Measuring Social “Informatization”: A Factor Analytic Approach*

Some scholars have argued that we are witnessing a new social revolution—social “informatization”—that is comparable in scope and impact to that of the Industrial Revolu‐tion of the eighteenth century. Others have argued that it is a much more modest phase in the ongoing development of communication and information‐processing technology. While there are a number of reasons for disagreement about what exactly “informatization” is, and what its impact will be, two are paramount: (1) conceptual imprecision, and (2) issues of measurement. Using factor analysis, this study aims to clarify its conceptualization, and, then, rather than focusing on a single dimension (e.g., technological or economic), it will develop a comprehensive multiple‐indicator measure that captures the economic, technological, and size (stock) dimensions of social informatization. We find that this measure of social informatization strongly correlates with the general level of socioeconomic development. This result implies that social informatization may be a more continuous and cumulative process than a disjunctive or discontinuous “revolution.”     

A note on sample size savings with the use of a single well‐controlled clinical trial to support the efficacy of a new drug

In terms of the risk of making a Type I error in evaluating a null hypothesis of equality, requiring two independent confirmatory trials with two‐sided p‐values less than 0.05 is equivalent to requiring one confirmatory trial with two‐sided p‐value less than 0.001 25. Furthermore, the use of a single confirmatory trial is gaining acceptability, with discussion in both ICH E9 and a CPMP Points to Consider document. Given the growing acceptance of this approach, this note provides a formula for the sample size savings that are obtained with the single clinical trial approach depending on the levels of Type I and Type II errors chosen. For two replicate trials each powered at 90%, which corresponds to a single larger trial powered at 81%, an approximate 19% reduction in total sample size is achieved with the single trial approach. Alternatively, a single trial with the same sample size as the total sample size from two smaller trials will have much greater power. For example, in the case where two trials are each powered at 90% for two‐sided α=0.05 yielding an overall power of 81%, a single trial using two‐sided α=0.001 25 would have 91% power. Copyright © 2004 John Wiley & Sons, Ltd.     

An Approach to Assessing the Impacts of Incident-Free Transportation of Radioactive Materials: II. Highway Transportation

Annual radiation doses and risks to the public living near, and traveling on, public highways, and occupational doses are calculated for highway shipments of radioactive materials under accident-free and incident-free conditions (i.e., under normal transportation conditions). The database developed by Sandia National Laboratories is used, and calculations are made using RADTRAN 4.0. This report is one of two reports estimating radiological risk associated with transportation of radioactive materials.     

Bayesian variable selection and regularization for time–frequency surface estimation

Summary.  We describe novel Bayesian models for time–frequency inverse modelling of non-stationary signals. These models are based on the idea of a Gabor regression , in which a time series is represented as a superposition of translated, modulated versions of a window function exhibiting good time–frequency concentration. As a necessary consequence, the resultant set of potential predictors is in general overcomplete—constituting a frame rather than a basis—and hence the resultant models require careful regularization through appropriate choices of variable selection schemes and prior distributions. We introduce prior specifications that are tailored to representative time series, and we develop effective Markov chain Monte Carlo methods for inference. To highlight the potential applications of such methods, we provide examples using two of the most distinctive time–frequency surfaces—speech and music signals—as well as standard test functions from the wavelet regression literature.     

Evaluating the Efficacy of a Mental Models Approach for Improving Occupational Chemical Risk Protection

Chemical risk protection in the workplace relies partly on informing workers about possible risks using material safety data sheets (MSDS). This article reports on phase 2 of a project (phase 1 reported in Cox et al.), which employed a mental models approach to improve on data sheets as communicative interventions for perchloroethylene in dry cleaning and rosin-based solder flux in the electronics industry within small businesses in the United Kingdom (small enterprises (SEs) < 25 employees in the workplace). It focuses on the efficacy of a multimethod evaluation strategy to assess (1) the capacity of a mental models approach to yield contextually relevant data for intervention design and (2) the effectiveness of the strategy itself in validating the mental models data. The evaluation was conducted using postal questionnaires and semi-structured verbal protocols to provide responses to the alternative intervention content and to prioritize risk messages. User discussion groups were then employed, particularly as a means of establishing whether contextual information could be obtained that would differ qualitatively from the kind elicited through individual (semi) structured methods. We conclude that the mental models approach as part of an iterative process including systematic multimethod evaluation is successful in supporting the design of relevant communications to the users of chemicals. The overall viability of communicative interventions in the context of health and safety in small businesses remains in question. Future research might aim to develop a more holistic approach to interventions in complex occupational contexts.     

Should I Stay or Should I Go? Explaining Turnover Intentions with Organizational Identification and Job Satisfaction*

The social identity approach is a powerful theoretical framework for the understanding of individuals' behaviour. The main argument is that individuals think and act on behalf of the group they belong to because this group membership adds to their social identity, which partly determines one's self‐esteem. In the organizational world, social identity and self‐categorization theories state that a strong organizational identification is associated with low turnover intentions. Because identification is the more general perception of shared fate between employee and organization, we propose that the relationship between identification and turnover will be mediated by job satisfaction as the more specific evaluation of one's task and working conditions. In four samples we found organizational identification feeding into job satisfaction, which in turn predicts turnover intentions.