Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50-100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Delegation of pricing authority to the sales force:an agency-theoretic perspective of its determinants and impact on performance

In industrial selling situations, the questions of what factors drive pricing authority delegation to salespeople and under what conditions price delegation is beneficial for the firm are often asked. To advance knowledge in this area, we (1) develop and empirically test a framework of important drivers of price delegation based on agency-theoretic research and (2) investigate the impact of price delegation on firm performance, taking into account agency theory variables as potential moderators. The study is based on data from a sample of 181 companies from the industrial machinery and electrical engineering industry in Germany. The results indicate that the degree of pricing delegation increases as information asymmetry between the salesperson and sales manager increases and as it becomes more difficult to monitor salespeople's efforts. Conversely, risk-aversion of salespeople is negatively related to the degree of price delegation. Furthermore, we find a positive effect of price delegation on firm performance, which is amplified when market-related uncertainty is high and when salespeople possess better customer-related information than their managers. Hence, our results clearly show that rigid, “one price fits all” policies are inappropriate in many B2B market situations. Instead, sales managers should grant their salespeople sufficient leeway to adapt prices to changing customer requirements and market conditions, especially in firms that operate in highly uncertain selling environments.

Guiding schools to improved performance using data envelopment analysis:an illustration with data from a local education authority

The educational process is characterised by multiple outcomes such as the achievement of academic results of various standards and non-academic achievements. This paper shows how data envelopment analysis (DEA) can be used to guide secondary schools to improved performance through role-model identification and target setting in a way which recognises the multi-outcome nature of the education process and reflects the relative desirability of improving individual outcomes. The approach presented in the paper draws from a DEA-based assessment of the schools of a local education authority carried out by the authors. Data from that assessment are used to illustrate the approach presented in the paper. (Key words: Data envelopment analysis, education, target setting.)