Zusammenfassungen

Ohne Zusammenfassung German translations by Monika Lütke-Daldrup.     

Partnering with Russia's new entrepreneurs: Software tsarina olga kirova

This article focuses on a Russian woman entrepreneur who established a very successful software firm under license from an American software company. The case illustrates a productive and successful partnership between a Western company and a new Russian entrepreneur. Recent developments in Russian entrepreneurship are reviewed, and the selection of Russian entrepreneurs as business partners is evaluated. The American company is described briefly, followed by an in-depth discussion of its Russian partner and founder, Olga Kirova. Conclusions and interpretations are offered on choosing the right Russian partners, assessing their personal values, objectives and leadership skills, being a good Western business partner, and considering Russian women as business associates.     

Ignorability conditions for frequentist non parametric analysis of conditional distributions with incomplete data

Rubin (1976 Rubin, D.B. (1976). Inference and missing data. Biometrika 63(3):581–592.[Crossref], [Web of Science ®] , [Google Scholar]) derived general conditions under which inferences that ignore missing data are valid. These conditions are sufficient but not generally necessary, and therefore may be relaxed in some special cases. We consider here the case of frequentist estimation of a conditional cdf subject to missing outcomes. We partition a set of data into outcome, conditioning, and latent variables, all of which potentially affect the probability of a missing response. We describe sufficient conditions under which a complete-case estimate of the conditional cdf of the outcome given the conditioning variable is unbiased. We use simulations on a renal transplant data set (Dienemann et al.) to illustrate the implications of these results.     

The big data divide and its consequences

Recent advances in technology and the increasing volumes of data that they enable have led to a wave of scholarly and popular attention to big data. While big data is often heralded for its ability to provide insight, the data, its analysis, and its outcomes are not evenly distributed. Currently, scholarship on big data is extending past work on the digital divide, theorizing a new big data divide. While this work most directly addresses the issues of ownership and access to big data, some work extends the divide to issues relating to skill and use. This extension opens up new complications relating to identity, social sorting, use, agency, and global development that are inextricably related to the issues above and to the study of big data. These issues go beyond the simple language of the digital divide extending inquiries into the realm of digital inequalities more generally. Any work on big data and the big data divide needs to engage with a more broad‐based notion of digital inequalities to be better equipped to handle the complex issues above, as well as the material, democratic, and identity problems that big data bring about.     

The service system challenges of work with juvenile justice involved young people in the Hunter Region,Australia

Current policies suggest that collaborative approaches are core to working effectively with juvenile justice involved young people. However, there is little research examining the workings of multi‐agency and collaborative endeavours in this field, or the experiences of the human service workers facilitating these connections. This paper reports on qualitative research that resulted from the Juvenile Justice and Education Equity in the Hunter Region project. Thirty‐eight human service workers were interviewed about their perceptions of the workings, strengths and challenges of the service system that supports young people who come into contact with the Children's Court in the Lower and Upper Hunter regions of New South Wales. Data analysis revealed three key themes related to (1) service gaps, cycles and maelstrom; (2) pursuing authentic service engagement; and (3) insider–outsider dynamics in service provision. Findings are discussed in relation to emerging practice and research agendas.     

Linear Increments with Non‐monotone Missing Data and Measurement Error

Linear increments (LI) are used to analyse repeated outcome data with missing values. Previously, two LI methods have been proposed, one allowing non‐monotone missingness but not independent measurement error and one allowing independent measurement error but only monotone missingness. In both, it was suggested that the expected increment could depend on current outcome. We show that LI can allow non‐monotone missingness and either independent measurement error of unknown variance or dependence of expected increment on current outcome but not both. A popular alternative to LI is a multivariate normal model ignoring the missingness pattern. This gives consistent estimation when data are normally distributed and missing at random (MAR). We clarify the relation between MAR and the assumptions of LI and show that for continuous outcomes multivariate normal estimators are also consistent under (non‐MAR and non‐normal) assumptions not much stronger than those of LI. Moreover, when missingness is non‐monotone, they are typically more efficient.     

Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence

Most existing reduced-form macroeconomic multivariate time series models employ elliptical disturbances, so that the forecast densities produced are symmetric. In this article, we use a copula model with asymmetric margins to produce forecast densities with the scope for severe departures from symmetry. Empirical and skew t distributions are employed for the margins, and a high-dimensional Gaussian copula is used to jointly capture cross-sectional and (multivariate) serial dependence. The copula parameter matrix is given by the correlation matrix of a latent stationary and Markov vector autoregression (VAR). We show that the likelihood can be evaluated efficiently using the unique partial correlations, and estimate the copula using Bayesian methods. We examine the forecasting performance of the model for four U.S. macroeconomic variables between 1975:Q1 and 2011:Q2 using quarterly real-time data. We find that the point and density forecasts from the copula model are competitive with those from a Bayesian VAR. During the recent recession the forecast densities exhibit substantial asymmetry, avoiding some of the pitfalls of the symmetric forecast densities from the Bayesian VAR. We show that the asymmetries in the predictive distributions of GDP growth and inflation are similar to those found in the probabilistic forecasts from the Survey of Professional Forecasters. Last, we find that unlike the linear VAR model, our fitted Gaussian copula models exhibit nonlinear dependencies between some macroeconomic variables. This article has online supplementary material.     

Developing and testing a satisfaction survey for nursing home residents: the Ohio Experience

Input from consumers has become an important part of quality improvement in long-term care and for consumer decision-making. This paper documents the development of the Ohio Nursing Home Resident Satisfaction Survey (ONHRSS) through a partnership of state government, research, and industry experts. The instrument was tested and refined through two waves of data--a pretest phase and later with statewide data. Exploratory and confirmatory analyses with statewide data identified eight primary factors along with an underlying, secondary Global Satisfaction factor. Reliability of the domains ranged from .69 to .95. Recommendations for further refinement and testing of the instrument are discussed along with policy and practice implications.