomposition methods using complete covariate information produce consistent estimates. In some practical cases these methods, that ostensibly require complete covariate information, actually only involve the observed covariates. These results offer an easy‐to‐use approach to simultaneously protect against bias from both cluster‐level confounding and MAR missingness in assessments of change.     

Sense of belonging in secondary schools: a survey of LGB and heterosexual students in Flanders

This study focuses on differences in sense of belonging between lesbian, gay, and bisexual (LGB) and heterosexual students. Data from 1,745 secondary school students were collected with an online survey. Step-wise multiple regression analyses was used to investigate the relationship between sexual orientation and sense of school belonging. The results show that sexual orientation has an impact on sense of belonging for girls, but not for boys. Perceived discrimination and LGB friendliness of the school appeared to be important indicators of sense of belonging for all the respondents, irrespective of their sexual orientation.     

Piece rates and workplace injury: Does survey evidence support Adam Smith?

While piece rates are routinely associated with higher productivity and wages, they can also generate unanticipated effects. Using cross-country European data, we provide among the first general survey evidence of a strong link between piece rates and workplace injury. Despite controls for workplace hazards, job characteristics and worker effort, piece rates workers suffer a 5 percentage point greater likelihood of injury. This remains despite attempts to control for endogeneity and heterogeneity. As piece rate wage premium estimates rarely control for injury likelihood, this raises the specter that part of that premium reflects a compensating wage differential for risk of injury.     

A location analysis approach for military maintenance scheduling with geographically dispersed service areas

This research uses a two-stage maximal covering location problem (MCLP) model to develop Inter continental ballistic missile (ICBM) maintenance schedules for the US Air Force. Solutions are compared to actual missile maintenance activities accomplished at F. E. Warren Air Force Base (AFB), Wyoming in May 2005. Sensitivity analysis is performed to determine the impact of altering security response times and the number of security patrol areas on the quality of daily maintenance schedules and personnel usage. Results indicate marked improvement over traditional Air Force scheduling methods. In addition sensitivity analysis identifies the levels at which the quality and quantity of maintenance performance is impacted.