Mastery, Frequency, and Interaction of Managerial Behaviors Relative to Subunit Effectiveness

The relationships among mastery and frequency ofmanagerial behaviors, and subunit effectiveness havefrequently been confused. This study investigates bothmain and interactive effects. The results show that the interaction effect can often be thestrongest effect and that increasing frequency withoutimproving mastery can sometimes be detrimental.Furthermore, these results offer a possible explanation as to why research of managerial behavior hasbeen so inconclusive. In addition, the results questionpopular advice given often to managers. Finally, somerecommendations are made as to how management development could be made more effective basedon the results of this study.     

Funding large-scale qualitative sociology

Myrna Silverman, Edward Mulvey, and Barry Glassner helped us with thoughtful critiques of earlier drafts.     

Fertility and Culture in Eastern Europe: A Case Study of Riga, Latvia, 1867–1881

Recent research on the secular decline of fertility in historical Europe has focused on cultural explanations in the wake of the European Fertility Project's failure to confirm demographic transition theory. Using the city of Riga in present-day Latvia as a case study, the essay provides initial estimates of nuptiality and fertility for resident language and religious groups in 1867 and 1881, and reviews the prospects of future work. Despite obstacles, Eastern Europe offers researchers an exceptional opportunity to test major cultural and economic hypotheses about the fertility decline because sustained ethnic diversity coexisted with economic development.     

Why we need a revolution in strategic planning

The author uses a metaphorical concept of Newtonian planning in an Einsteinian environment to express the predicament facing planners in an environment subject to sudden and unpredictable change. The conventional ‘Newtonian’ approach assumes a sense of order and control whereas the current environment is turbulent, disordered and potentially out of control. He makes recomendations for strategic planners that accept the limitations of existing and foreseeable analytical techniques.     

Changes in Income Concentration: Taxes or Macroeconomic Conditions?

We examine the relative importance of tax rates and macroeconomic fluctuations in explaining the share of national adjusted gross income (AGI) reported by the top 0.5% of all taxpayers. Results indicate that cutting the top income or capital gains tax rate would increase the top AGI share but not by enough to increase revenues. The preponderance of evidence suggests that the top AGI share is affected more by the capital gains tax rate than by the income tax rate but that real gross domestic product fluctuations have even larger effects.     

Using a mixture model for multiple imputation in the presence of outliers: the 'Healthy for life' project

Summary.  We consider the problem of obtaining population-based inference in the presence of missing data and outliers in the context of estimating the prevalence of obesity and body mass index measures from the 'Healthy for life' study. Identifying multiple outliers in a multivariate setting is problematic because of problems such as masking, in which groups of outliers inflate the covariance matrix in a fashion that prevents their identification when included, and swamping, in which outliers skew covariances in a fashion that makes non-outlying observations appear to be outliers. We develop a latent class model that assumes that each observation belongs to one of K unobserved latent classes, with each latent class having a distinct covariance matrix. We consider the latent class covariance matrix with the largest determinant to form an 'outlier class'. By separating the covariance matrix for the outliers from the covariance matrices for the remainder of the data, we avoid the problems of masking and swamping. As did Ghosh-Dastidar and Schafer, we use a multiple-imputation approach, which allows us simultaneously to conduct inference after removing cases that appear to be outliers and to promulgate uncertainty in the outlier status through the model inference. We extend the work of Ghosh-Dastidar and Schafer by embedding the outlier class in a larger mixture model, consider penalized likelihood and posterior predictive distributions to assess model choice and model fit, and develop the model in a fashion to account for the complex sample design. We also consider the repeated sampling properties of the multiple imputation removal of outliers.